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Recently, VR-based training applications have become popular and promising, as they can simulate real-world situations in a safe, repeatable, and cost-effective way. For immersive simulations, various input devices have been designed and proposed to increase the effectiveness of training. In this study, we developed a novel device that generates 3D hand motion data and provides haptic force feedback for VR interactions. The proposed device can track 3D hand positions using a combination of the global position estimation of ultrasonic sensors and the hand pose estimation of inertial sensors in real time. For haptic feedback, shape–memory alloy (SMA) actuators were designed to provide kinesthetic forces and an efficient power control without an overheat problem. Our device improves upon the shortcomings of existing commercial devices in tracking and haptic capabilities such that it can track global 3D positions and estimate hand poses in a VR space without using an external suit or tracker. For better flexibility in handling and feeling physical objects compared to exoskeleton-based devices, we introduced an SMA-based actuator to control haptic forces. Overall, our device was designed and implemented as a lighter and less bulky glove which provides comparable accuracy and performance in generating 3D hand motion data for a VR training application (i.e., the use of a fire extinguisher), as demonstrated in the experimental results. [ABSTRACT FROM AUTHOR]

This work investigates a new sensing technology for use in robotic human–machine interface (HMI) applications. The proposed method uses near E-field sensing to measure small changes in the limb surface topography due to muscle actuation over time. The sensors introduced in this work provide a non-contact, low-computational-cost, and low-noise method for sensing muscle activity. By evaluating the key sensor characteristics, such as accuracy, hysteresis, and resolution, the performance of this sensor is validated. Then, to understand the potential performance in intention detection, the unmodified digital output of the sensor is analysed against movements of the hand and fingers. This is done to demonstrate the worst-case scenario and to show that the sensor provides highly targeted and relevant data on muscle activation before any further processing. Finally, a convolutional neural network is used to perform joint angle prediction over nine degrees of freedom, achieving high-level regression performance with an RMSE value of less than six degrees for thumb and wrist movements and 11 degrees for finger movements. This work demonstrates the promising performance of this novel approach to sensing for use in human–machine interfaces. [ABSTRACT FROM AUTHOR]

In this study, we introduce a novel framework for practicing and analyzing arm muscles in motions, such as juggling actions, by estimating the hand motion of the user using a Leap Motion device. The proposed method can map the movement of a ball in a virtual world to the hand motion of the user in real time and visualize the relaxation and contraction of muscles to determine the amount of exercise performed. Our procedure has five sections: 1) The Leap Motion device tracks the hand position of the user. 2) A behavioral pattern in which a user throws a ball is defined as an event. 3) The hand motion is mapped to the ball based on the hand position of the user using the proposed parabola-based particle approach. 4) The quantity of muscle activity is visualized and analyzed in relation to the degree of arm bending. 5) Finally, we propose a method that utilizes the symmetry data-driven approach to extend solvers, enabling the efficient handling of avatar juggling motions in a virtual environment, based on user actions. Moreover, this method enhances the results by allowing diverse control over the virtual ball’s trajectory to match the user’s pose. Consequently, the proposed system enables real-time juggling in a virtual environment, as well as practice and analysis of the arm muscle activity of the user. The outcomes of the analyses are expected to be applied in various industries, including healthcare. In the solver extensions, we do not utilize all the hand position information of the user. Instead, we base the trajectory of one hand on the data of the other hand, synthesizing it through a data-driven approach. This results in a relatively lightweight algorithm that generates the avatar’s juggling motion. Additionally, by leveraging the user’s pose and parabolic motion, we can arbitrarily synthesize the trajectory of the virtual ball, facilitating the easy creation of a variety of scenes.

Recently, VR-based training applications have become popular and promising, as they can simulate real-world situations in a safe, repeatable, and cost-effective way. For immersive simulations, various input devices have been designed and proposed to increase the effectiveness of training. In this study, we developed a novel device that generates 3D hand motion data and provides haptic force feedback for VR interactions. The proposed device can track 3D hand positions using a combination of the global position estimation of ultrasonic sensors and the hand pose estimation of inertial sensors in real time. For haptic feedback, shape–memory alloy (SMA) actuators were designed to provide kinesthetic forces and an efficient power control without an overheat problem. Our device improves upon the shortcomings of existing commercial devices in tracking and haptic capabilities such that it can track global 3D positions and estimate hand poses in a VR space without using an external suit or tracker. For better flexibility in handling and feeling physical objects compared to exoskeleton-based devices, we introduced an SMA-based actuator to control haptic forces. Overall, our device was designed and implemented as a lighter and less bulky glove which provides comparable accuracy and performance in generating 3D hand motion data for a VR training application (i.e., the use of a fire extinguisher), as demonstrated in the experimental results.

