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Recent innovations in oscillometric blood pressure (BP) estimation leveraging pulse transit time (PTT) show promise even when pulse amplitude is compromised. However, current PTT methods often require multiple sensors. This study introduces a novel approach for oscillometric BP estimation utilizing only a single sensor. Our method simplifies PTT measurement through decomposing oscillometric pulses into forward and reflected wave components. Notably, we found that the time interval between the forward and first reflected wave is shortest when cuff pressure is at the systolic level, while the interval between the first and second reflected waves is longest when the cuff pressure corresponds to the diastolic level. To validate our method, we extensively analyzed two datasets: the first comprised 150 arm oscillometric recordings from 10 healthy individuals, and the second included 425 wrist oscillometric recordings from 85 healthy individuals. Our analysis revealed minimal standard deviation errors for BP measurements: 7.03 mmHg (systolic) and 5.96 mmHg (diastolic) for arm recordings, and 7.98 mmHg (systolic) and 5.39 mmHg (diastolic) for wrist recordings. Across both datasets, the mean error for both systolic and diastolic BP measurements was merely 0.01 mmHg, underscoring the precision of the method. Comparative analysis against established methodologies further affirmed the superior performance of our approach, which also met the ANSI/AAMI/ISO81060-2 standard. While our dataset primarily comprises recordings from healthy individuals, further validation on diverse datasets encompassing challenging cases such as obesity, arterial stiffness, and atrial fibrillation is imperative to evaluate its applicability across various clinical scenarios.

Eating speed is an important indicator that has been widely investigated in nutritional studies. The relationship between eating speed and several intake-related problems such as obesity, diabetes, and oral health has received increased attention from researchers. However, existing studies mainly use self-reported questionnaires to obtain participants' eating speed, where they choose options from slow, medium, and fast. Such a non-quantitative method is highly subjective and coarse at the individual level. This study integrates two classical tasks in automated food intake monitoring domain: bite detection and eating episode detection, to advance eating speed measurement in near-free-living environments automatically and objectively. Specifically, a temporal convolutional network combined with a multi-head attention module (TCN-MHA) is developed to detect bites (including eating and drinking gestures) from IMU data. The predicted bite sequences are then clustered into eating episodes. Eating speed is calculated by using the time taken to finish the eating episode to divide the number of bites. To validate the proposed approach on eating speed measurement, a 7-fold cross validation is applied to the self-collected fine-annotated full-day-I (FD-I) dataset, and a holdout experiment is conducted on the full-day-II (FD-II) dataset. The two datasets are collected from 61 participants with a total duration of 513 h, which are publicly available. Experimental results show that the proposed approach achieves a mean absolute percentage error (MAPE) of 0.110 and 0.146 in the FD-I and FD-II datasets, respectively, showcasing the feasibility of automated eating speed measurement in near-free-living environments.

Myoelectric prostheses are generally unable to accurately control the position and force simultaneously, prohibiting natural and intuitive human-machine interaction. This issue is attributed to the limitations of myoelectric interfaces in effectively decoding multi-degree-of-freedom (multi-DoF) kinematic and kinetic information. We thus propose a novel multi-task, spatial-temporal model driven by graphical high-density electromyography (HD-EMG) for simultaneous and proportional control of wrist angle and grasp force. Twelve subjects were recruited to perform three multi-DoF movements, including wrist pronation/supination, wrist flexion/extension, and wrist abduction/adduction while varying grasp force. Experimental results demonstrated that the proposed model outperformed five baseline models, with the normalized root mean square error of 13.2% and 9.7% and the correlation coefficient of 89.6% and 91.9% for wrist angle and grasp force estimation, respectively. In addition, the proposed model still maintained comparable accuracy even with a significant reduction in the number of HD-EMG electrodes. To the best of our knowledge, this is the first study to achieve simultaneous and proportional wrist angle and grasp force control via HD-EMG and has the potential to empower prostheses users to perform a broader range of tasks with greater precision and control, ultimately enhancing their independence and quality of life.

Estimating cumulative spike train (CST) of motor units (MUs) from surface electromyography (sEMG) is essential for the effective control of neural interfaces. However, the limited accuracy of existing estimation methods greatly hinders the further development of neural interface. This paper proposes a simple but effective approach for identifying CST based on spatial spike detection from high-density sEMG. Specifically, we use a spatial sliding window to detect spikes according to the spatial propagation characteristics of the motor unit action potential, focusing on the spikes of activated MUs in a local area rather than those of a specific MU. We validated the effectiveness of our proposed method through an experiment involving wrist flexion/extension and pronation/supination, comparing it with a recognized CST estimation method and an MU decomposition based method. The results demonstrated that the proposed method obtained higher accuracy on multi-DoF wrist torque estimation leveraging the estimated CST compared to the other three methods. On average, the correlation coefficient (R) and the normalized root mean square error (nRMSE) between the estimation results and recorded force were 0.96 ± 0.03 and 10.1% ± 3.7%, respectively. Moreover, there was an extremely high interpretive extent between the CSTs of proposed method and the MU decomposition method. The outcomes reveal the superiority of the proposed method in identifying CSTs and can provide promising driven signals for neural interface.

Electromyography (EMG) is a popular human-machine interface for hand gesture control of assistive and rehabilitative technology. EMG can be used to estimate motor intent even when an individual cannot physically move due to weakness or paralysis. EMG is traditionally recorded from the extrinsic hand muscles located in the forearm. However, the wrist has become an increasingly attractive recording location for commercial applications as EMG sensors can be integrated into wrist-worn wearables (e.g., watches, bracelets). Here we explored the impact that recording EMG from the wrist, instead of the forearm, has on stroke patients with upper-limb hemiparesis. We show that EMG signal-to-noise ratio is significantly worse at the paretic wrist relative to the paretic forearm and non-paretic wrist. Despite this, we also show that the ability to classify hand gestures from EMG was significantly better at the paretic wrist relative to the paretic forearm. Our results also provide guidance as to the ideal gestures for each recording location. Namely, single-digit gestures appeared easiest to classify from both forearm and wrist EMG on the paretic side. These results suggest commercialization of wrist-worn EMG would benefit stroke patients by providing more accurate EMG control in a more widely adopted wearable formfactor.

This work describes a three-degrees-of-freedom rehabilitation exoskeleton robot for wrist articulation movement: the Biomech-Wrist. The proposed development includes the design requirements based on the biomechanics and anthropometric features of the upper limb, the mechanical design, electronic instrumentation, software design, manufacturing, control algorithm implementation, and the experimental setup to validate the functionality of the system. The design requirements were set to achieve human wrist-like movements: ulnar-radial deviation, flexion-extension, and pronation-supination. Then, the mechanical design considers the human range of motion with proper torques, velocities, and geometry. The manufacturing consists of 3D-printed elements and tubular aluminum sections resulting in lightweight components with modifiable distances. The central aspect of the instrumentation is the actuation system consisting of three brushless motors and a microcontroller for the control implementation. The proposed device was evaluated by considering two control schemes to regulate the trajectory tracking on each joint. The first scheme was the conventional proportional-derivative controller, whereas the second was proposed as a first-order sliding mode. The results show that the Biomech-Wrist exoskeleton can perform trajectory tracking with high precision ( RMSE max = 0.0556 rad) when implementing the sliding mode controller.

