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The view of embodied emotion believes that emotions are the emotions of the body. While emotion-specific patterns of self-reported bodily sensation have been previously reported, the physiological bodily representation across emotions remains to be addressed. The present study aimed to investigate the effectiveness of multi-site bodily electrodermal representations of emotions. A multi-channel electrodermal measurement device was designed to record electrodermal activities from nine body sites (neck, back, chest, bilateral abdomen, bilateral wrist, and bilateral ankle) from thirty-six college students (all male), while they were presented with a series of emotional pictures. Using the integral skin conductance response feature and a random forest classification method, the classification of high and low arousal levels achieved an average classification accuracy of 80.4 ± 8.1%, and the classification of positive, neutral, and negative states reached an average classification accuracy of 76.4 ± 10.2%. The classification models for arousal and valence were found to rely on distinct bodily representations. Meanwhile, the classification results of multi-site measurement were significantly better than single-site results. Our findings for the first time illustrate the bodily electrodermal representations of emotion and suggest the feasibility of affective computing using bodily electrodermal signals. [ABSTRACT FROM AUTHOR]

Human man Activity Recognition (HAR) using wearable sensors, particularly wrist-worn devices, has garnered significant research interest. However, challenges such as sensor displacement and variations in wearing habits can affect the accuracy of HAR systems. Two compensation strategies for sensor displacemnt are proposed to address these issues. The first strategy is hybrid data fusion, which involves merging sensor data collected from different displacement locations on the wrist. This technique aims to mitigate the discrepancies in data distribution that result from the multiple wearing positions along the wrist, thereby enhancing the overall accuracy of HAR models. The second strategy is cross-location transfer fine-tuning, which involves pretraining a model with data from typical wrist locations and then fine-tuning it with data from a new sensor location. This approach improves the model’s ability to adapt and perform accurately when the sensor is placed in a different position, significantly enhancing its performance and generalization capabilities. To verify the effectiveness of these proposed compensation strategies, we built an LSTM baseline model and introduce a new Multi-stage Feature Extraction (MSFE) model that integrates 1D CNN and attention. Experiments on common activities such as walking, standing, using stairs, and lying down, with data recorded at multiple locations along the wrist, have shown that both hybrid data fusion and cross-location transfer fine-tuning strategies notably improve the recognition accuracy of HAR models. The proposed MSFE model achieves higher recognition accuracies than the LSTM model in all six experimental scenarios, particularly in Scenario 5 involving sensor displacement, with an improvement of up to 31.65%. Additionally, thecross-location transfer fine-tuning strategy enhances the recognition accuracy by 9.19% for Subject 3 with sensor displacement at the right wrist location. These advancements in handling sensor displacement and wearing variations are crucial for developing more reliable and versatile wearable technologies.INDEX TERMS Sensor displacement compensation, human activity recognition, deep learning, wrist.

This study aimed to develop a novel framework to quickly personalize electromyography (EMG)-driven musculoskeletal models (MMs) as efferent neural interfaces for upper limb prostheses. Our framework adopts a generic upper-limb MM as a baseline and uses an artificial neural network-based policy to fine-tune the model parameters for MM personalization. The policy was trained by reinforcement learning (RL) to heuristically adjust the MM parameters to maximize the accuracy of estimated hand and wrist motions from EMG inputs. Our present framework was compared to the baseline MM and a widely used MM parameter optimization method: simulated annealing (SA). An offline evaluation was performed to first quantify the time required for MM personalization and the kinematics estimation accuracy of personalized MMs based on data collected from non-disabled subjects. Then, in an online evaluation, additional human subjects, including an individual with a transradial amputation, performed a virtual hand posture matching task using generic and personalized MMs. Results showed that compared to the baseline generic MM, personalized MMs estimated joint motion with lower error in both offline (${p} \lt 0.05$) and online tests (${p} =0.014$), demonstrating the benefit of MM personalization. The RL-based framework performed model optimization in under one second on average in cases that took SA over 13 minutes and yielded comparable kinematics estimations both offline and online. Hence, our present personalization framework can be a practical solution for the daily use of EMG-driven MMs in prostheses or other assistive devices. [ABSTRACT FROM AUTHOR]

The application of electromyography (EMG) decomposition techniques in myoelectric control has gradually increased. However, most decomposition-based control schemes rely on machine learning, lacking interpretation of the biological mechanisms underlying movement generation and requiring large datasets for training. As neuromusculoskeletal modeling provides a promising alternative, this study proposes a decomposition-based musculoskeletal model for simultaneous and proportional myoelectric control. Sixteen non-disabled subjects participated in two experiments involving isometric wrist contractions in two degrees of freedom (DoF). High-density surface EMG signals and torques were recorded simultaneously. The EMG signals were decomposed into motor unit action potential trains (MUAPts). We proposed four clustering methods (two activation-based and two action potential-based) to group MUAPts, from which three neural features were extracted as neural excitations and input to the musculoskeletal model. An activation-based clustering method with the twitch feature achieved a relatively high accuracy (${R}^{{2}}={0}.{791}\pm {0}.{101}$ and ${0}.{622}\pm {0}.{148}$ in the two experiments) with the highest smoothness ($\textit {Roug}\textit {hnes}{s}={1}.{389}\pm {0}.{211}$ and ${1}.{140}\pm {0}.{159}$). The proposed MUAPt-based musculoskeletal model achieved promising accuracy in estimating continuous 2-DoF wrist torques, providing a novel approach for understanding the neuromechanical properties of multi-DoF movements and advancing the development of dexterous rehabilitation and robotic control. [ABSTRACT FROM AUTHOR]

After a neurological injury, neurorehabilitation aims to restore sensorimotor function of patients. Technological assessments can provide high-quality data on a patient’s performance and support clinical decision making towards the most appropriate therapy. In this study, the ArmeoPower, a robotic exoskeleton for the upper extremities, was used to assess 12 neurological patients and 31 non-disabled participants performing various standardized single joint and frontal plane game-like exercises. From the collected data, kinematic metrics (End-Point Error, Hand-Path Ratio, reaction time, stability, Number of Velocity Peaks, peak, and mean Velocity) and the game score, were calculated and analyzed according to three criteria: the reliability (a), the difference between patients and non-disabled participants (b), as well as the influence of robotic movement assistance (c). In total, 39 metrics were analyzed and the following five most promising assessment variables for different exercises could be identified based on the three above-mentioned criteria: smoothness (RainMug (wrist)), mean speed (RainMug (wrist)), reaction time (Goalkeeper), maximum speed (HighFlyer (elbow)) and accuracy (Connect the dots), with the former showing good validity (rho=0.82, p=0.02) when comparing to the patient’s severity level. The results demonstrate feasibility to extract and analyze various kinematic metrics from the ArmeoPower, which can provide quantitative information about human performance during training and therapy. The generated data increases the understanding of the patient’s movement and can be used in the future in clinical research for better performance evaluation and providing more feedback options, leading towards a more personalized and patient-centric therapy. [ABSTRACT FROM AUTHOR]

Decoding movement intentions from motor unit (MU) activities remains an ongoing challenge, which restricts our comprehension of the intricate transition mechanism from microscopic neural drive to macroscopic movements. This study presents an innovative neuro-musculoskeletal (NMS) model driven by MU activities for online estimation of continuous wrist movements. The proposed model employs a physiological and comprehensive utilization of MU firings and waveforms, thus facilitating the localization of MUs to muscle-tendon units (MTU) as well as the computation of MU-specific neural excitation. Subsequently, the MU-specific neural excitation was integrated to form the MTU-specific neural excitation, which were then inputted into a musculoskeletal model to accomplish the joint angle estimation. To assess the effectiveness of this model, high-density surface electromyography and angular data were collected from the forearms of eight subjects during their performance of wrist flexion-extension task. Two pieces of $8\times 8$ electrode arrays and a motion capture system were employed for data acquisition. Following offline model calibration with a global optimization algorithm, online angle estimation results demonstrated a significant superiority of the proposed model over the state-of-the-art NMS models (p < 0.05), yielding the lowest normalized root mean square error ($0.10~\pm ~0.02$) and the highest determination coefficient ($0.87~\pm ~0.06$). This study provides a novel idea for the decoding of joint movements from MU activities. The research findings hold the potential to advance the development of NMS models towards the control of multiple degrees of freedom, with promising applications in the fields of motor control, biomechanics, and neuro-rehabilitation engineering. [ABSTRACT FROM AUTHOR]

