A quantitative taxonomy of human hand grasps

Background: A proper modeling of human grasping and of hand movements is fundamental for robotics, prosthetics, physiology and rehabilitation. The taxonomies of hand grasps that have been proposed in scientific literature so far are based on qualitative analyses of the movements and thus they are usually not quantitatively justified. Methods: This paper presents to the best of our knowledge the first quantitative taxonomy of hand grasps based on biomedical data measurements. The taxonomy is based on electromyography and kinematic data recorded from 40 healthy subjects performing 20 unique hand grasps. For each subject, a set of hierarchical trees are computed for several signal features. Afterwards, the trees are combined, first into modality-specific (i.e. muscular and kinematic) taxonomies of hand grasps and then into a general quantitative taxonomy of hand movements. The modality-specific taxonomies provide similar results despite describing different parameters of hand movements, one being muscular and the other kinematic. Results: The general taxonomy merges the kinematic and muscular description into a comprehensive hierarchical structure. The obtained results clarify what has been proposed in the literature so far and they partially confirm the qualitative parameters used to create previous taxonomies of hand grasps. According to the results, hand movements can be divided into five movement categories defined based on the overall grasp shape, finger positioning and muscular activation. Part of the results appears qualitatively in accordance with previous results describing kinematic hand grasping synergies. Conclusions: The taxonomy of hand grasps proposed in this paper clarifies with quantitative measurements what has been proposed in the field on a qualitative basis, thus having a potential impact on several scientific fields

Analyze the Human Movements to Help CNS to Shape the Synergy using CNMF and Pattern Recognition

© 2017 The Authors. The Biomedical Signals have been studied for developing human control systems to improving the quality of life. The EMG signal is one of the main types of biomedical signals. It is a convoluted signal. This signal (EMG signal) controlled by the Central nervous system (CNS). It has been a long time expected that the human central nervous system (CNS) uses flexible combinations of some muscles synergy (MS) to solve and control redundant movements. Synergy muscles activities are different in a single muscle. In the concept of Synergy muscle, the CNS does not directly control the activation of a large number of muscles. There are two main movements can help CNS to shape the synergy. The automatic body response and the voluntary actions. These activities remain not too bright. Some studies support the hypothesis that the automatic body responses could be used as a reference to familiarize the voluntary efforts. It has been validating by analyzing the human voluntary movement and the automatic mechanical motions from the muscle synergy. Based on the validation, there was a proposition that the automatic synergy motion may express some features which could support the CNS to shape the voluntary synergy motion using the nonnegative matrix factorization (NMF). Thus the target of the presenting work is to analyses the human movements from the muscle synergy to help CNS shapes the synergy movement by suggestion using the concatenated non-negative matrix factorization (CNMF) method and the pattern recognition method. Then compare the two results and see if that help CNS to shape the synergy movements and which method has more accuracy

Quantification of Extent of Muscle-skin Shifting by Traversal sEMG Analysis Using High-density sEMG Sensor

Averaging electromyographic activity prior to muscle synergy computation is a common method employed to compensate for the inter-repetition variability usually associated with this kind of physiological recording. Capturing muscle synergies requires the preservation of accurate temporal and spatial information for muscle activity. The natural variation in electromyography data across consecutive repetitions of the same task raises several related challenges that make averaging a non-trivial process. Duration and triggering times of muscle activity generally vary across different repetitions of the same task. Therefore, it is necessary to define a robust methodology to segment and average muscle activity that deals with these issues. Emerging from this need, the present work proposes a standard protocol for segmenting and averaging muscle activations from periodic motions in a way that accurately preserves the temporal and spatial information contained in the original data and enables the isolation of a single averaged motion period. This protocol has been validated with muscle activity data recorded from 15 participants performing elbow flexion/extension motions, a series of actions driven by well-established muscle synergies. Using the averaged data, muscle synergies were computed, permitting their behavior to be compared with previous results related to the evaluated task. The comparison between the method proposed and a widely used methodology based on motion flags, shown the benefits of our system maintaining the consistency of muscle activation timings and synergie