Stress is an inevitable part of modern life. While stress can negatively impact a person's life and health, positive and under-controlled stress can also enable people to generate creative solutions to problems encountered in their daily lives. Although it is hard to eliminate stress, we can learn to monitor and control its physical and psychological effects. It is essential to provide feasible and immediate solutions for more mental health counselling and support programs to help people relieve stress and improve their mental health. Popular wearable devices, such as smartwatches with several sensing capabilities, including physiological signal monitoring, can alleviate the problem. This work investigates the feasibility of using wrist-based electrodermal activity (EDA) signals collected from wearable devices to predict people's stress status and identify possible factors impacting stress classification accuracy. We use data collected from wrist-worn devices to examine the binary classification discriminating stress from non-stress. For efficient classification, five machine learning-based classifiers were examined. We explore the classification performance on four available EDA databases under different feature selections. According to the results, Support Vector Machine (SVM) outperforms the other machine learning approaches with an accuracy of 92.9 for stress prediction. Additionally, when the subject classification included gender information, the performance analysis showed significant differences between males and females. We further examine a multimodal approach for stress classifications. The results indicate that wearable devices with EDA sensors have a great potential to provide helpful insight for improved mental health monitoring.

Objective: The purpose of this work was to develop an open-source musculoskeletal model of the hand and wrist and to evaluate its performance during simulations of functional tasks., Methods: The current model was developed by adapting and expanding upon existing models. An optimal control theory framework that combines forward-dynamics simulations with a simulated-annealing optimization was used to simulate maximum grip and pinch force. Active and passive hand opening were simulated to evaluate coordinated kinematic hand movements., Results: The model's maximum grip force production matched experimental measures of grip force, force distribution amongst the digits, and displayed sensitivity to wrist flexion. Simulated lateral pinch strength replicated in vivo palmar pinch strength data. Additionally, predicted activations for 7 of 8 muscles fell within variability of EMG data during palmar pinch. The active and passive hand opening simulations predicted reasonable activations and demonstrated passive motion mimicking tenodesis, respectively., Conclusion: This work advances simulation capabilities of hand and wrist models and provides a foundation for future work to build upon., Significance: This is the first open-source musculoskeletal model of the hand and wrist to be implemented during both functional kinetic and kinematic tasks. We provide a novel simulation framework to predict maximal grip and pinch force which can be used to evaluate how potential surgical and rehabilitation interventions influence these functional outcomes while requiring minimal experimental data.

Objective: Persons with normal arm function can perform complex wrist and hand movements over a wide range of limb positions. However, for those with transradial amputation who use myoelectric prostheses, control across multiple limb positions can be challenging, frustrating, and can increase the likelihood of device abandonment. In response, the goal of this research was to investigate convolutional neural network (RCNN)-based position-aware myoelectric prosthesis control strategies., Methods: Surface electromyographic (EMG) and inertial measurement unit (IMU) signals, obtained from 16 non-disabled participants wearing two Myo armbands, served as inputs to RCNN classification and regression models. Such models predicted movements (wrist flexion/extension and forearm pronation/supination), based on a multi-limb-position training routine. RCNN classifiers and RCNN regressors were compared to linear discriminant analysis (LDA) classifiers and support vector regression (SVR) regressors, respectively. Outcomes were examined to determine whether RCNN-based control strategies could yield accurate movement predictions, while using the fewest number of available Myo armband data streams., Results: An RCNN classifier (trained with forearm EMG data, and forearm and upper arm IMU data) predicted movements with 99.00% accuracy (versus the LDA's 97.67%). An RCNN regressor (trained with forearm EMG and IMU data) predicted movements with R 2 values of 84.93% for wrist flexion/extension and 84.97% for forearm pronation/supination (versus the SVR's 77.26% and 60.73%, respectively). The control strategies that employed these models required fewer than all available data streams., Conclusion: RCNN-based control strategies offer novel means of mitigating limb position challenges., Significance: This research furthers the development of improved position-aware myoelectric prosthesis control.

A wrist joint in upper limb prostheses significantly increases its handling capacity. However, current prostheses cannot reproduce the ability of torque combined with the volume and weight of the human wrist. Consequently, they do not provide high efficiency in handling and generate user dissatisfaction. In this context, this study aims to optimal design a wrist supination and pronation brake to improve the handling capacity of an upper limb prosthesis. The wrist actuator consists of an EC motor and harmonic drive parallel with a magnetorheological brake. The brake guarantees a fast response time, low energy consumption, controllability, and small dimensions. A particle swarm algorithm is applied to optimize design variables to minimize mass and energy consumption. As a result, the brake provided resistive torque of 7.4 N.m with dimensions close to a healthy member and weighing 0.1972 kg. Finally, a finite element analysis confirmed a satisfactory magnetic flux for the magnetorheological brake operating conditions. The designed brake addressed all the desired characteristics and is suitable to integrate the forearm prosthesis with wrist rotation.

A human hand is a complex biomechanical system, in which bones, ligaments, and musculotendon units dynamically interact to produce seemingly simple motions. A new physiological hand simulator has been developed, in which electromechanical actuators apply load to the tendons of extrinsic hand and wrist muscles to recreate movements in cadaveric specimens in a biofidelic way. This novel simulator simultaneously and independently controls the movements of the wrist (flexion/extension and radio-ulnar deviation) and flexion/extension of the fingers and thumb. Control of these four degrees of freedom (DOF) is made possible by actuating eleven extrinsic muscles of the hand. The coupled dynamics of the wrist, fingers, and thumb, and the over-actuated nature of the human musculoskeletal system make feedback control of hand movements challenging. Two control algorithms were developed and tested. The optimal controller relies on an optimization algorithm to calculate the required tendon tensions using the collective error in all DOFs, and the action-based controller loads the tendons solely based on their actions on the controlled DOFs (e.g., activating all flexors if a flexing moment is required). Both controllers resulted in hand movements with small errors from the reference trajectories ( ); however, the optimal controller achieved this with 16% lower total force. Owing to its simpler structure, the action-based controller was extended to enable feedback control of grip force. This simulator has been shown to be a highly repeatable tool (  N and variations in force and kinematics, respectively) for in vitro analyses of human hand biomechanics.

A wearable electrical impedance tomographic (wEIT) sensor with 8 electrodes is developed to realize gesture recognition with machine learning algorithms. To optimize the wEIT sensor, gesture recognition rates are compared by using a series of electrodes with different materials and shapes. To improve the gesture recognition rates, several Machine Learning algorithms are used to recognize three different gestures with the obtained voltage data. To clarify the gesture recognition mechanism, an electrical model of the electrode-skin contact impedance is established. Experimental results show that: rectangular copper electrodes realize the highest recognition rate; the existence of the electrode-skin contact impedance could improve the gesture recognition rate; Medium Gaussian SVM (Support Vector Machine) algorithm is the optimal algorithm with an average recognition rate of 95%.