In recent years, machining learning aided diagnosis can provide non-invasive, low-cost tools to support clinicians and assist the diagnosis and monitoring of neurodegenerative disorders, in particular Parkinson's disease (PD). As an important motor symptom, disorder of the hand motion is usually used for diagnosis and evaluation of PD; moreover, majority of the patients with PD have handwriting abnormalities, which plays a special role in PD detection. In this paper, as an useful tool, we propose a novel hybrid model to learn the handwriting differences between PD patients and healthy controls, by learning and enhancing significant features from three handwriting exams, i.e., meander, circle and spiral. Based on a three-layer convolutional neural network (CNN) and a bidirectional gated recurrent unit (BiGRU), the proposed network can assess the potential of sequential information of handwriting in identifying Parkinsonian symptoms. Compared with several state of the art studies, the recognition rates of our proposed framework are 92.91%, 85.71% and 90.55% respectively in these three tests, which verifies the excellent classification effect. [ABSTRACT FROM AUTHOR]

For elders and PWDs, it is sometimes arduous for them to reach the switch of the light and turn it on. Hence, innovating new and unique light bulbs might help them and it can be favorable for students, children, and the elderly. This study aimed to create a 2-in-1 motion and sound-powered desk lamp in terms of detecting different hand motions and sound sources. The device was assembled by programming the code of the Arduino. Different wires served their purpose and were then connected to the Arduino. Quantitative observation was utilized in this study in which a series of testing was conducted to determine the sound and motion sensitivity of the 2-in-1 motion and sound-powered desk lamp. The results showed that the motion and sound sensitivity were both rated excellent in turning the device on by 5 different hand motions (back and forth vertical and horizontal handwave motion, raising the front and back parts of the hand, pushing the palm towards the sensor, and peace sign hand gesture) and 3 specified sound sources (handclap, knocking, and tapping an object) The study concluded that the 2-in-1 motion and sound Powered desk lamp has a high sound and motion sensitivity which enables it to emit light automatically. [ABSTRACT FROM AUTHOR]

Natural object manipulation is one of the important human skills. However, generating natural hand manipulation motions that are adaptive to object shapes and the tasks at hand in virtual reality is still a challenge. In this paper, we propose a neural network-based finger movement generation approach, enabling the generation of plausible hand motions interacting with objects. Given the object shape and movement features in the wrist coordinate system, the first network Generator infers the hand pose at the current frame that matches the object motion and shape. The second network Optimizer then fine-tunes the pose to improve the plausibility of hand-object interaction. Notably, a differentiable optimization module is proposed to generate the training dataset for Optimizer. Experimental results show that our approach can generate plausible dexterous hand manipulation motions for hand-object interaction without obvious delay. [ABSTRACT FROM AUTHOR]

This work investigates a new sensing technology for use in robotic human–machine interface (HMI) applications. The proposed method uses near E-field sensing to measure small changes in the limb surface topography due to muscle actuation over time. The sensors introduced in this work provide a non-contact, low-computational-cost, and low-noise method for sensing muscle activity. By evaluating the key sensor characteristics, such as accuracy, hysteresis, and resolution, the performance of this sensor is validated. Then, to understand the potential performance in intention detection, the unmodified digital output of the sensor is analysed against movements of the hand and fingers. This is done to demonstrate the worst-case scenario and to show that the sensor provides highly targeted and relevant data on muscle activation before any further processing. Finally, a convolutional neural network is used to perform joint angle prediction over nine degrees of freedom, achieving high-level regression performance with an RMSE value of less than six degrees for thumb and wrist movements and 11 degrees for finger movements. This work demonstrates the promising performance of this novel approach to sensing for use in human–machine interfaces.

