A Hand Gesture Recognition Strategy Based on Virtual-Dimension Increase of EMG.

The electromyography(EMG) signal is the biocurrent associated with muscle contraction and can be used as the input signal to a myoelectric intelligent bionic hand to control different gestures of the hand. Increasing the number of myoelectric-signal channels can yield richer information of motion intention and improve the accuracy of gesture recognition. However, as the number of acquisition channels increases, its effect on the improvement of the accuracy of gesture recognition gradually diminishes, resulting in the improvement of the control effect reaching a plateau. To address these problems, this paper presents a proposed method to improve gesture recognition accuracy by virtually increasing the number of EMG signal channels. This method is able to improve the recognition accuracy of various gestures by virtually increasing the number of EMG signal channels and enriching the motion intention information extracted from data collected from a certain number of physical channels, ultimately providing a solution to the issue of the recognition accuracy plateau caused by saturation of information from physical recordings. Meanwhile, based on the idea of the filtered feature selection method, a quantitative measure of sample sets (separability of feature vectors [SFV]) derived from the divergence and correlation of the extracted features is introduced. The SFV value can predict the classification effect before performing the classification, and the effectiveness of the virtual-dimension increase strategy is verified from the perspective of feature set differentiability change. Compared to the statistical motion intention recognition success rate, SFV is a more representative and faster measure of classification effectiveness and is also suitable for small sample sets.

Comparison of Bipolar and Monopolar Electrode Configurations for FES on Biceps Brachii.

Function electrical stimulation (FES) is recommended as one of the effective methods for rehabilitation of motor function after stroke. There are two forms to deliver electrical stimulation to induce muscle contraction: Bipolar electrode configuration with two electrodes of the same size, and monopolar electrode configuration with a bigger electrode as an indifferent electrode and a smaller one as an active electrode. The purpose of this study is to compare the two kinds of configuration on biceps brachii in terms of induced muscle contraction force and muscle fatigue. In the experiment, electrical stimulation was applied on biceps brachii muscles of the right arm. Isometric contraction was induced by fixing the elbow joint during the stimulation. The experimental results showed that the induced contraction force was bigger using monopolar electrode configuration with the indifferent electrode on the antagonist muscle, and there was no significant difference in muscle fatigue between the configurations. Monopolar electrode configuration with the indifferent electrode on the antagonist muscle was suggested as the most effective method for FES on biceps brachii.Clinical Relevance- This study establishes an effective electrode configuration for FES on biceps brachii.

Research status of elderly-care robots and safe human-robot interaction methods.

Faced with the increasingly severe global aging population with fewer children, the research, development, and application of elderly-care robots are expected to provide some technical means to solve the problems of elderly care, disability and semi-disability nursing, and rehabilitation. Elderly-care robots involve biomechanics, computer science, automatic control, ethics, and other fields of knowledge, which is one of the most challenging and most concerned research fields of robotics. Unlike other robots, elderly-care robots work for the frail elderly. There is information exchange and energy exchange between people and robots, and the safe human-robot interaction methods are the research core and key technology. The states of the art of elderly-care robots and their various nursing modes and safe interaction methods are introduced and discussed in this paper. To conclude, considering the disparity between current elderly care robots and their anticipated objectives, we offer a comprehensive overview of the critical technologies and research trends that impact and enhance the feasibility and acceptance of elderly care robots. These areas encompass the collaborative assistance of diverse assistive robots, the establishment of a novel smart home care model for elderly individuals using sensor networks, the optimization of robot design for improved flexibility, and the enhancement of robot acceptability.

Improvement of Precision Grasping Performance by Interaction Between Soft Finger Pulp and Hard Nail.