Motor symptoms such as tremor and bradykinesia can develop concurrently in Parkinson’s disease; thus, the ideal home monitoring system should be capable of tracking symptoms continuously despite background noise from daily activities. The goal of this study is to demonstrate the feasibility of detecting symptom episodes in a free-living scenario, providing a higher level of interpretability to aid AI-powered decision-making. Machine learning models trained on wearable sensor data from scripted activities performed by participants in the lab and clinician ratings of the video recordings of these tasks identified tremor, bradykinesia, and dyskinesia in the supervised lab environment with a balanced accuracy of 83%, 75%, and 81%, respectively, when compared to the clinician ratings. The performance of the same models when evaluated on data from subjects performing unscripted activities unsupervised in their own homes achieved a balanced accuracy of 63%, 63%, and 67%, respectively, in comparison to self-assessment patient diaries, further highlighting their limitations. The ankle-worn sensor was found to be advantageous for the detection of dyskinesias but did not show an added benefit for tremor and bradykinesia detection here. [ABSTRACT FROM AUTHOR]

Upper extremity (UE) impairment is common after stroke resulting in reduced arm use in daily life. A few studies have examined the use of wearable feedback of the quantity of arm movement to promote recovery, but with limited success. We posit that it may be more effective to encourage an increase in beneficial patterns of movement practice – i.e. the overall quality of the movement experience – rather than simply the overall amount of movement. As a first step toward testing this idea, here we sought to identify statistical features of the distributions of daily arm movements that become more prominent as arm impairment decreases, based on data obtained from a wrist IMU worn by 22 chronic stroke participants during their day. We identified several measures that increased as UE Fugl-Meyer (UEFM) score increased: the fraction of movements achieved at a higher speed, forearm postural diversity (quantified by kurtosis of the tilt-angle), and forearm postural complexity (quantified by sample entropy of tilt angle). Dividing participants into severe, moderate, and mild impairment groups, we found that forearm postural diversity and complexity were best able to distinguish the groups (Cohen’s D =1.1, and 0.99, respectively) and were also the best subset of predictors for UEFM score. Based on these findings coupled with theories of motor learning that emphasize the importance of variety and challenge in practice, we suggest that using these measures of diversity and complexity in wearable rehabilitation could provide a basis to test whether the quality of the daily movement experience is therapeutic. [ABSTRACT FROM AUTHOR]

In Huntington’s disease (HD), wearable inertial sensors could capture subtle changes in motor function. However, disease-specific validation of methods is necessary. This study presents an algorithm for walking bout and gait event detection in HD using a leg-worn accelerometer, validated only in the clinic and deployed in free-living conditions. Seventeen HD participants wore shank- and thigh-worn tri-axial accelerometers, and a wrist-worn device during two-minute walk tests in the clinic, with video reference data for validation. Thirteen participants wore one of the thigh-worn tri-axial accelerometers (AP: ActivPAL4) and the wrist-worn device for 7 days under free-living conditions, with proprietary AP data used as reference. Gait events were detected from shank and thigh acceleration using the Teager-Kaiser energy operator combined with unsupervised clustering. Estimated step count (SC) and temporal gait parameters were compared with reference data. In the clinic, low mean absolute percentage errors were observed for stride (shank/thigh: 0.6/0.9%) and stance (shank/thigh: 3.3/7.1%) times, and SC (shank/thigh: 3.1%). Similar errors were observed for proprietary AP SC (3.2%), with higher errors observed for the wrist-worn device (10.9%). At home, excellent agreement was observed between the proposed algorithm and AP software for SC and time spent walking (ICC $_{{2},{1}}\gt 0.975$). The wrist-worn device overestimated SC by 34.2%. The presented algorithm additionally allowed stride and stance time estimation, whose variability correlated significantly with clinical motor scores. The results demonstrate a new method for accurate estimation of HD gait parameters in the clinic and free-living conditions, using a single accelerometer worn on either the thigh or shank. [ABSTRACT FROM AUTHOR]

This paper introduces a lightweight bilateral underactuated upper limb exoskeleton (UULE) designed to assist chronic stroke patients with distal joint (Elbow-Wrist) impairments during bimanual activities of daily living (ADL). The UULE aims to assist patients in shoulder flexion/extension, elbow flexion/extension, forearm pronation/supination, and wrist flexion/extension. Notable features include (i) a cable-driven mechanism maintaining a lightweight structure (1.783 kg); (ii) passive joints conforming to less-impaired proximal joints, reducing restrictions on their movements; (iii) a compact design with passive ball joints enabling bilateral configuration for scapula protraction/retraction; and (iv) implementation of the master-slave joint assistance training strategy in an underactuated exoskeleton, achieving symmetric robot joint motion in bimanual ADL. Experiments with ten healthy subjects demonstrated the UULE’s effectiveness by revealing significant reductions in muscle activity in a symmetric bimanual ADL task. These advancements address critical limitations of current exoskeletons, showcasing the UULE as a promising contribution to lightweight and effective robotic rehabilitation strategies for chronic stroke patients. [ABSTRACT FROM AUTHOR]

Utilization of hand-tracking cameras, such as Leap, for hand rehabilitation and functional assessments is an innovative approach to providing affordable alternatives for people with disabilities. However, prior to deploying these commercially-available tools, a thorough evaluation of their performance for disabled populations is necessary. In this study, we provide an in-depth analysis of the accuracy of Leap’s hand-tracking feature for both individuals with and without upper-body disabilities for common dynamic tasks used in rehabilitation. Leap is compared against motion capture with conventional techniques such as signal correlations, mean absolute errors, and digit segment length estimation. We also propose the use of dimensionality reduction techniques, such as Principal Component Analysis (PCA), to capture the complex, high-dimensional signal spaces of the hand. We found that Leap’s hand-tracking performance did not differ between individuals with and without disabilities, yielding average signal correlations between 0.7-0.9. Both low and high mean absolute errors (between 10-80mm) were observed across participants. Overall, Leap did well with general hand posture tracking, with the largest errors associated with the tracking of the index finger. Leap’s hand model was found to be most inaccurate in the proximal digit segment, underestimating digit lengths with errors as high as 18mm. Using PCA to quantify differences between the high-dimensional spaces of Leap and motion capture showed that high correlations between latent space projections were associated with high accuracy in the original signal space. These results point to the potential of low-dimensional representations of complex hand movements to support hand rehabilitation and assessment. [ABSTRACT FROM AUTHOR]

Illusory directional sensations are generated through asymmetric vibrations applied to the fingertips and have been utilized to induce upper-limb motions in the rehabilitation and training of patients with visual impairment. However, its effects on motor control remain unclear. This study aimed to verify the effects of illusory directional sensations on wrist motion. We conducted objective and subjective evaluations of wrist motion during a motor task, while inducing an illusory directional sensation that was congruent or incongruent with wrist motion. We found that, when motion and illusory directional sensations were congruent, the sense of agency for motion decreased. This indicates an induction sensation of the hand being moved by the illusion. Interestingly, although no physical force was applied to the hand, the angular velocity of the wrist was higher in the congruent condition than that in the no-stimulation condition. The angular velocity of the wrist and electromyography signals of the agonist muscles were weakly positively correlated, suggesting that the participants may have increased their wrist velocity. In other words, the congruence between the direction of motion and illusory directional sensation induced the sensation of the hand being moved, even though the participants’ wrist-motion velocity increased. This phenomenon can be explained by the discrepancy between the sensation of active motion predicted by the efferent copy, and that of actual motion caused by the addition of the illusion. The findings of this study can guide the design of novel rehabilitation methods. [ABSTRACT FROM AUTHOR]