Heart rate (HR) monitoring using photoplethysmography (PPG) is a promising feature in modern wearable devices. PPG is easily contaminated by motion artifacts (MA), hindering estimation of HR. For quasi-periodic motions, previous works generally focused on a few specific motions, such as walking and fast running. However, they may not work well for many different quasi-periodic motions where MA are very complex. In this paper, a robust HR monitoring scheme for different quasi-periodic motions using wrist-type PPG is proposed, which consists of dictionary learning for signal characteristics learning, human motion recognition for the current motion recognition and dictionary selection, sparse representation-based MA elimination for denoising, and spectral peak tracking for HR-related spectral peak tracking. The proposed scheme is robust to MA caused by different motions and has high accuracy. Experiments on six common quasi-periodic motions showed that the average absolute error of heart rate estimation was 2.40 beat per minute, and also showed that the proposed method is more robust than some state-of-the-art approaches for different motions.

Gait speed is an important parameter to characterize people's daily mobility. For real-world speed measurement, inertial sensors or global navigation satellite system (GNSS) can be used on wrist, possibly integrated in a wristwatch. However, power consumption of GNSS is high and data are only available outdoor. Gait speed estimation using wrist-mounted inertial sensors is generally based on machine learning and suffers from low accuracy because of the inadequacy of using limited training data to build a general speed model that would be accurate for the whole population. To overcome this issue, a personalized model was proposed, which took unique gait style of each subject into account. Cadence and other biomechanically derived gait features were extracted from a wrist-mounted accelerometer and barometer. Gait features were fused with few GNSS data (sporadically sampled during gait) to calibrate the step length model of each subject through online learning. The proposed method was validated on 30 healthy subjects where it has achieved a median [Interquartile Range] of root mean square error of 0.05 [0.04-0.06] (m/s) and 0.14 [0.11-0.17] (m/s) for walking and running, respectively. Results demonstrated that the personalized model provided similar performance as GNSS. It used 50 times less training GNSS data than nonpersonalized method and achieved even better results. This parsimonious GNSS usage allowed extending battery life. The proposed algorithm met requirements for applications which need accurate, long, real-time, low-power, and indoor/outdoor speed estimation in daily life.

Surgery is a particularly potent stressor and the detrimental effects of stress on people undergoing any surgery is indisputable. When left unchecked, the pre-surgery stress adversely impacts people's physical and psychological well-being, and may even evolve into severe pathological states. Therefore, it is essential to identify levels of preoperative stress in surgical patients. This paper focuses on developing an automatic pre-surgery stress detection scheme based on electrodermal activity (EDA). The measurement set up involves a wrist wearable that monitors EDA of a subject continuously in the most non-invasive and unobtrusive manner. Data were collected from 41 subjects [17 females and 24 males, age: 54.8 ± 16.8 years (mean ± SD)], who subsequently underwent different surgical procedures at the Sri Ramakrishna Hospital, Coimbatore, India. A supervised machine learning algorithm that detects motion artifacts in the recorded EDA data was developed. It yielded an accuracy of 97.83% on a new user dataset. The clean EDA data were further analyzed to determine low, moderate, and high levels of stress. A novel localized supervised learning scheme based on the adaptive partitioning of the dataset was adopted for stress detection. Consequently, the interindividual variability in the EDA due to person-specific factors such as the sweat gland density and skin thickness, which may lead to erroneous classification, could be eliminated. The scheme yielded a classification accuracy of 85.06% on a new user dataset and proved to be more effective than the general supervised classification model.

Objective: Several forward dynamics estimation approaches have been proposed to estimate individual muscle force. However, characterization of the estimation error that arises when measurements are available only from a subset of the muscles involved in the movement under analysis, as is the case of the forearm muscles, has been limited. Our objectives were: first, to quantify the accuracy of forward-dynamics muscle force estimators for forearm muscles; and second, to develop a muscle force estimator that is accurate even when measurements are available only from a subset of muscles acting on a given joint or segment., Methods: We developed a neuromusculoskeletal (NMSK) estimator that integrates forward dynamics estimation with a neural model of muscle cocontraction to estimate individual muscle force during isometric contractions, suitable to operate when measurements are not available for all muscles. We developed a computational framework to assess the effect of physiological variability in muscle cocontraction, cross-talk, and measurement error on the estimator accuracy using a sensitivity analysis. We thus compared the performance of our estimator with that of a standard estimator that neglects the contribution of unmeasured muscles., Results: The NMSK estimator reduces the estimation error by 25% in average noise conditions. Moreover, the NMSK estimator is robust against physiological variability in muscle cocontraction and outperforms the standard estimator even when the validity of the neural model is compromised., Conclusion and Significance: In isometric tasks, the NMSK estimator reduces muscle force estimation error compared to a standard estimator, and may enable future applications involving estimation of forearm muscle force during coordinated movements.

Overweight and obesity are both associated with in-meal eating parameters such as eating speed. Recently, the plethora of available wearable devices in the market ignited the interest of both the scientific community and the industry toward unobtrusive solutions for eating behavior monitoring. In this paper, we present an algorithm for automatically detecting the in-meal food intake cycles using the inertial signals (acceleration and orientation velocity) from an off-the-shelf smartwatch. We use five specific wrist micromovements to model the series of actions leading to and following an intake event (i.e., bite). Food intake detection is performed in two steps. In the first step, we process windows of raw sensor streams and estimate their micromovement probability distributions by means of a convolutional neural network. In the second step, we use a long short-term memory network to capture the temporal evolution and classify sequences of windows as food intake cycles. Evaluation is performed using a challenging dataset of 21 meals from 12 subjects. In our experiments, we compare the performance of our algorithm against three state-of-the-art approaches, where our approach achieves the highest F1 detection score (0.913 in the leave-one-subject-out experiment). The dataset used in the experiments is available at https://mug.ee.auth.gr/intake-cycle-detection/.

The assessment of Parkinson's disease (PD) poses a significant challenge, as it is influenced by various factors that lead to a complex and fluctuating symptom manifestation. Thus, a frequent and objective PD assessment is highly valuable for effective health management of people with Parkinson's disease (PwP). Here, we propose a method for monitoring PwP by stochastically modeling the relationships between wrist movements during unscripted daily activities and corresponding annotations about clinical displays of movement abnormalities. We approach the estimation of PD motor signs by independently modeling and hierarchically stacking Gaussian process models for three classes of commonly observed movement abnormalities in PwP including tremor, (non-tremulous) bradykinesia, and (non-tremulous) dyskinesia. We use clinically adopted severity measures as annotations for training the models, thus allowing our multi-layer Gaussian process prediction models to estimate not only their presence but also their severities. The experimental validation of our approach demonstrates strong agreement of the model predictions with these PD annotations. Our results show that the proposed method produces promising results in objective monitoring of movement abnormalities of PD in the presence of arbitrary and unknown voluntary motions, and makes an important step toward continuous monitoring of PD in the home environment.