In this study, we investigated the effect of the presence or absence of fingernails on precision grasping using artificial anthropomimetic fingers. We hypothesized that fingernails improve precision grasping performance by increasing the friction coefficient while suppressing fingertip deformation. To test our hypothesis, we developed artificial fingertips, each composed of bone, nail, skin, and soft tissue, and fabricated three types of artificial fingers with different skin softness grades and artificial fingers without nails as the control condition. Pullout experiments of cylindrical objects and T-shaped blocks were conducted using the developed artificial fingertips with and without nails, and the magnitude of the holding force was compared. The nail contributed to object grasping stability because the magnitude of the holding force was significantly increased by the presence of the nail in the artificial fingertip with soft skin. The rate of increase in the magnitude of the holding force of the T-shaped block was more significant (3.10 times maximum) compared with the cylindrical object (1.08 times maximum) because the finger pulp deformation was suppressed by the nail, and the form closure, that is, geometric constraint, was formed for the grasping object. The results of this study show that soft fingertips and hard nails can significantly improve the grasping performance of soft robotic hands. And these results suggest that the human nail improves precision grasping performance by forming geometric constraints on the grasped object, suppressing finger pulp deformation.

Coevolution of Myoelectric Hand Control under the Tactile Interaction among Fingers and Objects.

The usability of a prosthetic hand differs significantly from that of a real hand. Moreover, the complexity of manipulation increases as the number of degrees of freedom to be controlled increases, making manipulation with biological signals extremely difficult. To overcome this problem, users need to select a grasping posture that is adaptive to the object and a stable grasping method that prevents the object from falling. In previous studies, these have been left to the operating skills of the user, which is extremely difficult to achieve. In this study, we demonstrate how stable and adaptive grasping can be achieved according to the object regardless of the user's operation technique. The required grasping technique is achieved by determining the correlation between the motor output and each sensor through the interaction between the prosthetic hand and the surrounding stimuli, such as myoelectricity, sense of touch, and grasping objects. The agents of the 16-DOF robot hand were trained with the myoelectric signals of six participants, including one child with a congenital forearm deficiency. Consequently, each agent could open and close the hand in response to the myoelectric stimuli and could accomplish the object pickup task. For the tasks, the agents successfully identified grasping patterns suitable for practical and stable positioning of the objects. In addition, the agents were able to pick up the object in a similar posture regardless of the participant, suggesting that the hand was optimized by evolutionary computation to a posture that prevents the object from being dropped.

Design of an Effective Prosthetic Hand System for Adaptive Grasping with the Control of Myoelectric Pattern Recognition Approach.

In this paper, we develop a prosthetic bionic hand system to realize adaptive gripping with two closed-loop control loops by using a linear discriminant analysis algorithm (LDA). The prosthetic hand contains five fingers and each finger is driven by a linear servo motor. When grasping objects, four fingers except the thumb would adjust automatically and bend with an appropriate gesture, while the thumb is stretched and bent by the linear servo motor. Since the change of the surface electromechanical signal (sEMG) occurs before human movement, the recognition of sEMG signal with LDA algorithm can help to obtain people's action intention in advance, and then timely send control instructions to assist people to grasp. For activity intention recognition, we extract three features, Variance (VAR), Root Mean Square (RMS) and Minimum (MIN) for recognition. As the results show, it can achieve an average accuracy of 96.59%. This helps our system perform well for disabilities to grasp objects of different sizes and shapes adaptively. Finally, a test of the people with disabilities grasping 15 objects of different sizes and shapes was carried out and achieved good experimental results.

A 3-DOF Bionic Waist Joint for Humanoid Robot.

In this study, a 3 degree-of-freedom bionic waist joint was developed with coupled tendon-driven mechanism. This bionic waist joint can not only ensure the safety of the human-robot interface, but also increase the load capacity without increasing the weight. The coupled tendon-driven mechanism enables the motion of each joint to be driven by at least two motors together, and enables a maximum torque of 3 times the maximum motor output torque at each joint. The bionic waist joint has similar kinematic characteristics to a human waist, including degrees of freedom (DOF) and range of motion (ROM). The problem of coexistence of coupling and decoupling in the same rotating joint was solved with a novel mechanism that can promote further versatility of the coupled tendon-driven mechanism. The basic movements and characteristics of the waist was validated in the experiment.

Investigation of Motor Point Shift and Contraction Force of Triceps Brachii for Functional Electrical Stimulation.