Surface Electromyography (sEMG) signals are widely used as input to control robotic devices, prosthetic limbs, exoskeletons, among other devices, and provide information about someone’s intention to perform a particular movement. However, the redundant action of 32 muscles in the forearm and hand means that the neuromotor system can select different combinations of muscular activities to perform the same grasp, and these combinations could differ among subjects, and even among the trials done by the same subject. In this work, 22 healthy subjects performed seven representative grasp types (the most commonly used). sEMG signals were recorded from seven representative forearm spots identified in a previous work. Intra- and intersubject variability are presented by using four sEMG characteristics: muscle activity, zero crossing, enhanced wavelength and enhanced mean absolute value. The results confirmed the presence of both intra- and intersubject variability, which evidences the existence of distinct, yet limited, muscle patterns while executing the same grasp. This work underscores the importance of utilizing diverse combinations of sEMG features or characteristics of various natures, such as time-domain or frequency-domain, and it is the first work to observe the effect of considering different muscular patterns during grasps execution. This approach is applicable for fine-tuning the control settings of current sEMG devices. [ABSTRACT FROM AUTHOR]

Objective: A pathological tremor (PT) is an involuntary rhythmic movement of varying frequency and amplitude that affects voluntary motion, thus compromising individuals’ independence. A comprehensive model incorporating PT’s physiological and biomechanical aspects can enhance our understanding of the disorder and provide valuable insights for therapeutic approaches. This study aims to build a biomechanical model of pathological tremors using OpenSim’s realistic musculoskeletal representation of the human wrist with two degrees of freedom. Methods: We implemented a Matlab/OpenSim interface for a forward dynamics simulation, which allows for the modeling, simulation, and design of a physiological $\mathcal {H}_{\infty} $ closed-loop control. This system replicates pathological tremors similar to those observed in patients when their arm is extended forward, the wrist is pronated, and the hand is subject to gravity forces. The model was individually tuned to five subjects (four Parkinson’s disease patients and one diagnosed with essential tremor), each exhibiting distinct tremor characteristics measured by an inertial sensor and surface EMG electrodes. Simulation agreement with the experiments for EMGs, central frequency, joint angles, and angular velocities were evaluated by Jensen-Shannon divergence, histogram centroid error, and histogram intersection. Results: The model emulated individual tremor statistical characteristics, including muscle activations, frequency, variability, and wrist kinematics, with greater accuracy for the four Parkinson’s patients than the essential tremor. Conclusion: The proposed model replicated the main statistical features of subject-specific wrist tremor kinematics. Significance: Our methodology may facilitate the design of patient-specific rehabilitation devices for tremor suppression, such as neural prostheses and electromechanical orthoses. [ABSTRACT FROM AUTHOR]

We designed and tested a system for real-time control of a user interface by extracting surface electromyographic (sEMG) activity from eight electrodes in a wristband configuration. sEMG data were streamed into a machine-learning algorithm that classified hand gestures in real-time. After an initial model calibration, participants were presented with one of three types of feedback during a human-learning stage: veridical feedback, in which predicted probabilities from the gesture classification algorithm were displayed without alteration; modified feedback, in which we applied a hidden augmentation of error to these probabilities; and no feedback. User performance was then evaluated in a series of minigames, in which subjects were required to use eight gestures to manipulate their game avatar to complete a task. Experimental results indicated that relative to the baseline, the modified feedback condition led to significantly improved accuracy. Class separation also improved, though this trend was not significant. These findings suggest that real-time feedback in a gamified user interface with manipulation of feedback may enable intuitive, rapid, and accurate task acquisition for sEMG-based gesture recognition applications. [ABSTRACT FROM AUTHOR]

The poor generalization performance and heavy training burden of the gesture classification model contribute as two main barriers that hinder the commercialization of sEMG-based human-machine interaction (HMI) systems. To overcome these challenges, eight unsupervised transfer learning (TL) algorithms developed on the basis of convolutional neural networks (CNNs) were explored and compared on a dataset consisting of 10 gestures from 35 subjects. The highest classification accuracy obtained by CORrelation Alignment (CORAL) reaches more than 90%, which is 10% higher than the methods without using TL. In addition, the proposed model outperforms 4 common traditional classifiers (KNN, LDA, SVM, and Random Forest) using the minimal calibration data (two repeated trials for each gesture). The results also demonstrate the model has a great transfer robustness/flexibility for cross-gesture and cross-day scenarios, with an accuracy of 87.94% achieved using calibration gestures that are different with model training, and an accuracy of 84.26% achieved using calibration data collected on a different day, respectively. As the outcomes confirm, the proposed CNN TL method provides a practical solution for freeing new users from the complicated acquisition paradigm in the calibration process before using sEMG-based HMI systems. [ABSTRACT FROM AUTHOR]

Recent developments in dexterous myoelectric prosthetics have established a hardware base for human-machine interfaces. Although pattern recognition techniques have seen successful deployment in gesture classification, their applications remain largely confined to certain specific discrete gestures. Addressing complex daily tasks demands an immediate need for precise simultaneous and proportional control (SPC) for multiple degrees of freedom (DoFs) movements. In this paper, we introduce an SPC approach for multi-DoF wrist movements using the cumulative spike trains (CSTs) of motor unit pools, merely leveraging single-DoF training. The efficacy of our proposed approach was validated offline against existing methods respectively based on non-negative matrix factorization and motor unit spike trains, using experimental data. The experimental process includes both single-DoF (for training) and multi-DoF (for testing) movements. We evaluated the performance using Pearson correlation coefficient (R) and the normalized root mean square error (nRMSE). The results reveal that our method outperforms comparative approaches in force estimation for both testing datasets (3 and 4). On average, for dataset 3, R and nRMSE of the flexion/extension DoF (the pronation/supination DoF) are 0.923±0.037 (0.901±0.040) and 12.3±3.1% (12.9±2.2%); similarly, those of dataset 4 are 0.865±0.057 (0.837±0.053) and 14.9±2.9% (15.4±2.0%), respectively. The outcomes demonstrate the effectiveness of our method in simultaneous and proportional force estimation for multi-DoF wrist movements, showing a promising potential as a neural-machine interface for SPC of dexterous myoelectric prostheses. [ABSTRACT FROM AUTHOR]

For Brain-Computer Interface (BCI) based on motor imagery (MI), the MI task is abstract and spontaneous, presenting challenges in measurement and control and resulting in a lower signal-to-noise ratio. The quality of the collected MI data significantly impacts the cross-subject calibration results. To address this challenge, we introduce a novel cross-subject calibration method based on passive tactile afferent stimulation, in which data induced by tactile stimulation is utilized to calibrate transfer learning models for cross-subject decoding. During the experiments, tactile stimulation was applied to either the left or right hand, with subjects only required to sense tactile stimulation. Data from these tactile tasks were used to train or fine-tune models and subsequently applied to decode pure MI data. We evaluated BCI performance using both the classical Common Spatial Pattern (CSP) combined with the Linear Discriminant Analysis (LDA) algorithm and a state-of-the-art deep transfer learning model. The results demonstrate that the proposed calibration method achieved decoding performance at an equivalent level to traditional MI calibration, with the added benefit of outperforming traditional MI calibration with fewer trials. The simplicity and effectiveness of the proposed cross-subject tactile calibration method make it valuable for practical applications of BCI, especially in clinical settings. [ABSTRACT FROM AUTHOR]

Many rehabilitation exoskeletons have been used in the field of stroke rehabilitation. Generating human-like motion is necessary for exoskeletons to help patients perform activities of daily living (ADL) while maintaining interaction quality and ergonomics. However, most of the current motion generation algorithms utilize inverse kinematics (IK) to solve the final configuration before generation, and do not consider the movement of shoulder girdle. Separately considering the shoulder girdle motion and arm motion, this paper proposes an algorithm integrated IK to generate human-like motion. The arm moves towards the target with a bell-shaped velocity in the absence of the final configuration, and the shoulder girdle maintain natural passive motion. Moreover, the generated motion can be mapped to the configuration space of exoskeletons. Compared with the experimental data collected using a motion capture system, the values of RMSE and HPDI of the generated wrist trajectory in the task space are within 0.2 and 0.17, respectively, while those of RMSE in the joint space are within 15 deg, which demonstrates the human-like nature of the generated motion. [ABSTRACT FROM AUTHOR]