Myoelectric Control Systems for Hand Rehabilitation Device: A Review

One of the challenges of the hand rehabilitation device is to create a smooth interaction between the device and user. The smooth interaction can be achieved by considering myoelectric signal generated by human's muscle. Therefore, the so-called myoelectric control system (MCS) has been developed since the 1940s. Various MCS's has been proposed, developed, tested, and implemented in various hand rehabilitation devices for different purposes. This article presents a review of MCS in the existing hand rehabilitation devices. The MCS can be grouped into main groups, the non-pattern recognition and pattern recognition ones. In term of implementation, it can be classified as MCS for prosthetic and exoskeleton hand. Main challenges for MCS today is the robustness issue that hampers the implementation of MCS on the clinical application

Fast human motion prediction for human-robot collaboration with wearable interfaces

In this paper, we aim at improving human motion prediction during human-robot collaboration in industrial facilities by exploiting contributions from both physical and physiological signals. Improved human-machine collaboration could prove useful in several areas, while it is crucial for interacting robots to understand human movement as soon as possible to avoid accidents and injuries. In this perspective, we propose a novel human-robot interface capable to anticipate the user intention while performing reaching movements on a working bench in order to plan the action of a collaborative robot. The proposed interface can find many applications in the Industry 4.0 framework, where autonomous and collaborative robots will be an essential part of innovative facilities. A motion intention prediction and a motion direction prediction levels have been developed to improve detection speed and accuracy. A Gaussian Mixture Model (GMM) has been trained with IMU and EMG data following an evidence accumulation approach to predict reaching direction. Novel dynamic stopping criteria have been proposed to flexibly adjust the trade-off between early anticipation and accuracy according to the application. The output of the two predictors has been used as external inputs to a Finite State Machine (FSM) to control the behaviour of a physical robot according to user's action or inaction. Results show that our system outperforms previous methods, achieving a real-time classification accuracy of $94.3\pm2.9\%$ after $160.0msec\pm80.0msec$ from movement onset

Shoulder muscle activation pattern recognition based on sEMG and machine learning algorithms

BACKGROUND AND OBJECTIVE: Surface electromyography (sEMG) has been used for robotic rehabilitation engineering for volitional control of hand prostheses or elbow exoskeleton, however, using sEMG for volitional control of an upper limb exoskeleton has not been perfectly developed. The long-term goal of our study is to process shoulder muscle bio-electrical signals for rehabilitative robotic assistive device motion control. The purposes of this study included: 1) to test the feasibility of machine learning algorithms in shoulder motion pattern recognition using sEMG signals from shoulder and upper limb muscles, 2) to investigate the influence of motion speed, individual variability, EMG recording device, and the amount of EMG datasets on the shoulder motion pattern recognition accuracy. METHODS: A novel convolutional neural network (CNN) structure was constructed to process EMG signals from 12 muscles for the pattern recognition of upper arm motions including resting, drinking, backward-forward motion, and abduction motion. The accuracy of the CNN models for pattern recognition under different motion speeds, among individuals, and by EMG recording devices was statistically analyzed using ANOVA, GLM Univariate analysis, and Chi-square tests. The influence of EMG dataset number used for CNN model training on recognition accuracy was studied by gradually increasing dataset number until the highest accuracy was obtained. RESULTS: Results showed that the accuracy of the normal speed CNN model in motion pattern recognition was 97.57% for normal speed motions and 97.07% for fast speed motions. The accuracy of the cross-subjects CNN model in motion pattern recognition was 79.64%. The accuracy of the cross-device CNN model in motion pattern recognition was 88.93% for normal speed motion and 80.87% for mixed speed. There was a statistical difference in pattern recognition accuracy between different CNN models. CONCLUSION: The EMG signals of shoulder and upper arm muscles from the upper limb motions can be processed using CNN algorithms to recognize the identical motions of the upper limb including drinking, forward/backward, abduction, and resting. A simple CNN model trained by EMG datasets of a designated motion speed accurately detected the motion patterns of the same motion speed, yielding the highest accuracy compared with other mixed CNN models for various speeds of motion pattern recognition. Increase of the number of EMG datasets for CNN model training improved the pattern recognition accuracy

A Systematic Review of EMG Applications for the Characterization of Forearm and Hand Muscle Activity during Activities of Daily Living: Results, Challenges, and Open Issues