Functional electrical stimulation (FES) has been used for neurorehabilitation of individuals with paralysis due to spinal cord injuries or stroke aftereffects. The biceps brachii is often adopted in studies on FES because of the ease of stimulation, while there are few studies on the triceps brachii. Stimulation of the triceps brachii is important because the biceps brachii tends to be spastic. The aim of this study is to investigate the position shift of the motor points (MPs) of the three main muscle groups in triceps brachii with respect to the elbow joint angle, and the contraction force of the muscle groups. Firstly, MPs were measured in 6 healthy individuals using an MP pen at 5 elbow joint angles. The MPs of the long and lateral heads shifted distally and laterally, and the MPs of the medial head shifted distally and medially as the arm extended. The MPs of the long head shifted farthest of all. Secondly, the contraction force was measured in 9 healthy individuals using a force gauge at elbow joint angle of 90 degrees. Three different voltages were applied: 4, 8, and 12 V. The results showed that the medial head yields a sufficient contraction force although the medial head is situated deeper than the other two muscle groups. These findings will help to better understand the stimulation of the triceps brachii and improve the efficiency of electrical stimulation therapy.

A Study on the Classification Effect of sEMG Signals in Different Vibration Environments Based on the LDA Algorithm.

Myoelectric prosthesis has become an important aid to disabled people. Although it can help people to recover to a nearly normal life, whether they can adapt to severe working conditions is a subject that is yet to be studied. Generally speaking, the working environment is dominated by vibration. This paper takes the gripping action as its research object, and focuses on the identification of grasping intentions under different vibration frequencies in different working conditions. In this way, the possibility of the disabled people who wear myoelectric prosthesis to work in various vibration environment is studied. In this paper, an experimental test platform capable of simulating 0-50 Hz vibration was established, and the Surface Electromyography (sEMG) signals of the human arm in the open and grasping states were obtained through the MP160 physiological record analysis system. Considering the reliability of human intention recognition and the rapidity of algorithm processing, six different time-domain features and the Linear Discriminant Analysis (LDA) classifier were selected as the sEMG signal feature extraction and recognition algorithms in this paper. When two kinds of features, Zero Crossing (ZC) and Root Mean Square (RMS), were used as input, the accuracy of LDA algorithm can reach 96.9%. When three features, RMS, Minimum Value (MIN), and Variance (VAR), were used as inputs, the accuracy of the LDA algorithm can reach 98.0%. When the six features were used as inputs, the accuracy of the LDA algorithm reached 98.4%. In the analysis of different vibration frequencies, it was found that when the vibration frequency reached 20 Hz, the average accuracy of the LDA algorithm in recognizing actions was low, while at 0 Hz, 40 Hz and 50 Hz, the average accuracy was relatively high. This is of great significance in guiding disabled people to work in a vibration environment in the future.

Asymmetric shape of distal phalanx of human finger improves precision grasping.

In morphology field, the functions of an asymmetric-shaped distal phalanx in human finger have only been inferred. In this study, we used an engineering approach to empirically examine the effects of the shape of distal phalanx on the ability of precision grasping. Hence, we developed artificial fingertips consisting of four parts, namely bones, nails, skin, and subcutaneous tissue, that substitute the actual human fingertips. Furthermore, we proposed a method to evaluate the grasping ability of artificial fingers. When a cylindrical object was grasped by an artificial fingertip, a pull-out experiment was conducted. Thus, the asymmetric type was found to be superior in terms of drawing force, holding time, and work of friction than the symmetric type. Our results clearly demonstrate that the asymmetric shape, particularly the mirror-reversed shape of the distal phalanx, improves the ability of precision grasping and suggests that the human distal phalanx is shaped favorably for object grasping.

Joint angle based motor point tracking stimulation for surface FES: A Study on biceps brachii.

Functional electrical stimulation (FES) has been an effective treatment option in clinical rehabilitation such as motor function recovery after stroke. The main limitation of FES is the lack of stimulation efficiency in motor unit recruitment compared with voluntary contractions, which may cause the early onset of muscle fatigue. The stimulation efficiency of FES can be improved by optimizing electrode positions to target the motor point (MP). However, the location of MP relative to the skin may shift with the change of muscle geometry during dynamic exercise. Hence, the purpose of this study is to maintain the stimulation efficiency of FES in dynamic exercise by switching the stimulation position to follow the shift of MP. We first measured the shift of the MP of the biceps brachii with respect to the elbow joint angle, and then conducted an experiment to compare four stimulation methods: 2-channel simultaneous stimulation (SS), 2-channel time based shifting stimulation (TSS), 2-channel joint angle based shifting stimulation (JASS), and 3-channel JASS. TSS and JASS were designed as two different MP tracking strategies. The experimental results show that the 3-channel JASS caused the smallest decrease in the maximal elbow angle and the angular velocity. The results also suggest that MP tracking stimulation based on joint angle is effective for the sustainable induction of muscle contraction. Both tracking selectivity and tracking density were shown to be important to improve the stimulation efficiency of FES.