In recent years, robot-assisted training has been shown to significantly improve motor function and proprioception in people with functional disabilities, but the efficiency of proprioceptive acuity was unclear. To characterize the efficiency of joint proprioceptive acuity improvement in space, we designed a robot-assisted ipsilateral joint position matching experiment using the wrist as the study object. We conducted 2-way repeated measures ANOVA on error data before and after training in 12 healthy subjects and mapped the distribution of wrist proprioceptive learning ability in different workspaces. The results showed significant differences in the proprioceptive acuity of the wrist joint in different workspaces and movement directions before and after training in 12 subjects ($\text{p} < 0.01$), and the proprioceptive acuity of the wrist after training was significantly higher than before training. In addition, the learning ability of wrist proprioceptive acuity showed significant differences in different workspaces and movement directions (Flexion and Extension in habit workspace (HW) (${P}={0}.{037}$); Flexion and Extension in maximum workspace (MW) (${P}={0}.{016}$); Flexion in HW and MW (${P}={0}.{043}$)). Robot-assisted training is beneficial for improving the proprioceptive acuity of the wrist. The learning ability of proprioceptive acuity of joints in different movement directions is independently distributed and influenced by usage habits, which accelerate the improvement of proprioceptive acuity. This research hopes to clinically guide the development of highly effective rehabilitation programs to achieve better recovery and help build patient confidence. [ABSTRACT FROM AUTHOR]

Though the forearm is the focus of the prostheses, myoelectric control with the electrodes on the wrist is more comfortable for general consumers because of its unobtrusiveness and incorporation with the existing wrist-based wearables. Recently, deep learning methods have gained attention for myoelectric control but their performance is unclear on wrist myoelectric signals. This study compared the gesture recognition performance of myoelectric signals from the wrist and forearm between a state-of-the-art method, TDLDA, and four deep learning models, including convolutional neural network (CNN), temporal convolutional network (TCN), gate recurrent unit (GRU) and Transformer. It was shown that with forearm myoelectric signals, the performance between deep learning models and TDLDA was comparable, but with wrist myoelectric signals, the deep learning models outperformed TDLDA significantly with a difference of at least 9%, while the performance of TDLDA was close between the two signal modalities. This work demonstrated the potential of deep learning for wrist-based myoelectric control and would facilitate its application into more sections. [ABSTRACT FROM AUTHOR]

Recent studies on human upper limb motion highlighted the benefit of dimensionality reduction techniques to extrapolate informative joint patterns. These techniques can simplify the description of upper limb kinematics in physiological conditions, serving as a baseline for the objective assessment of movement alterations, or to be implemented in a robotic joint. However, the successful description of kinematic data requires a proper alignment of the acquisitions to correctly estimate kinematic patterns and their motion variability. Here, we propose a structured methodology to process and analyze upper limb kinematic data, considering time warping and task segmentation to register task execution on a common normalized completion time axis. Functional principal component analysis (fPCA) was used to extract patterns of motion of the wrist joint from the data collected by healthy participants performing activities of daily living. Our results suggest that wrist trajectories can be described as a linear combination of few functional principal components (fPCs). In fact, three fPCs explained more than 85% of the variance of any task. Wrist trajectories in the reaching phase of movement were highly correlated among participants and significantly more than trajectories in the manipulation phase ( $\text{p}< 0.01$ ). These findings may be useful in simplifying the control and design of robotic wrists, and could aid the development of therapies for the early detection of pathological conditions.

Motor brain-computer interface (BCI) refers to the BCI that decodes voluntary motion intentions from brain signals directly and outputs corresponding control commands without activating peripheral nerves and muscles. Motor BCIs can be used for the restoration, compensation, and augmentation of motor function by activating the neuromuscular circuit and facilitating neural plasticity. The essential applications of motor BCIs include neurorehabilitation and daily-life assistance for motor-impaired patients. In recent years, studies on motor BCIs mainly concentrate on neural signatures, movement decoding, and its applications. In this review, we aim to provide a comprehensive review of the state-of-the-art research of electroencephalography (EEG) signals-based motor BCIs for the first time. We also aim to give some insights into advancing motor BCIs to a more natural and practical application scenario. In particular, we focus on the motor BCIs for the movements of the upper limbs. Specifically, the experimental paradigms, techniques, and application systems of upper-limb BCIs are reviewed. Several vital issues in developing more natural and practical upper-limb motor BCIs, including developing target-users-oriented, distraction-robust, and multi-limbs motor BCIs, and applying fusion techniques to promote the natural and practical motor BCIs, are discussed. [ABSTRACT FROM AUTHOR]

Objective. In this study, we propose a tactile-assisted calibration method for a motor imagery (MI) based Brain-Computer Interface (BCI) system. Method. In the proposed calibration, tactile stimulation was applied to the hand wrist to assist the subjects in the MI task, which is named SA-MI task. Then, classifier training in the SA-MI Calibration was performed using the SA-MI data, while the Conventional Calibration employed the MI data. After the classifiers were trained, the performance was evaluated on a common MI dataset. Results. Our study demonstrated that the SA-MI Calibration significantly improved the performance as compared with the Conventional Calibration, with a decoding accuracy of (78.3% vs. 71.3%). Moreover, the average calibration time could be reduced by 40%. This benefit of the SA-MI Calibration effect was further validated by an independent control group, which showed no improvement when tactile stimulation was not applied during the calibration phase. Further analysis showed that when compared with MI, greater motor-related cortical activation and higher ${R} ^{{2}}$ value in the alpha-beta frequency band were induced in SA-MI. Conclusion. Indeed, the SA-MI Calibration could significantly improve the performance and reduce the calibration time as compared with the Conventional Calibration. Significance. The proposed tactile stimulation-assisted MI Calibration method holds great potential for a faster and more accurate system setup at the beginning of BCI usage. [ABSTRACT FROM AUTHOR]

Combining functional electrical stimulation (FES) and robotics may enhance recovery after stroke, by providing neural feedback with the former while improving quality of motion and minimizing muscular fatigue with the latter. Here, we explored whether and how FES, robot assistance and their combination, affect users’ performance, effort, fatigue and user experience. 15 healthy participants performed a wrist flexion/extension tracking task with FES and/or robotic assistance. Tracking performance improved during the hybrid FES-robot and the robot-only assistance conditions in comparison to no assistance, but no improvement is observed when only FES is used. Fatigue, muscular and voluntary effort are estimated from electromyographic recording. Total muscle contraction and volitional activity are lowest with robotic assistance, whereas fatigue level do not change between the conditions. The NASA-Task Load Index answers indicate that participants found the task less mentally demanding during the hybrid and robot conditions than the FES condition. The addition of robotic assistance to FES training might thus facilitate an increased user engagement compared to robot training and allow longer motor training session than with FES assistance. [ABSTRACT FROM AUTHOR]

Human-machine interfaces (HMIs) based on electromyography (EMG) signals have been developed for simultaneous and proportional control (SPC) of multiple degrees of freedom (DoFs). The EMG-driven musculoskeletal model (MM) has been used in HMIs to predict human movements in prosthetic and robotic control. However, the neural information extracted from surface EMG signals may be distorted due to their limitations. With the development of high density (HD) EMG decomposition, accurate neural drive signals can be extracted from surface EMG signals. In this study, a neural-driven MM was proposed to predict metacarpophalangeal (MCP) joint flexion/extension and wrist joint flexion/extension. Ten non-disabled subjects (male) were recruited and tested. Four 64-channel electrode grids were attached to four forearm muscles of each subject to record the HD EMG signals. The joint angles were recorded synchronously. The acquired HD EMG signals were decomposed to extract the motor unit (MU) discharge for estimating the neural drive, which was then used as the input to the MM to calculate the muscle activation and predict the joint movements. The Pearson’s correlation coefficient (r) and the normalized root mean square error (NRMSE) between the predicted joint angles and the measured joint angles were calculated to quantify the estimation performance. Compared to the EMG-driven MM, the neural-driven MM attained higher r values and lower NRMSE values. Although the results were limited to an offline application and to a limited number of DoFs, they indicated that the neural-driven MM outperforms the EMG-driven MM in prediction accuracy and robustness. The proposed neural-driven MM for HMI can obtain more accurate neural commands and may have great potential for medical rehabilitation and robot control. [ABSTRACT FROM AUTHOR]