Intersubject variability in accelerometer-based activity recognition may significantly affect classification accuracy, limiting a reliable extension of methods to new users. In this paper, we propose an approach for personalizing classification rules to a single person. We demonstrate that the method improves activity detection from wrist-worn accelerometer data on a four-class recognition problem of interest to the exercise science community, where classes are ambulation, cycling, sedentary, and other. We extend a previously published activity classification method based on support vector machines so that it estimates classification uncertainty. Uncertainty is used to drive data label requests from the user, and the resulting label information is used to update the classifier. Two different datasets-one from 33 adults with 26 activity types, and another from 20 youth with 23 activity types-were used to evaluate the method using leave-one-subject-out and leave-one-group-out cross validation. The new method improved overall recognition accuracy up to 11% on average, with some large person-specific improvements (ranging from -2% to +36%). The proposed method is suitable for online implementation supporting real-time recognition systems.

According to the World Health Organization, stroke is the third leading cause of disability. A common consequence of stroke is hemiparesis, which leads to the impairment of one side of the body and affects the performance of activities of daily living. It has been proven that targeting the motor impairments as early as possible while using wearable mechatronic devices as a robot assisted therapy, and letting the patient be in control of the robotic system, can improve the rehabilitation outcomes. However, despite the increased progress on control methods for wearable mechatronic devices, a need for a more natural interface that allows for better control remains. In this work, a user-independent gesture classification method based on a sensor fusion technique using surface electromyography (EMG) and an inertial measurement unit (IMU) is presented. The Myo Armband was used to extract EMG and IMU data from healthy subjects. Participants were asked to perform 10 types of gestures in 4 different arm positions while using the Myo on their dominant limb. Data obtained from 14 participants were used to classify the gestures using a Multilayer Perceptron Network. Finally, the classification algorithm was tested on 5 novel users, obtaining an average accuracy of 78.94%. These results demonstrate that by using the proposed approach, it is possible to achieve a more natural human machine interface that allows better control of wearable mechatronic devices during robot assisted therapies.

Individuals with paralyzed limbs due to spinal cord injuries lack the ability to perform the reaching motions necessary to every day life. Functional electrical stimulation (FES) is a promising technology for restoring reaching movements to these individuals by reanimating their paralyzed muscles. We have proposed using a quasi-static model-based control strategy to achieve reaching controlled by FES. This method uses a series of static positions to connect the starting wrist position to the goal. As a first step to implementing this controller, we have completed a simulated study using a MATLAB based dynamic model of the arm in order to determine the suitable parameters for the quasi-static controller. The selected distance between static positions in the path was 6 cm, and the amount of time between switching target positions was 1.3 s. The final controller can complete reaches of over 30 cm with a median accuracy of 6.8 cm.

Recent technological improvements are consistently improving the efficacy of wearable mechatronic devices designed to support rehabilitation. However, it has been identified that there is currently a limited number of devices that can perform resistive motion tasks. To address this limitation, a Wearable Mechatronics-Enabled (WearME) Glove has been developed to support rehabilitative motion tasks. Using the WearME Glove, a control system was developed to enable the performance of resistive finger and wrist motion tasks. An initial evaluation of the device applied to rehabilitation tasks shows that average control errors of 2.4% and 1.5% were achieved for a resistive finger task and a resistive wrist flexion-extension task, respectively. In addition, an analysis of each task showed that for the index finger, the thumb and the wrist motion, an average of 69%, 76% and 83% of the duration, respectively, were being resisted by the WearME Glove. The results of this study show that the WearME glove can provide consistent resistance to the finger and wrist for different rehabilitation tasks.

Upper limb loss is a devastating injury for which current prosthetic replacement inadequately compensates. A lack of wrist movement in prostheses due to mechanical design and control system considerations compels prosthetic users to employ compensatory movements using their upper back and shoulder that can eventually result in strain and overuse injuries. One possible means of easing this control burden is to allow a prosthetic wrist to self-regulate, keeping the terminal device of the prosthesis level relative to the ground when appropriate, such as when raising a cup of liquid. This study aims to outline such a wrist control scheme, and evaluate its function in terms of the effect on compensatory movements, objective system performance, and subjective perception of system performance based on user feedback. To that end, twelve able-bodied participants were recruited to control a body-mounted robotic arm using three different control schemes: fixed-wrist (FW), sequential switching (SS), and automatic levelling (AL). The resulting movement strategies were recorded for two different tasks using 3D motion-capture. SS and AL control schemes induced similar movement strategies and less compensation than FW for horizontal movements, while AL reduced shoulder flexion compared to FW and SS for vertical movements. However, AL was ranked less intuitive and less reliable than the FW. AL and SS both seemed to involve more conscious thought to operate than FW. These results suggest that more complex wrist control schemes may indeed be able to eliminate harmful compensatory movements, but reinforce prior observations that control must be reliable and simple to use or people will opt for an easier system.

The use of robotic devices to provide active motor support and sensory feedback of ongoing motor intention, by means of a Brain Computer Interface (BCI), has received growing support by recent literature, with particular focus on neurorehabilitation therapies. At the same time, performance in the use of the BCI has become a more critical factor, since it directly influences congruency and consistency of the provided sensory feedback. As motor imagery is the mental simulation of a given movement without depending on residual function, training of patients in the use of motor imagery BCI can be extended beyond each rehabilitation session, and practiced by using simpler devices than rehabilitation robots available in the hospital. In this work, we investigated the use of haptic stimulation provided by vibrating electromagnetic motors to enhance BCI system training. The BCI is based on motor imagery of hand grasping and designed to operate a hand exoskeleton. We investigated whether haptic stimulation at fingerpads proves to be more effective than stimulation at wrist, already experimented in literature, due to the higher density of mechano-receptors. Our results did not show significant differences between the two body locations in BCI performance, yet a wider and more stable event-relateddesynchronization appeared for the finger-located stimulation. Future investigations will put in relation training with haptic feedback at fingerpads with BCI performance using the handexoskeleton, in grasping tasks that naturally involve haptic feedback at fingerpads.

Objective: This study proposes a novel wearable pulse monitoring system, which can realize the synchronous measurements of pulse wave, skin, temperature, and pulse wave velocity (PWV)., Methods: A flexible sensor based on thermosensation is used to detect pressure and temperature stimuli simultaneously. A total of two sensors are integrated to detect pulse transit along two specific points of the artery, e.g., Cun and Chi at a wrist, the data of which are subsequently used to figure out the PWV by using a tailor-designed algorithm conducted in a microprocessor. Calibration experiments and application cases are conducted to validate the effectiveness of the monitor., Results: The developed monitor detects the physiological signals of pulse wave, PWV, and skin temperature simultaneously. In addition, the monitor can measure the pulse changes before and after exercises and track skin temperature variations when warming and cooling. Moreover, the monitor can be also used to detect the local PWV at the wrist., Conclusion: The synchronous measurements of pulse wave, skin temperature, and PWV using a wearable monitor are feasible., Significance: The monitor is small, simple-structured, with multifunction, and thus provides a promising auxiliary approach for traditional Chinese medicine pulse diagnosis.