Semi-Automated Control System for Reaching Movements in EMG Shoulder Disarticulation Prosthesis Based on Mixed Reality Device.

Goal: The development of a control system for an electromyographic shoulder disarticulation (EMG-SD) prosthesis to rapidly achieve a task with a reduction in the operational failure of the user. Methods: The motion planning of an EMG-SD prosthesis was automated using measured visual information through a mixed reality device. The detection of an object to be grasped and motion execution depended on the EMG of the user, which gives voluntary controllability and makes the system semi-automated. Two evaluation experiments with reaching and reach-to-grasp movements were conducted to compare the performance of the conventional system when operated using only visual feedback control of the user. Results: The proposed system can more rapidly and accurately achieve reaching movements (32% faster) and more accurate (69%) reach-to-grasp movements than a conventional system. Conclusions: The proposed control system achieves a high task performance with a reduction in the operational failure of an EMG-SD prosthesis user.

Survey on Main Drive Methods Used in Humanoid Robotic Upper Limbs.

Humanoid robotic upper limbs including the robotic hand and robotic arm are widely studied as the important parts of a humanoid robot. A robotic upper limb with light weight and high output can perform more tasks. The drive system is one of the main factors affecting the weight and output of the robotic upper limb, and therefore, the main purpose of this study is to compare and analyze the effects of the different drive methods on the overall structure. In this paper, we first introduce the advantages and disadvantages of the main drive methods such as tendon, gear, link, fluid (hydraulic and pneumatic), belt, chain, and screw drives. The design of the drive system is an essential factor to allow the humanoid robotic upper limb to exhibit the structural features and functions of the human upper limb. Therefore, the specific applications of each drive method on the humanoid robotic limbs are illustrated and briefly analyzed. Meanwhile, we compared the differences in the weight and payload (or grasping force) of the robotic hands and robotic arms with different drive methods. The results showed that the tendon drive system is easier to achieve light weight due to its simple structure, while the gear drive system can achieve a larger torque ratio, which results in a larger output torque. Further, the weight of the actuator accounts for a larger proportion of the total weight, and a reasonable external placement of the actuator is also beneficial to achieve light weight.

Learning Data Correction for Myoelectric Hand Based on "Survival of the Fittest".

In recent years, myoelectric hands have become multi-degree-of-freedom (DOF) devices, which are controlled via machine learning methods. However, currently, learning data for myoelectric hands are gathered manually and thus tend to be of low quality. Moreover, in the case of infants, gathering accurate learning data is nearly impossible because of the difficulty of communicating with them. Therefore, a method that automatically corrects errors in the learning data is necessary. Myoelectric hands are wearable robots and thus have volumetric and weight constraints that make it infeasible to store large amounts of data or apply complex processing methods. Compared with general machine learning methods such as image processing, those for myoelectric hands have limitations on the data storage, although the amount of data to be processed is quite large. If we can use this huge amount of processing data to correct the learning data without storing the processing data, the machine learning performance is expected to improve. We then propose a method for correcting the learning data through utilisation of the signals acquired during the use of the myoelectric hand. The proposed method is inspired by "survival of the fittest." The effectiveness of the method was verified through offline analysis. The method reduced the amount of learning data and learning time by approximately a factor of 10 while maintaining classification rates. The classification rates improved for one participant but slightly deteriorated on average among all participants. To solve this problem, verifying the method via interactive learning will be necessary in the future.

Development of a Shoulder Disarticulation Prosthesis System Intuitively Controlled With the Trunk Surface Electromyogram.