Upper limb tremor is a prominent symptom of both Parkinson’s disease and essential tremor. Its kinematic parameters overlap substantially for these two pathological conditions, thus leading to high rate of misdiagnosis, especially for community doctors. Several groups have proposed various methods for improving differential diagnosis. These prior studies have attempted to identify better kinematic parameters, however they have mainly focused on single limb features including tremor intensity, tremor frequency, and tremor variability. In this paper, we propose a wearable system for multi-segment assessment of upper limb tremor and differential diagnosis of Parkinson’s disease versus essential tremor. The proposed system collected tremor data from both wrist and fingers simultaneously. From this data, we extracted multi-segment features in the form of phase relationships between limb segments. Using support vector machine classifiers, we then performed differential diagnosis from the extracted features. We evaluated the performance of the proposed system on 19 Parkinson’s disease patients and 12 essential tremor patients. Moreover, we also assessed the performance cost associated with reducing task load and sensor array size. The proposed system reached perfect accuracy in leave-one-out cross validation. Task reduction and sensor array reduction were associated with penalties of 2% and 9-10% respectively. The results demonstrated that the proposed system could be simplified for clinical applications, and successfully applied to the differential diagnosis of Parkinson’s disease versus essential tremor in real-world setting. [ABSTRACT FROM AUTHOR]

Cervical spinal cord injury (cSCI) often results in bilateral impairment of the arms, leading to difficulties in performing daily activities. However, little is known about the neuromotor alterations that affect the ability of individuals with cSCI to perform coordinated movements with both arms. To address this issue, we developed and tested a functional assessment that integrates clinical, kinematic, and muscle activity measures, including the evaluation of bilateral arm movements. Twelve subjects with a C5-C7 spinal lesion and six unimpaired subjects underwent an evaluation that included three tests: the Manual Muscle Test, Range Of Motion test and Arm stabilisation test, a subsection of the “Van Lieshout arm/hand function test”. During the latter, we recorded kinematic and muscle activity data from the upper-body during the execution of a set of movements that required participants to stabilize both arms against gravity at different configurations. Analytical methods, including muscle synergies, spinal maps, and Principal Component Analysis, were used to analyse the data. Clinical tests detected limitations in shoulder abduction-flexion of cSCI participants and alterations in elbows-wrists motor function. The instrumented assessment provided insight into how these limitations impacted the ability of cSCI participants to perform bilateral movements. They exhibited severe difficulty in performing movements involving over-the-shoulder motion and shoulder internal rotation due to altered patterns of activity of the scapular stabilizer muscles, latissimus dorsi, pectoralis, and triceps. Our findings shed light on the bilateral neuromotor changes that occur post-cSCI addressing not only motor deficits, but also the underlying abnormal, weak, or silent muscle activations. [ABSTRACT FROM AUTHOR]

We propose a neuromorphic framework to process the activity of human spinal motor neurons for movement intention recognition. This framework is integrated into a non-invasive interface that decodes the activity of motor neurons innervating intrinsic and extrinsic hand muscles. One of the main limitations of current neural interfaces is that machine learning models cannot exploit the efficiency of the spike encoding operated by the nervous system. Spiking-based pattern recognition would detect the spatio-temporal sparse activity of a neuronal pool and lead to adaptive and compact implementations, eventually running locally in embedded systems. Emergent Spiking Neural Networks (SNN) have not yet been used for processing the activity of in-vivo human neurons. Here we developed a convolutional SNN to process a total of 467 spinal motor neurons whose activity was identified in 5 participants while executing 10 hand movements. The classification accuracy approached ${0}.{95} \pm {0}.{14}$ for both isometric and non-isometric contractions. These results show for the first time the potential of highly accurate motion intent detection by combining non-invasive neural interfaces and SNN. [ABSTRACT FROM AUTHOR]

Surface electromyography (sEMG) is a non-invasive technique that measures the electrical activity generated by the muscles using sensors placed on the skin. It has been widely used in the field of prosthetics and other assistive systems because of the physiological connection between muscle electrical activity and movement dynamics. However, most existing sEMG-based decoding algorithms show a limited number of detectable degrees of freedom that can be proportionally and simultaneously controlled in real-time, which limits the use of EMG in a wide range of applications, including prosthetics and other consumer-level applications (e.g., human/machine interfacing). In this work, we propose a new deep learning method that can decode and map the electrophysiological activity of the forearm muscles into proportional and simultaneous control of > 20 degrees of freedom of the human hand with real-time resolution and with latency within the neuromuscular delays (< 50 ms). We recorded the kinematics of the human hand during grasping, pinching, individual digit movements and three gestures at slow (0.5 Hz) and fast (0.75 Hz) movement speeds in healthy participants. We demonstrate that our neural network can predict the kinematics of the hand in real-time at a constant 32 predictions per second. To achieve this, we employed transfer learning and created a prediction smoothing algorithm for the output of the neural network that reconstructed the full geometry of the hand in three-dimensional Cartesian space in real-time. Our results demonstrate that high-density EMG signals from the forearm muscles contain almost all the information that is needed to predict the kinematics of the human hand. The proposed method has the capability of predicting the full kinematics of the human hand with real-time resolution with immediate translational impact in subjects with motor impairments. [ABSTRACT FROM AUTHOR]

Although deep learning (DL) techniques have been extensively researched in upper-limb myoelectric control, system robustness in cross-day applications is still very limited. This is largely caused by non-stable and time-varying properties of surface electromyography (sEMG) signals, resulting in domain shift impacts on DL models. To this end, a reconstruction-based method is proposed for domain shift quantification. Herein, a prevalent hybrid framework that combines a convolutional neural network (CNN) and a long short-term memory network (LSTM), i.e. CNN-LSTM, is selected as the backbone. The paring of auto-encoder (AE) and LSTM, abbreviated as LSTM-AE, is proposed to reconstruct CNN features. Based on reconstruction errors (RErrors) of LSTM-AE, domain shift impacts on CNN-LSTM can be quantified. For a thorough investigation, experiments were conducted in both hand gesture classification and wrist kinematics regression, where sEMG data were both collected in multi-days. Experiment results illustrate that, when the estimation accuracy degrades substantially in between-day testing sets, RErrors increase accordingly and can be distinct from those obtained in within-day datasets. According to data analysis, CNN-LSTM classification/regression outcomes are strongly associated with LSTM-AE errors. The average Pearson correlation coefficients could reach $-0.986\,\,\pm $ 0.014 and $-0.992\,\,\pm $ 0.011, respectively. [ABSTRACT FROM AUTHOR]

Brain computer interface (BCI) is a system that directly uses brain neural activities to communicate with the outside world. Recently, the decoding of the human upper limb based on electroencephalogram (EEG) signals has become an important research branch of BCI. Even though existing research models are capable of decoding upper limb trajectories, the performance needs to be improved to make them more practical for real-world applications. This study is attempt to reconstruct the continuous and nonlinear multi-directional upper limb trajectory based on Chinese sign language. Here, to reconstruct the upper limb motion trajectory effectively, we propose a novel Motion Trajectory Reconstruction Transformer (MTRT) neural network that utilizes the geometric information of human joint points and EEG neural activity signals to decode the upper limb trajectory. Specifically, we use human upper limb bone geometry properties as reconstruction constraints to obtain more accurate trajectory information of the human upper limbs. Furthermore, we propose a MTRT neural network based on this constraint, which uses the shoulder, elbow, and wrist joint point information and EEG signals of brain neural activity during upper limb movement to train its parameters. To validate the model, we collected the synchronization information of EEG signals and upper limb motion joint points of 20 subjects. The experimental results show that the reconstruction model can accurately reconstruct the motion trajectory of the shoulder, elbow, and wrist of the upper limb, achieving superior performance than the compared methods. This research is very meaningful to decode the limb motion parameters for BCI, and it is inspiring for the motion decoding of other limbs and other joints. [ABSTRACT FROM AUTHOR]