The Action Research Arm Test (ARAT) presents a ceiling effect that prevents the detection of improvements produced with rehabilitation treatments in stroke patients with mild finger joint impairments. The aim of this study was to develop classification models to predict whether activities with similar ARAT scores were performed by a healthy subject or by a subject post-stroke using the extension and flexion angles of 11 finger joints as features. For this purpose, we used three algorithms: Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN). The dataset presented class imbalance, and the classification models presented a low recall, especially in the stroke class. Therefore, we implemented class balance using Borderline-SMOTE. After data balancing the classification models showed significantly higher accuracy, recall, f1-score, and AUC. However, after data balancing, the SVM classifier showed a higher performance with a precision of 98%, a recall of 97.5%, and an AUC of 0.996. The results showed that classification models based on human hand motion features in combination with the oversampling algorithm Borderline-SMOTE achieve higher performance. Furthermore, our study suggests that there are differences in ARAT activities performed between healthy and post-stroke individuals that are not detected by the ARAT scoring process. [ABSTRACT FROM AUTHOR]

Nowadays, with the advancement of the Internet and personal mobile devices, many interactive media are prevailing, where viewers make their own decisions on the story of the media based on their interactions. The interaction that the user can make is usually pre-programmed by a programmer. Therefore, interactions that users can make are limited to programmable areas. In comparison, in this paper, we propose an Interactive media authoring tool which can compose diverse two-hand interactions from several one-hand interactive components. The aim is to provide content creators with a tool to produce multiple hand motions so that they can design a variety of user interactions to stimulate the interest of content viewers and increase their sense of immersion. Using the proposed system, the content creator can gain greater freedom to create more diverse and complex interactions than programmable ones. The system is composed of a complex motion editor that edits one-hand motions into complex two-hand motions, a touchless sensor that senses the hand motion and a metadata manager that handles the metadata, which specify the settings for the interactive functions. To our knowledge, the proposed system is the first web-based authoring tool that can authorize complex two-hand motions from single hand motions, and which can also control a touchless motion control device. [ABSTRACT FROM AUTHOR]

• A squeeze-and-excite multivariate long short-term memory fully convolutional network is proposed to extract features from hand motion analysis. • A random forest achieves accurate assessment of clinical information. • The combination deep learning on hand motion and clinical information significantly improves the accuracy of cervical spondylotic myelopathy severity assessment. Hand motor function is found to be associated with cervical spondylotic myelopathy (CSM). This study aimed to explore a deep learning-based hand motion classification to quantify the severity. A hand dysfunction assessment method based on hand motion data and clinical information variables was proposed. A squeeze-and-excite multivariate long short-term memory fully convolutional network (SE-MLSTM-FCN) was developed to extract feature map from hand motion data. Feature map was then combined with clinical information variables to determine the severity by various classifiers. To evaluate the method, kinematic data of the left and right hands during rapid grip and release and basic clinical information were collected from 186 CSM patients with different severity levels. The results show that when only hand motion data was used for severity assessment, SE-MLSTM-FCN achieved an accuracy of 79.97 %, an F1 score of 0.7937 and a Kappa score of 0.6837, outperforming the baseline models. Moreover, it had a 15 % lower computational complexity, indicating that the proposed feature extraction model is superior in both performance and efficiency. By combining clinical information variables with the feature map from SE-MLSTM-FCN, random forest (RF) achieved the best classification result, with an accuracy of 83.06 %, an F1 score of 0.8282, a Kappa score of 0.7332 for three-class severity assessment. Therefore, the deep learning approach based on hand motion data and clinical information is promising for assessing the severity of hand dysfunction in CSM, which would be of great value in monitoring CSM progression and evaluating treatment effect. [ABSTRACT FROM AUTHOR]

Designing virtual characters that are capable of conveying a sense of personality is important for generating realistic experiences, and thus a key goal in computer animation research. Though the influence of gesture and body motion on personality perception has been studied, little is known about which attributes of hand pose and motion convey particular personality traits. Using the "Big Five" model as a framework for evaluating personality traits, this work examines how variations in hand pose and motion impact the perception of a character's personality. As has been done with facial motion, we first study hand motion in isolation as a requirement for running controlled experiments that avoid the combinatorial explosion of multimodal communication (all combinations of facial expressions, arm movements, body movements, and hands) and allow us to understand the communicative content of hands. We determined a set of features likely to reflect personality, based on research in psychology and previous human motion perception work: shape, direction, amplitude, speed, and manipulation. Then we captured realistic hand motion varying these attributes and conducted three perceptual experiments to determine the contribution of these attributes to the character's personalities. Both hand poses and the amplitude of hand motion affected the perception of all five personality traits. Speed impacted all traits except openness. Direction impacted extraversion and openness. Manipulation was perceived as an indicator of introversion, disagreeableness, neuroticism, and less openness to experience. From these results, we generalize guidelines for designing detailed hand motion that can add to the expressiveness and personality of characters. We performed an evaluation study that combined hand motion with gesture and body motion. Even in the presence of body motion, hand motion still significantly impacted the perception of a character's personality and could even be the dominant factor in certain situations.