With the ability to detect volumetric changes of contracting muscles, ultrasound (US) was a potential technique in the field of human-machine interface. Compared to the US imaging (B-mode US), the signal from a static single-element US transducer, A-mode US, was a more cost-effective and convenient way toward the real-world application, particularly the wearables. This study compared the performance of the single-channel A-mode US with single-channel surface electromyogram (sEMG) signals, one of the most popular signal modalities for wrist and finger gesture recognition. We demonstrated that A-mode US outperformed sEMG in six out of nine gestures recognition, while sEMG was superior to A-mode US on the detection of the rest state. We also demonstrated that, through feature space analysis, the advantage of A-mode US over sEMG for gesture recognition was due to its superior ability in detecting information from deep musculature. This study presented the clear complementary advantages between A-mode US and sEMG, indicating the possibility of fusing two signal modalities for the gesture recognition applications.

Objective: To demonstrate the feasibility of everaging impedance plethysmography (IPG) for detection of pulse transit time (PTT) and estimation of blood pressure (BP)., Methods: We first established the relationship between BP, PTT, and arterial impedance (i.e., the IPG observations). The IPG sensor was placed on the wrist while the photoplethysmography sensor was attached to the index finger to measure the PTT. With a cuff-based BP monitoring system placed on the upper arm as a reference, our proposed methodology was evaluated on 15 young, healthy human subjects leveraging handgrip exercises to manipulate BP/PTT and compared to several conventional PTT models to assess the efficacy of PTT/BP detections., Results: The proposed model correlated with BP fairly well with group average correlation coefficients of [Formula: see text] for systolic BP (SBP) and [Formula: see text] for diastolic BP (DBP). In comparison with the other PTT methods, PTT-IPG-based BP estimation provided a lower root-mean-squared-error of [Formula: see text] and [Formula: see text] for SBP and DBP, respectively., Conclusion: We conclude that the measurement of arterial impedance via IPG methods is an adequate indicator to estimate BP. The proposed method appears to offer superiority compared to the conventional PTT estimation approaches., Significance: Using impedance magnitude to estimate PTT offers promise to realize wearable and cuffless BP devices.

For wrist complaints related to motion, a 2-D radiograph or CT scan of the static wrist may not always be considered diagnostic. 3-D motion imaging, i.e., multiple 3DCT scans in time (4DCT), enables quantifying carpal motion and comparing motion patterns of the affected wrist with those of the healthy contralateral side. The accuracy and precision of the method, however, is limited by noise and motion artifacts. Although, the technique is considered promising in existing literature, the accuracy and precision of carpal motion analysis has never been investigated systematically. In this paper, we introduce and evaluate a semi-automatic segmentation- and registration-based method for 3-D carpal motion analysis. We investigate the accuracy and precision of the method, and its dependency on motion and scan parameters (angular velocity, dose, gantry revolution angle for image reconstruction, and scanner type) using a wrist phantom. During standstill the positioning error was ≤ 0.23 mm and ≤ 0.78°. A partial gantry revolution for 3-D reconstruction introduced image deformation, contributing to a positioning error of approx. 0.8 mm. This error increased with reduced dose, and with increasing angular velocity of the wrist phantom. In cases where the phantom was rotating about an axis parallel to the rotation axis of the gantry, and in a direction opposite to the gantry, the positioning error increased, probably because of the apparent increase in angular velocity with respect to the gantry. Slow carpal motion 4DCT analysis is feasible using a regular CT scanner. A partial gantry revolution angle for 3-D reconstruction may introduce image deformation, which decreases the accuracy of carpal motion analysis. Knowing the positioning error in 4DCT imaging with the proposed method is considered valuable when investigating wrist injury since it enables discrimination of actual motion from apparent motion caused by methodological error.

In this study, we propose a sensory stimulation training (SST) approach to improve the performance of a brain-computer interface (BCI) based on somatosensory attentional orientation (SAO). In this BCI, subjects imagine the tactile sensation and maintain the attention on the corresponding hand as if there was a tactile stimulus on the wrist skin. Twenty BCI naïve subjects were recruited and randomly divided into a Control-Group and an SST-Group. In the Control-Group, subjects performed left hand and right hand SAO tasks in six consecutive runs (with 40 trials in each run), divided into three blocks with each having two runs. For the SST-Group, two runs included real tactile stimulation to the left or right hand (SST training block), between the first two (Pre-SST block) and the last two SAO runs (Post-SST block). Results showed that the SST-Group had a significantly improved performance of 9.4% between the last block and the first block after SST training (F (2,18) = 11.11, p = 0.0007); in contrast, no significant difference was found in the Control-Group between the first, second, and the last block (F (2,18) = 2.07, p = 0.1546), indicating no learning effect. The tactile sensation-induced oscillatory dynamics were similar to those induced by SAO. In the SST-Group, R 2 discriminative information was enhanced around the somatosensory cortex due to the real sensory stimulation as compared with that in the Control-Group. Since the SAO mental task is inherently an internal process, the proposed SST method is meant as an adjuvant to SAO to facilitate subjects in achieving an initial SAO-based BCI control.

Epileptic seizure detection requires specialized approaches such as video/electroencephalography monitoring. However, these approaches are restricted mainly to hospital setting and requires video/EEG analysis by experts, which makes these approaches resource- and labor-intensive. In contrast, we aim to develop a wireless remote monitoring system based on a single wrist-worn accelerometer device, which is sensitive to multiple types of convulsive seizures and is capable of detecting seizures with short duration. Simple time domain features including a new set of Poincar´e plot based features were extracted from the active movement events recorded using a wrist-worn accelerometer device. The best features were then selected using the area under the ROC curve analysis. Kernelized support vector data description (SVDD) was then used to classify non-seizure and seizure events. The proposed algorithm was evaluated on 5;576h of recordings from 79 patients and detected 40 (86:95%) of 46 convulsive seizures (generalized tonic-clonic (GTCS), psychogenic non-epileptic (PNES), and complex partial seizures (CPS)) from twenty patients with a total of 270 false alarms (1:16=24h). Furthermore, the algorithm showed a comparable performance (sensitivity 95:23% and false alarm rate 0:64=24h) with respect to existing unimodal and multi-modal methods for GTCS detection. The promising results shows the potential to build an ambulatory monitoring convulsive seizure detection system. A wearable accelerometer based seizure detection system would aid in continuous assessment of convulsive seizures in a timely and non-invasive manner.