Individuals with cervical spinal cord injury (C-SCI) often use a tenodesis grip to compensate for their hand function deficits. Although clinical evidence confirms that assistive devices can help achieve hand function improvements, the currently available devices have some limitations in terms of their price and accessibility and the difference in the user’s muscle strength. Therefore, in this study, we developed a 3D-printed wrist-driven orthosis to improve the gripping effect and tested the feasibility of this device by assessing its functional outcomes. A total of eight participants with hand function impairment due to a C-SCI were enrolled, and a wrist-driven orthosis with a triple four-bar linkage was designed. The hand function of the participants was assessed before and after they wore the orthosis, and the outcomes were assessed using a pinch force test, a dexterity test (Box and block test, BBT), and a Spinal Cord Independence Measure Version III questionnaire. In the results, before the participants wore the device, the pinch force was 0.26 lb. However, after they wore the device, it increased by 1.45 lb. The hand dexterity also increased by 37%. After 2 weeks, the pinch force increased by 1.6 lb and the hand dexterity increased by 78%. However, no significant difference was observed in the self-care ability. The results showed that this 3D-printed device with a triple four-bar linkage for individual with C-SCI improved pinch strength and hand dexterity in these patients, but did not improve their self-care ability. It may help patient in the early stages of C-SCI to learn and use the tenodesis grip easily. However, the usability of the device in daily life needs further research. [ABSTRACT FROM AUTHOR]

State-of-the-art myoelectric hand prostheses provide multi-functional control but lack somatosensory feedback. To accommodate the full functionality of a dexterous prosthesis, the artificial sensory feedback needs to convey several degrees of freedom (DoF) simultaneously. However, this is a challenge with current methods as they are characterized by a low information bandwidth. In this study, we leverage the flexibility of a recently developed system for simultaneous electrotactile stimulation and electromyography (EMG) recording to present the first solution for closed-loop myoelectric control of a multifunctional prosthesis with full-state anatomically congruent electrotactile feedback. The novel feedback scheme (coupled encoding) conveyed proprioceptive (hand aperture, wrist rotation) and exteroceptive information (grasping force). The coupled encoding was compared to the conventional approach (sectorized encoding) and incidental feedback in 10 non-disabled and one amputee participant who used the system to perform a functional task. The results showed that both feedback approaches increased the accuracy of position control compared to incidental feedback. However, the feedback increased completion time, and it did not significantly improve grasping force control. Importantly, the performance of the coupled feedback was not significantly different compared to the conventional scheme, despite the latter being easier to learn during training. Overall, the results indicate that the developed feedback can improve prosthesis control across multiple DoFs but they also highlight the subjects’ ability to exploit minimal incidental information. Importantly, the current setup is the first to convey three feedback variables simultaneously using electrotactile stimulation while providing multi-DoF myoelectric control with all hardware components mounted on the same forearm. [ABSTRACT FROM AUTHOR]

The surface electromyography (EMG) decomposition techniques provide access to motor neuron activities and have been applied to myoelectric control schemes. However, the current decomposition-based myoelectric control mainly focuses on discrete gestures or single-DoF continuous movements. In this study, we aimed to map the motor unit discharges, which were identified from high-density surface EMG, to the three degrees of freedom (DoFs) wrist movements. The 3-DoF wrist torques and high-density surface EMG signals were recorded concurrently from eight non-disabled subjects. The experimental protocol included single-DoF movements and their various combinations. We decoded the motor unit discharges from the EMG signals using a segment-wise decomposition algorithm. Then the neural features were extracted from motor unit discharges and projected to wrist torques with a multiple linear regression model. We compared the performance of two neural features (twitch model and spike counting) and two training schemes (single-DoF and multi-DoF training). On average, 145 ± 33 motor units were identified from each subject, with a pulse-to-noise ratio of 30.8 ± 4.2 dB. Both neural features exhibited high estimation accuracy of 3-DoF wrist torques, with an average $\text{R}^{{2}}$ of 0.76 ± 0.12 and normalized root mean square error of 11.4 ± 3.1%. These results demonstrated the efficiency of the proposed method in continuous estimation of 3-DoF wrist torques, which has the potential to advance dexterous myoelectric control based on neural information. [ABSTRACT FROM AUTHOR]

Contralateral controlled functional electrical stimulation (CCFES) can induce simultaneous movements in patients’ bilateral hands. It has been clinically proven to be effective in improving hand motor control and dexterity. sEMG and bending sensor-based data gloves for detecting patients’ motor intent have been developed with limitations. sEMG sensor signals are unstable and susceptible to noise. Data gloves composed of bending sensors require complicated calibration and tend to have data drift. In this paper, a LiDAR-based system for hand CCFES is proposed. The method utilized LiDAR to detect the patient’s motion intention without contact in CCFES systems. It has been clinically proven that LiDARs can effectively distinguish the different motion amplitudes of hand gestures as quantitative evaluation sensors of functional electrical stimulation (FES). Training data for classifiers were collected from 9 healthy individuals and 15 stroke patients performing 4 gestures, including hand opening, fist clenching, wrist extension, and wrist flexion. The support vector machine (SVM), linear discriminant analysis (LDA), and k-nearest neighbor (kNN) were verified for their classification performance in offline hand gesture recognition tests. Experiments were also conducted on 6 stroke volunteers to evaluate gestures triggered by FES. The SVM classifier showed excellent classification performance for four hand gestures, with an average F1-score of 0.97 ± 0.05 in offline tests. As for online gesture recognition, an average F1-score of 0.92 ± 0.09 was obtained. In the evaluation experiments, between data from 50% and 100% movement amplitude, paired t-tests showed significant differences. The experimental results indicated that the proposed system showed promise for hand rehabilitation. [ABSTRACT FROM AUTHOR]

MRI-compatible robots provide a means of studying brain function involved in complex sensorimotor learning processes, such as adaptation. To properly interpret the neural correlates of behavior measured using MRI-compatible robots, it is critical to validate the measurements of motor performance obtained via such devices. Previously, we characterized adaptation of the wrist in response to a force field applied via an MRI-compatible robot, the MR-SoftWrist. Compared to arm reaching tasks, we observed lower end magnitude of adaptation, and reductions in trajectory errors beyond those explained by adaptation. Thus, we formed two hypotheses: that the observed differences were due to measurement errors of the MR-SoftWrist; or that impedance control plays a significant role in control of wrist movements during dynamic perturbations. To test both hypotheses, we performed a two-session counterbalanced crossover study. In both sessions, participants performed wrist pointing in three force field conditions (zero force, constant, random). Participants used either the MR-SoftWrist or the UDiffWrist, a non-MRI-compatible wrist robot, for task execution in session one, and the other device in session two. To measure anticipatory co-contraction associated with impedance control, we collected surface EMG of four forearm muscles. We found no significant effect of device on behavior, validating the measurements of adaptation obtained with the MR-SoftWrist. EMG measures of co-contraction explained a significant portion of the variance in excess error reduction not attributable to adaptation. These results support the hypothesis that for the wrist, impedance control significantly contributes to reductions in trajectory errors in excess of those explained by adaptation. [ABSTRACT FROM AUTHOR]

In this paper we presented the mechanical design and evaluation of a low-profile and lightweight exoskeleton that supports the finger extension of stroke patients during daily activities without applying axial forces to the finger. The exoskeleton consists of a flexible structure that is secured to the index finger of the user while the thumb is fixed in an opposed position. Pulling on a cable will extend the flexed index finger joint such that objects can be grasped. The device can achieve a grasp size of at least 7 cm. Technical tests confirmed that the exoskeleton was able to counteract the passive flexion moments corresponding to the index finger of a severely affected stroke patient (with an MCP joint stiffness of k = 0.63Nm/rad), requiring a maximum cable activation force of 58.8N. A feasibility study with stroke patients (n=4) revealed that the body-powered operation of the exoskeleton with the contralateral hand caused a mean increase of 46° in the range of motion of the index finger MCP joint. The patients (n=2) who performed the Box & Block Test were able to grasp and transfer maximally 6 blocks in 60 sec. with exoskeleton, compared to 0 blocks without exoskeleton. Our results showed that the developed exoskeleton has the potential to partially restore hand function of stroke patients with impaired finger extension capabilities. An actuation strategy that does not involve the contralateral hand should be implemented during further development to make the exoskeleton suitable for bimanual daily activities. [ABSTRACT FROM AUTHOR]