Although basal ganglia (BG) are involved in the motor disorders of aged people, the effect of aging on the functional interaction of BG is not well-known. This work was aimed at studying the influence of aging on the functional connectivity of the motor circuit of BG (BGmC). Thirty healthy volunteers were studied (young-group 26.4 ± 5.7 years old; aged-group 63.1 ± 5.8 years old) with a procedure planned to prevent the spurious functional connectivity induced by the closed-loop arrangement of the BGmC. BG showed different functional interactions during the inter-task intervals and when subjects did not perform any voluntary task. Aging induced marked changes in the functional connectivity of the BGmC during these inter-task intervals. The finger movements changed the functional connectivity of the BG, these modifications were also different in the aged-group. Taken together, these data show a marked effect of aging on the functional connectivity of the BGmC, and these effects may be at the basis of the motor handicaps of aged people during the execution of motor-tasks and when they are not performing any voluntary motor task. [ABSTRACT FROM AUTHOR]

Although basal ganglia (BG) are involved in the motor disorders of aged people, the effect of aging on the functional interaction of BG is not well-known. This work was aimed at studying the influence of aging on the functional connectivity of the motor circuit of BG (BGmC). Thirty healthy volunteers were studied (young-group 26.4 ± 5.7 years old; aged-group 63.1 ± 5.8 years old) with a procedure planned to prevent the spurious functional connectivity induced by the closed-loop arrangement of the BGmC. BG showed different functional interactions during the inter-task intervals and when subjects did not perform any voluntary task. Aging induced marked changes in the functional connectivity of the BGmC during these inter-task intervals. The finger movements changed the functional connectivity of the BG, these modifications were also different in the aged-group. Taken together, these data show a marked effect of aging on the functional connectivity of the BGmC, and these effects may be at the basis of the motor handicaps of aged people during the execution of motor-tasks and when they are not performing any voluntary motor task.

Objective: We investigated the impact of low-tempo, repetitive hand movements on vibrotactile sensitivity by employing various temporal and spatial patterns in the hand and wrist area., Background: The investigation of a human's ability to perceive vibrotactile stimuli during dynamic hand movements remains understudied, despite the prevalence of slow to mild hand motions in applications such as hand navigation or gesture control using haptic gloves in Virtual Reality (VR) and Augmented Reality (AR)., Method: We investigated vibrotactile sensitivity, analyzing the impact of various factors, including Motion (static and low-tempo repetitive hand movements), Temporal Patterns (Single or Double vibrations with varying onset times), Tactor Placements (hand and wrist), Spatial Patterns, and Biological Sex ., Results: Our study revealed that Motion significantly influences vibrotactile sensitivity in the hand and wrist areas, leading to reduced accuracy rates during dynamic conditions. Additionally, as the stimulus onset approached in Double vibrations, accuracy rates markedly decreased. Notably, Hand Placement resulted in significantly higher accuracy rates compared to the Wrist Placement ., Conclusion: Our findings underscore the impact of motion in reducing vibrotactile sensitivity on the back of the hand and around the wrist., Application: This research has wide-ranging practical applications, particularly in the field of VR/AR experiences, rehabilitation programs, and accessibility solutions through the use of haptic gloves. Insights from our study can be harnessed to enhance the efficacy of haptic gloves in conveying vibrotactile cues within these contexts., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