Objective: We propose a novel calibration strategy to facilitate the decoding of covert somatosensory attention by exploring the oscillatory dynamics induced by tactile sensation., Methods: It was hypothesized that the similarity of the oscillatory pattern between stimulation sensation (SS, real sensation) and somatosensory attentional orientation (SAO) provides a way to decode covert somatic attention. Subjects were instructed to sense the tactile stimulation, which was applied to the left (SS-L) or the right (SS-R) wrist. The brain-computer interface (BCI) system was calibrated with the sensation data and then applied for online SAO decoding., Results: Both SS and SAO showed oscillatory activation concentrated on the contralateral somatosensory hemisphere. Offline analysis showed that the proposed calibration method led to a greater accuracy than the traditional calibration method based on SAO only. This is confirmed by online experiments, where the online accuracy on 15 subjects was 78.8 ± 13.1%, with 12 subjects >70% and 4 subject >90%., Conclusion: By integrating the stimulus-induced oscillatory dynamics from sensory cortex, covert somatosensory attention can be reliably decoded by a BCI system calibrated with tactile sensation., Significance: Indeed, real tactile sensation is more consistent during calibration than SAO. This brain-computer interfacing approach may find application for stroke and completely locked-in patients with preserved somatic sensation.

Automatic recognition of activities of daily living (ADL) is an important component in understanding of energy balance, quality of life, and other areas of health and well-being. In our previous work, we had proposed an insole-based activity monitor-SmartStep, designed to be socially acceptable and comfortable. The goals of the current study were: first, validation of SmartStep in recognition of a broad set of ADL; second, comparison of the SmartStep to a wrist sensor and testing these in combination; third, evaluation of SmartStep's accuracy in measuring wear noncompliance and a novel activity class (driving); fourth, performing the validation in free living against a well-studied criterion measure (ActivPAL, PAL Technologies); and fifth, quantitative evaluation of the perceived comfort of SmartStep. The activity classification models were developed from a laboratory study consisting of 13 different activities under controlled conditions. Leave-one-out cross validation showed 89% accuracy for the combined SmartStep and wrist sensor, 81% for the SmartStep alone, and 69% for the wrist sensor alone. When household activities were grouped together as one class, SmartStep performed equally well compared to the combination of SmartStep and wrist-worn sensor (90% versus 94%), whereas the accuracy of the wrist sensor increased marginally (73% from 69%). SmartStep achieved 92% accuracy in recognition of nonwear and 82% in recognition of driving. Participants then were studied for a day under free-living conditions. The overall agreement with ActivPAL was 82.5% (compared to 97% for the laboratory study). The SmartStep scored the best on the perceived comfort reported at the end of the study. These results suggest that insole-based activity sensors may present a compelling alternative or companion to commonly used wrist devices.

In this paper, we perform complex network analysis on a connectivity dataset retrieved from a monitoring system in order to classify simple daily activities. The monitoring system is composed of a set of wearable sensing modules positioned on the subject's body and the connectivity data consists of the correlation between each pair of modules. A number of network measures are then computed followed by the application of statistical significance and feature selection methods. These methods were implemented for the purpose of reducing the total number of modules in the monitoring system required to provide accurate activity classification. The obtained results show that an overall accuracy of 84.6% for activity classification is achieved, using a random forest classifier, and when considering a monitoring system composed of only two modules positioned at the neck and thigh of the subject's body.

Estimating the 3-D pose of instruments is an important part of robotic minimally invasive surgery for automation of basic procedures as well as providing safety features, such as virtual fixtures. Image-based methods of 3-D pose estimation provide a non-invasive low cost solution compared with methods that incorporate external tracking systems. In this paper, we extend our recent work in estimating rigid 3-D pose with silhouette and optical flow-based features to incorporate the articulated degrees-of-freedom (DOFs) of robotic instruments within a gradient-based optimization framework. Validation of the technique is provided with a calibrated ex-vivo study from the da Vinci Research Kit (DVRK) robotic system, where we perform quantitative analysis on the errors each DOF of our tracker. Additionally, we perform several detailed comparisons with recently published techniques that combine visual methods with kinematic data acquired from the joint encoders. Our experiments demonstrate that our method is competitively accurate while relying solely on image data.

This paper proposes the use of posterior-adapted class-based weighted decision fusion to effectively combine multiple accelerometer data for improving physical activity recognition. The cutting-edge performance of this method is benchmarked against model-based weighted fusion and class-based weighted fusion without posterior adaptation, based on two publicly available datasets, namely PAMAP2 and MHEALTH. Experimental results show that: 1) posterior-adapted class-based weighted fusion outperformed model-based and class-based weighted fusion; 2) decision fusion with two accelerometers showed statistically significant improvement in average performance compared to the use of a single accelerometer; 3) generally, decision fusion from three accelerometers did not show further improvement from the best combination of two accelerometers; and 4) a combination of ankle and wrist located accelerometers showed the best overall performance compared to any combination of two or three accelerometers.

Heart rate (HR) monitoring using photoplethysmographic (PPG) signals recorded from wearers' wrist greatly facilitates design of wearable devices and maximizes user experience. However, placing PPG sensors in wrist causes much stronger and complicated motion artifacts (MA) due to loose interface between sensors and skin. Therefore, developing robust HR estimation algorithms for wrist-type PPG signals has significant commercial values. In this paper, we propose a robust HR estimation algorithm for wrist-type PPG signals using multiple reference adaptive noise cancellation (ANC) technique-termed here as "MURAD." The main challenge of using ANC for MA reduction is to devise a qualified reference noise signal (RNS) to the adaptive filter. We propose a novel solution by using four RNSs, namely, the three-axis accelerometer data and the difference signal between the two PPG signals. For each RNS, we get a different version of the cleaned PPG signal. Then, a set of probable HR values is estimated using all of the cleaned PPG signals, and then, the value that is closest to the estimated HR of the previous time window is chosen to be the HR estimate of the current window. Then, some peak verification techniques are employed to ensure accurate HR estimations. The proposed technique gives lower average absolute error compared to state-of-the art methods. So, MURAD method provides a promising solution to the challenge of HR monitoring using PPG in wearable devices during severe MA conditions.

Objective: long-term wearable instantaneous heart rate (IHR) monitoring is essential to enable pervasive heart health and fitness management. In this paper, a novel framework is proposed to robustly estimate the IHR from electrocardiogram (ECG) signals corrupted by large amounts of daily motion artifacts, which are one of the major impediments against the long-term IHR monitoring., Methods: the corrupted ECG signals are first projected to a high-dimensional phase space, where the constructed phase portraits of heartbeats are of many new geometrical properties and are expected to be powerful patterns more immune to the motion artifacts. Afterwards, a multiview dynamic time warping approach is applied on the constructed phase portraits, to effectively capture motion artifacts-induced inconsistencies and reveal heartbeats-related consistencies from corrupted signals. Finally, the phase portraits of heartbeats in the multidimensional phase space can be identified, and then, the IHR estimates are achieved., Results: the proposed framework is evaluated on a wrist-ECG dataset acquired by a semicustomized platform and also a public ECG dataset. With a signal-to-noise ratio as low as -9 dB, the mean absolute error and root mean square error of the estimated IHR are 2.5 beats per minute (BPM) and 7.0 BPM, respectively., Conclusion: these results demonstrate that our framework can effectively identify the heartbeats from ECG signals continuously corrupted by intense and random motion artifacts and estimate the IHR., Significance: the proposed framework greatly outperforms previously reported approaches and is expected to contribute to long-term IHR monitoring., Objective: long-term wearable instantaneous heart rate (IHR) monitoring is essential to enable pervasive heart health and fitness management. In this paper, a novel framework is proposed to robustly estimate the IHR from electrocardiogram (ECG) signals corrupted by large amounts of daily motion artifacts, which are one of the major impediments against the long-term IHR monitoring., Methods: the corrupted ECG signals are first projected to a high-dimensional phase space, where the constructed phase portraits of heartbeats are of many new geometrical properties and are expected to be powerful patterns more immune to the motion artifacts. Afterwards, a multiview dynamic time warping approach is applied on the constructed phase portraits, to effectively capture motion artifacts-induced inconsistencies and reveal heartbeats-related consistencies from corrupted signals. Finally, the phase portraits of heartbeats in the multidimensional phase space can be identified, and then, the IHR estimates are achieved., Results: the proposed framework is evaluated on a wrist-ECG dataset acquired by a semicustomized platform and also a public ECG dataset. With a signal-to-noise ratio as low as -9 dB, the mean absolute error and root mean square error of the estimated IHR are 2.5 beats per minute (BPM) and 7.0 BPM, respectively., Conclusion: these results demonstrate that our framework can effectively identify the heartbeats from ECG signals continuously corrupted by intense and random motion artifacts and estimate the IHR., Significance: the proposed framework greatly outperforms previously reported approaches and is expected to contribute to long-term IHR monitoring.