Recent studies on human upper limb motion highlighted the benefit of dimensionality reduction techniques to extrapolate informative joint patterns. These techniques can simplify the description of upper limb kinematics in physiological conditions, serving as a baseline for the objective assessment of movement alterations, or to be implemented in a robotic joint. However, the successful description of kinematic data requires a proper alignment of the acquisitions to correctly estimate kinematic patterns and their motion variability. Here, we propose a structured methodology to process and analyze upper limb kinematic data, considering time warping and task segmentation to register task execution on a common normalized completion time axis. Functional principal component analysis (fPCA) was used to extract patterns of motion of the wrist joint from the data collected by healthy participants performing activities of daily living. Our results suggest that wrist trajectories can be described as a linear combination of few functional principal components (fPCs). In fact, three fPCs explained more than 85% of the variance of any task. Wrist trajectories in the reaching phase of movement were highly correlated among participants and significantly more than trajectories in the manipulation phase ($\text{p}< 0.01$). These findings may be useful in simplifying the control and design of robotic wrists, and could aid the development of therapies for the early detection of pathological conditions. [ABSTRACT FROM AUTHOR]

Crutch walking, especially when using a swing-through gait pattern, is associated with high, repetitive joint forces, hyperextension/ulnar deviation of the wrist, and excessive palmar pressure that compresses the median nerve. To reduce these adverse effects, we designed a pneumatic sleeve orthosis that utilized a soft pneumatic actuator and secured to the crutch cuff for long-term Lofstrand crutch users. Eleven non-disabled young adult participants performed both swing-through and reciprocal crutch gait patterns with and without the custom orthosis for comparison. Wrist kinematics, crutch forces, and palmar pressures were analyzed. Significantly different wrist kinematics, crutch kinetics, and palmar pressure distribution were observed in swing-through gait trials with orthosis use (p <0.001, p=0.01, p=0.03, respectively). Reductions in peak and mean wrist extension (7%, 6%), wrist range of motion (23%), and peak and mean ulnar deviation (26%, 32%) indicate improved wrist posture. Significantly increased peak and mean crutch cuff forces suggest increased load sharing between the forearm and cuff. Reduced peak and mean palmar pressures (8%, 11%) and shifted peak palmar pressure location toward the adductor pollicis denote a redirection of pressure away from the median nerve. In reciprocal gait trials, non-significant but similar trends were observed in wrist kinematics and palmar pressure distribution, whereas a significant effect of load sharing was noticed (p=0.01). These results suggest that Lofstrand crutches modified with orthosis may improve wrist posture, reduce wrist and palmar load, redirect palmar pressure away from the median nerve, and thus may reduce or prevent the onset of wrist injuries. [ABSTRACT FROM AUTHOR]

There has been a debate on the most appropriate way to evaluate electromyography (EMG)-based neural-machine interfaces (NMIs). Accordingly, this study examined whether a relationship between offline kinematic predictive accuracy (R2) and user real-time task performance while using the interface could be identified. A virtual posture-matching task was developed to evaluate motion capture-based control and myoelectric control with artificial neural networks (ANNs) trained to low (R2 ≈ 0.4), moderate (R2 ≈ 0.6), and high ($\text {R}^{\vphantom {\text {D}^{\text {a}}}{2}} \approx 0.8$) offline performance levels. Twelve non-disabled subjects trained with each offline performance level decoder before evaluating final real-time posture matching performance. Moderate to strong relationships were detected between offline performance and all real-time task performance metrics: task completion percentage (r = 0.66, p < 0.001), normalized task completion time (r = −0.51, p = 0.001), path efficiency (r = 0.74, p < 0.001), and target overshoots (r = −0.79, p < 0.001). Significant improvements in each real-time task evaluation metric were also observed between the different offline performance levels. Additionally, subjects rated myoelectric controllers with higher offline performance more favorably. The results of this study support the use and validity of offline analyses for optimization of NMIs in myoelectric control research and development. [ABSTRACT FROM AUTHOR]

Dementia is an increasing global health challenge. Motoric Cognitive Risk Syndrome (MCR) is a predementia stage that can be used to predict future occurrence of dementia. Traditionally, gait speed and subjective memory complaints are used to identify older adults with MCR. Our previous studies indicated that dual-task upper-extremity motor performance (DTUEMP) quantified by a single wrist-worn sensor was correlated with both motor and cognitive function. Therefore, the DTUEMP had a potential to be used in the diagnosis of MCR. Instead of using inertial sensors to capture kinematic data of upper-extremity movements, here we proposed a deep neural network-based video processing model to obtain DTUEMP metrics from a 20-second repetitive elbow flexion-extension test under dual-task condition. In details, we used a deep residual neural network to obtain joint coordinate set of the elbow and wrist in each frame, and then used optical flow method to correct the joint coordinates generated by the neural network. The coordinate sets of all frames in a video recording were used to generate an angle sequence which represents rotation angle of the line between the wrist and elbow. Then, the DTUEMP metrics (the mean and SD of flexion and extension phase) were derived from angle sequences. Multi-task learning (MTL) was used to assess cognitive and motor function represented by MMSE and TUG scores based on DTUEMP metrics, with single-task learning (STL) linear model as a benchmark. The results showed a good agreement (r $\ge0.80$ and ICC $\ge0.58$) between the derived DTUEMP metrics from our proposed model and the ones from clinically validated sensor processing model. We also found that there were correlations with statistical significance (p < 0.05) between some of video-derived DTUEMP metrics (i.e. the mean of flexion time and extension time) and clinical cognitive scale (Mini-Mental State Examination, MMSE). Additionally, some of video-derived DTUEMP metrics (i.e. the mean and standard deviation of flexion time and extension time) were also associated with the scores of timed-up and go (TUG) which is a gold standard to measure functional mobility. Mean absolute percentage error (MAPE) of MTL surpassed that of STL (For MMSE, MTL: 18.63%, STL: 23.18%. For TUG, MTL: 17.88%, STL: 22.53%). The experiments with different light conditions and shot angles verified the robustness of our proposed video processing model to extract DTUEMP metrics in potentially various home environments (r $\ge0.58$ and ICC $\ge0.71$). This study shows possibility of replacing sensor processing model with video processing model for analyzing the DTUEMP and a promising future to adjuvant diagnosis of MCR via a mobile platform. [ABSTRACT FROM AUTHOR]

Human wrist motion decoding with a biological-signal-based interface is a key technique in the upper-limb exoskeleton and prosthesis control. One critical issue in this field is achieving high recognition precision and fast time response while against external disturbances of sensor re-wearing. In this study, we proposed a high-framerate Electrical Impedance Tomography (EIT) system combined with an adaptive recognition algorithm for real-time wrist kinematics decoding. The high-framerate EIT system was developed by a parallel stimulation-measurement sequence, and the sampling rate was as high as 104 Hz. Compared to the most widely used myoelectric techniques, the EIT-based interface can provide extra deep muscular spatial information with similar surface electrodes. It greatly benefited the subsequent recognition algorithms, in which the key EIT regions indicating muscle morphology kept consistent after an arbitrary sensor re-donning. The designed adaptive algorithm achieved equally high performance with an automatic update of the classifier mean values with a fast self-operated calibration process. We validated the approach on 12 subjects with a 2-dimensional Fitts’ law test. The wrist gestures and joint angles were mapped to the direction and speed of the cursor movement, respectively. The average throughputs (TPs) of Fitts’ law tests were 1.0269 ± 0.0971 bits/s and 1.0095 ± 0.0931 bits/s without and with sensor re-donning, respectively, which were comparable to the TPs of sEMG-based studies. The results showed the promise of the EIT-based interface on real-time human motion intent recognition. Future endeavors are worth being paid in this direction for more complicated robotic tasks. [ABSTRACT FROM AUTHOR]