The Action Research Arm Test (ARAT) presents a ceiling effect that prevents the detection of improvements produced with rehabilitation treatments in stroke patients with mild finger joint impairments. The aim of this study was to develop classification models to predict whether activities with similar ARAT scores were performed by a healthy subject or by a subject post-stroke using the extension and flexion angles of 11 finger joints as features. For this purpose, we used three algorithms: Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN). The dataset presented class imbalance, and the classification models presented a low recall, especially in the stroke class. Therefore, we implemented class balance using Borderline-SMOTE. After data balancing the classification models showed significantly higher accuracy, recall, f1-score, and AUC. However, after data balancing, the SVM classifier showed a higher performance with a precision of 98%, a recall of 97.5%, and an AUC of 0.996. The results showed that classification models based on human hand motion features in combination with the oversampling algorithm Borderline-SMOTE achieve higher performance. Furthermore, our study suggests that there are differences in ARAT activities performed between healthy and post-stroke individuals that are not detected by the ARAT scoring process.

Natural hand manipulations exhibit complex finger maneuvers adaptive to object shapes and the tasks at hand. Learning dexterous manipulation from data in a brute force way would require a prohibitive amount of examples to effectively cover the combinatorial space of 3D shapes and activities. In this paper, we propose a hand-object spatial representation that can achieve generalization from limited data. Our representation combines the global object shape as voxel occupancies with local geometric details as samples of closest distances. This representation is used by a neural network to regress finger motions from input trajectories of wrists and objects. Specifically, we provide the network with the current finger pose, past and future trajectories, and the spatial representations extracted from these trajectories. The network then predicts a new finger pose for the next frame as an autoregressive model. With a carefully chosen hand-centric coordinate system, we can handle single-handed and two-handed motions in a unified framework. Learning from a small number of primitive shapes and kitchenware objects, the network is able to synthesize a variety of finger gaits for grasping, in-hand manipulation, and bimanual object handling on a rich set of novel shapes and functional tasks. We also demonstrate a live demo of manipulating virtual objects in real-time using a simple physical prop. Our system is useful for offline animation or real-time applications forgiving to a small delay. [ABSTRACT FROM AUTHOR]

The learning of inter-day representation of electromyographic (EMG) signals across multiple days remains a challenging topic and not fully accommodated yet. This study aims to improve the inter-day hand motion classification accuracy via convolutional neural network (CNN)-based data feature fusion. An EMG database (ISRMyo-I) was recorded from six subjects on 10 days via a low density electrode setting. This study investigated CNNs' capability of feature learning, and found that the output of the first fully connected layer (CNNFeats) was a decent supplement feature set to the most prevalent Hudgins' time domain features in combination with fourth-order autoregressive coefficients (TDAR). Through adding the automatically learned CNNFeats to the handcrafted TDAR feature set, both linear discriminant analysis (LDA) and support vector machine (SVM) classifiers received > 3% accuracy improvement. Similarly, taking TDAR as additional input to the CNN improved the accuracy by > 1% in the comparison with the basic CNN. Our results also demonstrated that the CNN approach outperformed conventional approaches when multiple subjects' data were available for training, while traditional approaches were more adept at presenting motion patterns for single subject. A preliminary conclusion is drawn that substantial "common knowledge/features" can be learned by CNNs from the raw EMG signals across multiple days and multiple subjects, and thus it is believed that a pre-trained CNN model would contribute to higher accuracy as well as the reduction of learning burden. [ABSTRACT FROM AUTHOR]

Based on HSV gamut space, a visualization system of muscle activity is proposed to study the mapping relationship between hand motion and active areas of upper arm muscle. There is a significant threshold change in the starting and ending points of the active segment in the original EMG signal, and the part that exceeds the threshold TH is the active segment date. Set the window width K and fixed increment Kt of time window to remove redundant data. The sEMG intensity information of each sampling electrode is obtained by calculating MAV in each window, and the simulation experiment is conducted in HSV gamut space. Through the human-computer interaction experiment of the visual system, it is proved that this system can visually display the relationship between different channels in the spatial domain, thus intuitively identify the activity intensity of different muscles in hand motion. [ABSTRACT FROM AUTHOR]