In recent years, many efforts have been made to promote a healthcare paradigm shift from the traditional reactive hospital-centered healthcare approach towards a proactive, patient-oriented, and self-managed approach that could improve service quality and help reduce costs while contributing to sustainability. Managing and caring for patients with chronic diseases accounts over 75% of healthcare costs in developed countries. One of the most resource demanding diseases is chronic kidney disease (CKD), which often leads to a gradual and irreparable loss of renal function, with up to 12% of the population showing signs of different stages of this disease. Peritoneal dialysis and home haemodialysis are life-saving home-based renal replacement treatments that, compared to conventional in-center hemodialysis, provide similar long-term patient survival, less restrictions of life-style, such as a more flexible diet, and better flexibility in terms of treatment options and locations. Bioimpedance has been largely used clinically for decades in nutrition for assessing body fluid distributions. Moreover, bioimpedance methods are used to assess the overhydratation state of CKD patients, allowing clinicians to estimate the amount of fluid that should be removed by ultrafiltration. In this work, the initial validation of a handheld bioimpedance system for the assessment of body fluid status that could be used to assist the patient in home-based CKD treatments is presented. The body fluid monitoring system comprises a custom-made handheld tetrapolar bioimpedance spectrometer and a textile-based electrode garment for total body fluid assessment. The system performance was evaluated against the same measurements acquired using a commercial bioimpedance spectrometer for medical use on several voluntary subjects. The analysis of the measurement results and the comparison of the fluid estimations indicated that both devices are equivalent from a measurement performance perspective, allowing for its use on ubiquitous e-healthcare dialysis solutions.

This study examined wrist proprioception in a cross-sectional sample of 44 children aged between 8-to 14-years and a control group of 10 neurologically and physically healthy adults. Using a 3-degrees of freedom robotic device, participants performed an ipsilateral joint position matching task in which target amplitude (40% or 80% functional range of motion [fRoM]) and degrees-of-freedom (Flexion/Extension [FE], Radial/Ulnar deviation [RUD], Pronation/Supination [PS]) were manipulated. Results indicated that proprioceptive function became more accurate and consistent over the developmental spectrum, but that the ability to utilize proprioceptive feedback did not reach adult levels till the age of 10-11 years. Furthermore, proprioceptive acuity was influenced by target amplitude, such that movements were more accurate for the 80% fROM compared to the 40% fROM target for both the RUD and PS DoFs, independently of age. The present results provide critical information about the typical development of wrist proprioception that will enable clinicians to chart the course of development and dysfunction in neurological disorders in children, and help establish protocols for the robotic diagnosis and assessment of neurodevelopmental disorders.

The main goal of this study is to analyse the effects of combined transcranial direct current stimulation (tDCS) and wrist robot-assisted therapy in subacute stroke patients. Twenty-four patients were included in this study and randomly assigned to the experimental (EG) or control group (CG). All participants performed wrist robot-assisted training a) in conjunction with tDCS (real stimulation for patients in EG) or b) without tDCS (sham stimulation for patients in CG). Clinical scales and kinematic parameters recorded by the robot were used for the assessment. Clinical outcome measures show a significant decrease in motor impairment after the treatment in both groups. Kinematic data show several significant improvements after the integrated therapy in both groups. However, no significant differences in both clinical outcome measures and kinematic parameters was found between two groups. The potential advantages of combined tDCS and wrist robot-assisted therapy in subacute stroke patients are still unclear.

With continuous advancements on active prosthetics the detection of the user's intention becomes the new technological bottleneck. While electromyography (EMG) is widely used to detect individual muscular contributions, sweat and relative sensor movements degrade the quality of the signal over time. In this paper, we bypass the problems created with the skin contact analyzing the muscular activation with Archimedean Spiral (AS) electrodes. We compare traditional EMG electrodes with AS electrodes, stacked up in textile embroidered layers to improve their functionality, and eventually adding a layer of cloth/silicon between the electrodes and the human skin to ascertain the feasibility of the method. We use n=9 volunteers to perform a loaded wrist extension and record signals from the extensor digitorum muscle group. We evaluate the amplitude and noise from all results and conclude that the AS electrode is capable of detecting muscular activation without touching the skin. As part of a low-cost prosthetic initiative, this EMG alternative can be potentially embedded to the prosthetic socket to improve usage and reduce adaptation problems.

Myocontrol, that is control of prostheses using bodily signals, has proved in the decades to be a surprisingly hard problem for the scientific community of assistive and rehabilitation robotics. In particular, traditional surface electromyography (sEMG) seems to be no longer enough to guarantee dexterity (i.e., control over several degrees of freedom) and, most importantly, reliability. Multi-modal myocontrol is concerned with the idea of using novel signal gathering techniques as a replacement of, or alongside, sEMG, to provide high-density and diverse signals to improve dexterity and make the control more reliable. In this paper we present an offline and online assessment of multi-modal sEMG and force myography (FMG) targeted at hand and wrist myocontrol. A total number of twenty sEMG and FMG sensors were used simultaneously, in several combined configurations, to predict opening/closing of the hand and activation of two degrees of freedom of the wrist of ten intact subjects. The analysis was targeted at determining the optimal sensor combination and control parameters; the experimental results indicate that sEMG sensors alone perform worst, yielding a nRMSE of 9.1%, while mixing FMG and sEMG or using FMG only reduces the nRMSE to 5.2-6.6%. To validate these results, we engaged the subject with median performance in an online goal-reaching task. Analysis of this further experiment reveals that the online behaviour is similar to the offline one.