Forearm amputees can use body-powered hooks and myoelectric hands for their daily activities. The body-powered hooks are suitable for delicate manipulation. However, their appearance is not always preferred by amputees, and a harness to pull a control cable is not easy to wear. Although the myoelectric hands have a natural appearance similar to the human hand and can be intuitively controlled by a myoelectric control system, they are not easy to try out and are heavy. This paper reports on the Finch, a prosthetic arm with three opposing fingers controlled by a muscle bulge. The aim of developing the Finch is to realize a lightweight prosthetic arm that is easy to wear and use. Three opposing fingers are controlled according to the degree of muscle bulge measured with a muscle bulge sensor on the user’s forearm caused by muscle contraction. A supporter socket, consisting of a resin socket frame and a fabric supporter, allows easy fitting. A simple design using a linear actuator and 3D-printed parts achieved light weight (330 g) and low cost. Six functional tests and user tests using Southampton Hand Assessment Procedure showed that the Finch had a practical function that could be used in daily activities. [ABSTRACT FROM AUTHOR]

With the increasing availability of more advanced prostheses individuals with a transradial amputation can now be fit with single to multi-degree of freedom hands. Reliable and accurate control of these multi-grip hands still remains challenging. This is the first multi-user study to investigate at-home control and use of a multi-grip hand prosthesis under pattern recognition and direct control. Individuals with a transradial amputation were fitted with and trained to use an OSSUR i-Limb Ultra Revolution with Coapt COMPLETE CONTROL system. They participated in two 8-week home trials using the hand under myoelectric direct and pattern recognition control in a randomized order. While at home, participants demonstrated broader usage of grips in pattern recognition compared to direct control. After the home trial, they showed significant improvements in the Assessment of Capacity for Myoelectric Control (ACMC) outcome measure while using pattern recognition control compared to direct control; other outcome measures showed no differences between control styles. Additionally, this study provided a unique opportunity to evaluate EMG signals during home use. Offline analysis of calibration data showed that users were 81.5% [7.1] accurate across a range of three to five grips. Although EMG signal noise was identified during some calibrations, overall EMG quality was sufficient to provide users with control performance at or better than direct control. [ABSTRACT FROM AUTHOR]

The design of prosthetic controllers by means of neurophysiological signals still poses a crucial challenge to bioengineers. State of the art of electromyographic (EMG) continuous pattern recognition controllers rely on the questionable assumption that repeated muscular contractions produce repeatable patterns of steady-state EMG signals. Conversely, we propose an algorithm that decodes wrist and hand movements by processing the signals that immediately follow the onset of contraction (i.e., the $\textit {transient}$ EMG). We collected EMG data from the forearms of 14 non-amputee and 5 transradial amputee participants while they performed wrist flexion/extension, pronation/supination, and four hand grasps (power, lateral, bi-digital, open). We firstly identified the combination of wrist and hand movements that yielded the best control performance for the same participant (intra-subject classification). Then, we assessed the ability of our algorithm to classify participant data that were not included in the training set (cross-subject classification). Our controller achieved a median accuracy of ~96% with non-amputees, while it achieved heterogeneous outcomes with amputees, with a median accuracy of ~89%. Importantly, for each amputee, it produced at least one $\textit {acceptable}$ combination of wrist-hand movements (i.e., with accuracy >85%). Regarding the cross-subject classifier, while our algorithm obtained promising results with non-amputees (accuracy up to ~80%), they were not as good with amputees (accuracy up to ~35%), possibly suggesting further assessments with domain-adaptation strategies. In general, our offline outcomes, together with a preliminary online assessment, support the hypothesis that the transient EMG decoding could represent a viable pattern recognition strategy, encouraging further online assessments. [ABSTRACT FROM AUTHOR]

The impact of tissue movements on the accuracy of heart rate (HR) estimates is a challenge in today’s wearable technology. Tissue movements are caused by muscle activity that modifies the optical path of the reflectance photoplethysmography (PPG), leading to motion artifacts (MAs) that mask the true HR. This kind of MA is not always detected using accelerometers (ACCs). In this study, we propose a method to increase the PPG HR accuracy of a wristwatch using wrist surface electromyogram (EMG) and ACC using spectrum subtraction algorithms. We collected the wrist EMG, wristwatch PPG, ACC data, and the electrocardiogram (ECG) from nine subjects. Data were recorded during four frequent hand movements and three activities (weightlifting and running/walking with and without holding a racket). The added value of the EMG was studied. Visual results indicate that wrist EMG correlates well with the MA seen in the PPG signal and provides additional information over the typically used ACC data. Our analysis showed that the proposed artifact removal method using EMG and ACC decreases the HR estimation error on average by 49% compared to only ACC. Our results showed that wrist EMG contains complementary information on the PPG artifacts and offers a novel signal modality for improving optical HR estimation accuracy in smartwatches. [ABSTRACT FROM AUTHOR]

Computational pulse diagnosis is a convenient, non-invasive, and effective Diabetes Mellitus (DM) detection technique. Generally, diverse pulse features are extracted from different views to represent pulse signals and then used for achieving the pulse diagnosis. However, current pulse-based DM detection methods only used one pulse feature for detection, ignoring the fact that diverse pulse features can be combined together to boost the diagnosis performance. To this end, we propose a novel Multi-Feature Complementary Learning (MFCL) model for DM detection. By designing feature-specific projections, multiple features are separately projected into a shared observation space and effectively fused into one vector. Besides, a mapping function is built to correlate the fused vectors to category labels to make the fused vectors suitable for classification. Inspired by the graph Laplacian matrix, which effectively preserves the correlations among samples from different categories, we integrate it in MFCL and design a discriminative prior to make the fused vectors sufficiently discriminative. Finally, an optimization algorithm is proposed to alternatively optimize the projection variables and then generate fused feature vectors. The proposed method reaches an accuracy of 92.85% in DM detection, outperforming state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Haptic feedback is known to enhance the realism of an individual’s interactions with objects in virtual environments. Wearable haptic devices, such as vibrotactile sleeves or armbands, can provide haptic feedback in a smaller and more lightweight form factor than haptic gloves that can be bulky and cumbersome to the wearer. In this article, we present tactile and squeeze bracelet interface (Tasbi), a multimodal haptic wristband that can provide radial squeeze forces around the wrist along with vibrotactile feedback at six discrete locations around the band. Tasbi implements a squeezing mechanism that minimizes tangential forces between the band’s points of contact with the skin, instead of focusing the motor actuation to predominantly normal forces. Force sensing capacitors enable closed-loop control of the squeeze force, while vibration is achieved with linear resonant actuators. A detailed description of the design and experimental results demonstrating closed-loop control of squeeze cues provided by Tasbi is presented. Additionally, we present the results of psychophysical experiments that quantify user perception of the vibration and squeeze cues, including vibrotactile identification accuracy in the presence of varying squeeze forces, discrimination thresholds for the squeeze force, and an analysis of user preferences for squeeze actuation magnitudes. [ABSTRACT FROM AUTHOR]

Human–computer communication using hand gestures has always been difficult. More than half a century ago, people used differentways of interactionwith computers from the early mediums such as perforated game cards. Nowadays, if a richer lexicon of gestures is given, people can communicate more effectively with computers. Machine learning is now used to recognize and classify hand gestures in amore preciseway. In order to increase the communication between computers and humans, we proposed a technique, which uses a wearable low-cost device to generate the electrical impedance tomography (EIT) images to recover the inner impedance structure of a user’s wrist. This is done by measuring the transverse impedance between all the 16 pairs of electrodesofwrist band that lie on the skin of the user hand. The proposedtechnique is enough to integrate the technology into the prototypewrist band tomonitor and classify gestures in real time. We have conducted a study of 16 gestures with a focus on gross hand and pinch finger gestures. The results evaluation shows that the gross hand gestures achieved 90% accuracy inwrist position, while pinch gestures achieved 93% accuracy. [ABSTRACT FROM AUTHOR]