Although a large number of surface electromyography (sEMG) features have been proposed to improve hand gesture recognition accuracy, it is still hard to achieve acceptable performance in inter-session and inter-subject tests. To promote the application of sEMG-based human machine interaction, a convolutional neural network based feature extraction approach (CNNFeat) is proposed to improve hand gesture recognition accuracy. A sEMG database is recorded from eight subjects while performing ten hand gestures. Three classic classifiers, including linear discriminant analysis (LDA), support vector machine (SVM) and K nearest neighbor (KNN), are employed to compare the CNNFeat with 25 traditional features. This work concentrates on the analysis of CNNFeat through accuracy, safety index and repeatability index. The experimental results show that CNNFeat outperforms all the tested traditional features in inter-subject test and is listed as the best three features in inter-session test. Besides, it is also found that combining CNNFeat with traditional features can further improve the accuracy by 4.35%, 3.62% and 4.7% for SVM, LDA and KNN, respectively. Additionally, this work also demonstrates that CNNFeat can be potentially enhanced with more data for model training. [ABSTRACT FROM AUTHOR]

Voluntary participation of hemiplegic patients is crucial for functional electrical stimulation therapy. A wearable functional electrical stimulation system has been proposed for real-time volitional hand motor function control using the electromyography bridge method. Through a series of novel design concepts, including the integration of a detecting circuit and an analog-to-digital converter, a miniaturized functional electrical stimulation circuit technique, a low-power super-regeneration chip for wireless receiving, and two wearable armbands, a prototype system has been established with reduced size, power, and overall cost. Based on wrist joint torque reproduction and classification experiments performed on six healthy subjects, the optimized surface electromyography thresholds and trained logistic regression classifier parameters were statistically chosen to establish wrist and hand motion control with high accuracy. Test results showed that wrist flexion/extension, hand grasp, and finger extension could be reproduced with high accuracy and low latency. This system can build a bridge of information transmission between healthy limbs and paralyzed limbs, effectively improve voluntary participation of hemiplegic patients, and elevate efficiency of rehabilitation training.

Electromyography (EMG) has been widely accepted to interact with prosthetic hands, but still limited to using few channels for the control of few degrees of freedom. The use of more channels can improve the controllability, but it also increases system's complexity and reduces its wearability. It is yet clear if optimizely placing the EMG channel could provide a feasible solution to this challenge. This study customized a genetic algorithm to optimize the number of channels and its position on the forearm in inter-day hand gesture recognition scenario. Our experimental results demonstrate that optimally selected 14 channels out of 16 can reach a peak inter-day hand gesture recognition accuracy at 72.3%, and optimally selecting 9 and 11 channels would reduce the performance by 3% and 10%. The cross-validation results also demonstrate that the optimally selected EMG channels from five subjects also work on the rest of the subjects, improving the accuracies by 3.09% and 4.5% in 9- and 11-channel combination, respectively. In sum, this study demonstrates the feasibility of channel reduction through genetic algorithm, and preliminary proves the significance of EMG channel optimization for human–machine interaction. [ABSTRACT FROM AUTHOR]

Current basal ganglia models integrate information obtained from humans and animals to explain motor disorders in Parkinson's disease. These models explain some motor disturbances of Parkinson's disease (PD), but different clinical observations which remain unexplained have promoted the development of new basal ganglia (BG) models. The present study uses the time-relationship (partial correlation) of the BOLD-signal fluctuations to study the influence of PD on BG interactions of 17 age-matched controls (58.7 ± 5.3 years of age) and 24 PD patients (56.7 ± 8.4 years of age). Controls showed a complex functional connectivity of BG with a positive correlation between some nuclei (synchrony) and a negative correlation between other nuclei (anti-synchrony). This functional connectivity was different in PD-patients who showed: 1. an increased synchrony between the primary motor cortex(M1)-external pallidum(GPe), putamen(Put)-GPe, Put-subthalamic nucleus (STN), STN-internal pallidum (GPi), STN-motor thalamus (Tal), STN-GPi substantia nigra (SN) and SN-Tal, 2. a decreased synchrony between Put-GPi, GPe-STN, GPe-SN, STN-SN and GPi-SN, and 3. an increased anti-synchrony between GPe-SN and GPi-Tal. In control subjects, the motor-task increased the Put-Tal, GPi-SN and STN-Tal synchrony, decreased the STN-GPi and STN-SN synchrony and decreased the M1-GPe and the GPe-GPi anti-synchrony. The effect of the motor-task was very different in PD-patients, in whom it induced a decrease of the M1-GPe, STN-GPi and SN-Tal synchrony and a decrease of the GPe-Tal and GPe-SN anti-synchrony. Functional connectivity imaging methods may provide data that cannot be obtained by other methods in humans, and that may help to understand the physiology of BG and its deterioration in PD. [ABSTRACT FROM AUTHOR]