The role of the hand is crucial for the performance of activities of daily living, thereby ensuring a full and autonomous life. Its motion is controlled by a complex musculoskeletal system of approximately 38 muscles. Therefore, measuring and interpreting the muscle activation signals that drive hand motion is of great importance in many scientific domains, such as neuroscience, rehabilitation, physiotherapy, robotics, prosthetics, and biomechanics. Electromyography (EMG) can be used to carry out the neuromuscular characterization, but it is cumbersome because of the complexity of the musculoskeletal system of the forearm and hand. This paper reviews the main studies in which EMG has been applied to characterize the muscle activity of the forearm and hand during activities of daily living, with special attention to muscle synergies, which are thought to be used by the nervous system to simplify the control of the numerous muscles by actuating them in task-relevant subgroups. The state of the art of the current results are presented, which may help to guide and foster progress in many scientific domains. Furthermore, the most important challenges and open issues are identified in order to achieve a better understanding of human hand behavior, improve rehabilitation protocols, more intuitive control of prostheses, and more realistic biomechanical models

Lumbar Posture and Tissue Loading During Short-Term Static Trunk Bending

Low back pain (LBP) is among the most prevalent occupational health problems worldwide and is a leading cause of lost work days. Previous studies have suggested that static prolonged trunk bending could generate lumbar muscle fatigue and introduce creep to the lumbar posterior tissues. Such physical changes could lead to alterations to the lumbar active and passive tissue sharing mechanism and also elevate spinal loading, which is highly associated with the risk of LBP. In the past, most occupational ergonomic studies focused on the instantaneous spine biomechanical responses during task performance. A few studies assessed the changes of spine biomechanics due to spinal tissue creep (introduced by prolonged trunk full flexion) and lumbar muscle fatigue (introduced by prolonged or repetitive trunk bending). However, the dynamic changes of lumbar and trunk postures and spinal tissue loadings during the performance of relatively short-term trunk bending tasks are still unclear. Therefore, the purpose of the current study was to investigate the changes of lumbar biomechanics during short-term, sustained trunk bending.;In the present study, fifteen participants performed short-term (40 seconds) static trunk bending tasks in two different trunk postures (30° or 60°) with two different hand load levels (0 or 15lbs). Results of the current study revealed significant reduction of lumbar muscle activities during the course of task performance. This change was coupled with significant increase of lumbar flexion angle and lumbar passive moment. Such increase of lumbar passive tissue loading could help relief/delay lumbar muscle fatigue by compensating the reduced lumber active tissue loading. Findings of this study suggest that, during the performance of sustained trunk bending, there is an internal mechanism to shift loading from lumbar active tissues to passive tissues by increasing the lumbar flexion. This mechanism is beneficial in reducing the amount of lumbar muscle fatigue; however, lumbar passive tissue creep could be generated at a faster rate

Predicting Continuous Locomotion Modes via Multidimensional Feature Learning from sEMG

Walking-assistive devices require adaptive control methods to ensure smooth transitions between various modes of locomotion. For this purpose, detecting human locomotion modes (e.g., level walking or stair ascent) in advance is crucial for improving the intelligence and transparency of such robotic systems. This study proposes Deep-STF, a unified end-to-end deep learning model designed for integrated feature extraction in spatial, temporal, and frequency dimensions from surface electromyography (sEMG) signals. Our model enables accurate and robust continuous prediction of nine locomotion modes and 15 transitions at varying prediction time intervals, ranging from 100 to 500 ms. In addition, we introduced the concept of 'stable prediction time' as a distinct metric to quantify prediction efficiency. This term refers to the duration during which consistent and accurate predictions of mode transitions are made, measured from the time of the fifth correct prediction to the occurrence of the critical event leading to the task transition. This distinction between stable prediction time and prediction time is vital as it underscores our focus on the precision and reliability of mode transition predictions. Experimental results showcased Deep-STP's cutting-edge prediction performance across diverse locomotion modes and transitions, relying solely on sEMG data. When forecasting 100 ms ahead, Deep-STF surpassed CNN and other machine learning techniques, achieving an outstanding average prediction accuracy of 96.48%. Even with an extended 500 ms prediction horizon, accuracy only marginally decreased to 93.00%. The averaged stable prediction times for detecting next upcoming transitions spanned from 28.15 to 372.21 ms across the 100-500 ms time advances.Comment: 10 pages,7 figure