Tremor is the most common movement deficit and manifests in a variety of disorders, including Essential Tremor, Parkinson's Disease, Dystonia, and Cerebellar Ataxia. Although medication and surgical interventions have significantly reduced patient suffering, they are only partially effective and can carry undesired side effects, leaving many patients without satisfactory treatment options. Wearable tremor-suppressing devices could provide an alternative to medication and surgery. Multiple research groups have developed orthotic prototypes to low-pass filter tremor, but these devices have not yet been optimized for in-vivo use. Optimizing non-invasive tremor suppression requires an understanding of where the tremor originates mechanically (which muscles) and how it propagates to the hand (where it matters most). Here we present on the beginnings of our multi-pronged work to determine the origin, propagation, and distribution of Essential Tremor, and we provide preliminary results.

Modulation of stiffness provides a great deal of advantage in the way humans interact with the environment, and is very important in successfully performing activities of daily living. In the context of human-machine interactions, stiffness control could provide a safer interaction, especially when dealing with unpredictable environment. In this paper we propose a user-modulated stiffness and position control for the wrist flexion/extension degree of freedom while physically coupled to a haptic device. A virtual position tracking experiment in a varying external force field is designed in order to test the performance of the control strategy with and without co-contraction techniques. Tracking accuracy and smoothness of motion indicate better performance when subjects use co-contraction techniques, and the difference in the two types of experiment is also statistically significant.

Chronic wrist impairment is frequent following stroke and negatively impacts everyday life. Rehabilitation of the dysfunctional limb is possible but requires extensive training and motivation. Wearable training devices might offer new opportunities for rehabilitation. However, few devices are available to train wrist extension even though this movement is highly relevant for many upper limb activities of daily living. As a proof of concept, we developed the eWrist, a wearable one degree-of-freedom powered exoskeleton which supports wrist extension training. Conceptually one might think of an electric bike which provides mechanical support only when the rider moves the pedals, i.e. it enhances motor activity but does not replace it. Stroke patients may not have the ability to produce overt movements, but they might still be able to produce weak muscle activation that can be measured via surface electromyography (sEMG). By combining force and sEMG-based control in an assist-as-needed support strategy, we aim at providing a training device which enhances activity of the wrist extensor muscles in the context of daily life activities, thereby, driving cortical reorganization and recovery. Preliminary results show that the integration of sEMG signals in the control strategy allow for adjustable assistance with respect to a proxy measurement of corticomotor drive.

Proprioceptive signals from cutaneous, joint, tendon and muscle receptors create the basis for bodily perception and are known to be essential for motor control. However, which are the mechanisms underlying the proprioceptive signals and which are the variables that affect them is still a matter of debate. In particular, what is worth to investigate is, namely, the codification of proprioceptive information related to pointing movements of the wrist towards kinesthetic targets. In this work we asked 10 healthy adults to perform with their wrist a robot-aided proprioceptive matching task, in which the starting position of the matching movements was shifted forward or backward, in order to ascertain to which extent such shifts cause target over/under estimation and how important is movement's length on task performance. Results indicate that accuracy and precision of performance are highly correlated with the starting position and targets tended to be undershot when the active matching movements were longer. Moreover, further analysis revealed a consistent decrement of movement speed for shorter movements and conversely, faster displacement in case of backward of starting position.

State of art upper limb prostheses lack several degrees of freedom (DoF) and force the individuals to compensate for them by changing the motions of their arms and body. Such movements often yield to articulation injuries, nonetheless these could be prevented by adding DoFs, for instance, an articulated passive wrist. Available stiff or compliant wrists with passive flexion/extension and/or radial/ulnar deviation are sub-optimal solutions. Indeed, stiff wrists induce the individuals wearing them to perform exaggerated compensatory movements during the reaching phase while compliant wrists proved to be unpractical while manipulating heavy objects. Here we present a wrist capable of combining the benefits of stiff and compliant wrists. It is provided with two switchable levels of passive compliance that are automatically selected. The prototype was integrated in a body-powered hydraulic hand prosthesis and actuated using the same hydraulic circuit of the hand. Detailed analysis of the parameters that affect the compliance, the critical load and the performance of the prosthesis are presented.

Robotic devices have been clinically verified for use in long duration and high intensity rehabilitation needed for motor recovery after neurological injury. Targeted and coordinated hand and wrist therapy, often overlooked in rehabilitation robotics, is required to regain the ability to perform activities of daily living. To this end, a new coupled hand-wrist exoskeleton has been designed. This paper details the design of the wrist module and several human-related considerations made to maximize its potential as a coordinated hand-wrist device. The serial wrist mechanism has been engineered to facilitate donning and doffing for impaired subjects and to insure compatibility with the hand module in virtual and assisted grasping tasks. Several other practical requirements have also been addressed, including device ergonomics, clinician-friendliness, and ambidextrous reconfigurability. The wrist module's capabilities as a rehabilitation device are quantified experimentally in terms of functional workspace and dynamic properties. Specifically, the device possesses favorable performance in terms of range of motion, torque output, friction, and closed-loop position bandwidth when compared with existing devices. The presented wrist module's performance and operational considerations support its use in a wide range of future clinical investigations.

Executing coordinated movements requires that motor and sensory systems cooperate to achieve a motor goal. Impairment of either system may lead to unstable and/or inaccurate movements. In rehabilitation training, however, most approaches have focused on the motor aspects of the control loop. We are examining mechanisms that may enhance the sensory system to improve motor control. More precisely, the effects of stochastic subliminal vibratory tactile stimulation on wrist proprioception. We developed a device - based on a novel soft pneumatic actuator skin technology - to stimulate multiple sites simultaneously and independently. This device applies vibratory stimulation (amplitude < 0.50 mm, bandwidth 20-120 Hz) to the skin overlaying the tendons of a joint to target the receptors in charge of position and movement encoding. It achieves high spatial resolution (< 1 mm 2 ), uses a soft and flexible interface, and has the potential to be used in combination with additional rehabilitation interventions. We conducted a feasibility study with 16 healthy subjects (11 younger - 6 females; 5 older - 2 females) in which a robotic manipulandum moved the subject's wrist to defined positions that had to be matched with a gauge. Comparing trials with and without stimulation we found that stochastic stimulation influenced joint position sense. The device we developed can be readily used in psycho-physical experiments, and subsequently benefit physiotherapy and rehabilitation treatments.

Reduction of the number of sensors needed to evaluate arm movements, makes a system for the assessment of human body movements more suitable for clinical practice and daily life assessments. In this study, we propose an algorithm to reconstruct lower arm orientation, velocity and position, based on a sensing system which consists of only one inertial measurement unit (IMU) to the forearm. Lower arm movements were reconstructed using a single IMU and assuming that within a measurement there are moments without arm movements. The proposed algorithm, together with a single IMU attached to the forearm, may be used to evaluate lower arm movements during clinical assessments or functional tasks. In this pilot study, reconstructed quantities were compared with an optical reference system. The limits of agreement in the magnitude of the orientation vector and the norm of the velocity vectors are respectively 4.2 deg (normalized, 5.2 percent) and 7.1 cm/s (normalized, 5.8 percent). The limit of agreement of the difference between the reconstructed positions of both sensing systems were relatively greater 7.7 cm (normalized, 16.8 percent).