Desktop action recognition from first-person view (egocentric) video is an important task due to its omnipresence in our daily life, and the ideal first-person viewing perspective for observing hand-object interactions. However, no previous research efforts have been dedicated on the benchmark of the task. In this paper, we first release a dataset of daily desktop actions recorded with a wearable camera and publish it as a benchmark for desktop action recognition. Regular desktop activities of six participants were recorded in egocentric video with a wide-angle head-mounted camera. In particular, we focus on five common desktop actions in which hands are involved. We provide original video data, action annotations at frame-level, and hand masks at pixel-level. We also propose a feature representation for the characterization of different desktop actions based on the spatial and temporal information of hands. In experiments, we illustrate the statistical information about the dataset, and evaluate the action recognition performance of different features as a baseline. The proposed method achieves promising performance for five action classes. [ABSTRACT FROM AUTHOR]

(1) Background: Three-dimensional (3-D) hand position is one of the kinematic parameters that can be inferred from Electromyography (EMG) signals. The inferred parameter is used as a communication channel in human–robot collaboration applications. Although its application from the perspective of rehabilitation and assistive technologies are widely studied, there are few papers on its application involving healthy subjects such as intelligent manufacturing and skill transfer. In this regard, for tasks associated with complex hand trajectories without the consideration of the degree of freedom (DOF), the prediction of 3-D hand position from EMG signal alone has not been addressed. (2) Objective: The primary aim of this study is to propose a model to predict human motor intention that can be used as information from human to robot. Therefore, the prediction of a 3-D hand position directly from the EMG signal for complex trajectories of hand movement, without the direct consideration of joint movements, is studied. In addition, the effects of slow and fast motions on the accuracy of the prediction model are analyzed. (3) Methods: This study used the EMG signal that is collected from the upper limb of healthy subjects, and the position signal of the hand while the subjects manipulate complex trajectories. We considered and analyzed two types of tasks with complex trajectories, each with quick and slow motions. A recurrent fuzzy neural network (RFNN) model was constructed to predict the 3-D position of the hand from the features of EMG signals alone. We used the Pearson correlation coefficient (CC) and normalized root mean square error (NRMSE) as performance metrics. (4) Results: We found that 3-D hand positions of the complex movement can be predicted with the mean performance of CC = 0.85 and NRMSE = 0.105. The 3-D hand position can be predicted well within a future time of 250 ms, from the EMG signal alone. Even though tasks performed under quick motion had a better prediction performance; the statistical difference in the accuracy of prediction between quick and slow motion was insignificant. Concerning the prediction model, we found that RFNN has a good performance in decoding for the time-varying system. (5) Conclusions: In this paper, irrespective of the speed of the motion, the 3-D hand position is predicted from the EMG signal alone. The proposed approach can be used in human–robot collaboration applications to enhance the natural interaction between a human and a robot.

The AnyBody Modeling System™ (AMS) is a musculoskeletal software simulation solution using inverse dynamics analysis. It enables the determination of muscle and joint forces for a given bodily motion. The recording of the individual movement and the transfer into the AMS is a complex and protracted process. Researches indicated that the contactless, visual Leap Motion Controller (LMC) provides clinically meaningful motion data for hand tracking. Therefore, the aim of this study was to integrate the LMC hand motion data into the AMS in order to improve the process of recording a hand movement. A Python-based interface between the LMC and the AMS, termed ROSE Motion, was developed. This solution records and saves the data of the movement as Biovision Hierarchy (BVH) data and AnyScript vector files that are imported into the AMS simulation. Setting simulation parameters, initiating the calculation automatically, and fetching results is implemented by using the AnyPyTools library from AnyBody. The proposed tool offers a rapid and easy-to-use recording solution for elbow, hand, and finger movements. Features include animation, cutting/editing, exporting the motion, and remote controlling the AMS for the analysis and presentation of musculoskeletal simulation results. Comparing the motion tracking results with previous studies, covering problems when using the LMC limit the correctness of the motion data. However, fast experimental setup and intuitive and rapid motion data editing strengthen the use of marker less systems as the herein presented compared to marker based motion capturing.