We developed an intuitively operational shoulder disarticulation prosthesis system that can be used without long-term training. The developed system consisted of four degrees of freedom joints, as well as a user adapting control system based on a machine learning technique and surface electromyogram (EMG) of the trunk. We measured the surface EMG of the trunk of healthy subjects at multiple points and analyzed through principal component analysis to identify the proper EMG measurement portion of the trunk, which was determined to be distributed in the chest and back. Additionally, evaluation experiments demonstrated the capability of four healthy subjects to grasp and move objects in the horizontal as well as the vertical directions, using our developed system controlled via the EMG of the chest and back. Moreover, we also quantitatively confirmed the ability of a bilateral shoulder disarticulation amputee to complete the evaluation experiment similar to healthy subjects.

Development of an sEMG sensor composed of two-layered conductive silicone with different carbon concentrations.

To achieve robust sEMG measurements in an EMG prosthetic system, this study proposes a surface electromyogram (sEMG) sensor with a novel electrode structure composed of two-layered conductive silicone with different carbon concentrations. We hypothesized there is an optimal carbon concentration for achieving a large sEMG amplitude with robustness to external perturbation, and we empirically determined this optimal concentration. We produced fourteen sets of electrodes, with the weight ratio of carbon to silicone ranging from 1.7% to 4.0%. Using these electrodes, the user sEMG and electrical properties of the electrodes were measured. An external perturbation was applied on one side of the electrode to introduce a condition of unbalanced contact to the sEMG sensor. We defined an index of robustness for the sEMG sensor based on the signal-to-noise ratio in the balanced and unbalanced contact conditions. Based on the results of the robustness index, two optimal carbon concentrations, at weight ratios of 2.0%-2.1% and 2.6%-2.7%, were observed. Moreover, the double-peak property was correlated to the capacitance. Our results clearly demonstrate an optimal carbon concentration for robust sEMG measurements, and suggest that the robust measurement of sEMG is supported by the capacitance component of the sensor system.

Design and Implementation of Arch Function for Adaptive Multi-Finger Prosthetic Hand.

Although arch motions of the palm substantially contribute to frequent hand grasping, they are usually neglected in the development of prosthetic hands which focuses on digit movements. We designed the arch function for its implementation on an adaptive multi-finger prosthetic hand. The digits from the developed hand can perform adaptive grasping, and two carpometacarpal joints enable the palm of the prosthetic hand to form an arch with the thumb. Moreover, the arch posture can be passively released, mimicking the human hand switching between sphere and medium wrap grasps according to the situation. Other requirements such as weight, cost, and size limitations for hand prostheses were also considered. As a result, we only used three actuators fully embedded in the palm through a novel tendon-driven transmission. Although the prosthetic hand is almost the same size of an adult hand, it weighs only 146 g and can perform 70% of the 10 most frequent grasps.

Development of a Parent Wireless Assistive Interface for Myoelectric Prosthetic Hands for Children.

In this study, a one-degree-of-freedom myoelectric prosthesis system was proposed using a Parent Wireless Assistive Interface (PWAI) that allowed an external assistant (e. g., the parent of the user) to immediately adjust the parameters of the prosthetic hand controller. In the PWAI, the myoelectric potential of use of the upper limb was plotted on an external terminal in real time. Simultaneously, the assistant adjusted the parameters of the prosthetic hand control device and manually manipulated the prosthetic hand. With these functions, children that have difficulty verbally communicating could obtain properly adjusted prosthetic hands. In addition, non-experts could easily adjust and manually manipulate the prosthesis; therefore, training for the prosthetic hands could be performed at home. Two types of hand motion discrimination methods were constructed in this study of the myoelectric control system: (1) a threshold control based on the myoelectric potential amplitude information and (2) a pattern recognition of the frequency domain features. In an evaluation test of the prosthesis threshold control system, child subjects achieved discrimination rates as high as 89%, compared with 96% achieved by adult subjects. Furthermore, the high discrimination rate was maintained by sequentially updating the threshold value. In addition, a discrimination rate of 82% on average was obtained by recognizing three motions using the pattern recognition method.

One-Channel Surface Electromyography Decomposition for Muscle Force Estimation.

Estimating muscle force by surface electromyography (sEMG) is a non-invasive and flexible way to diagnose biomechanical diseases and control assistive devices such as prosthetic hands. To estimate muscle force using sEMG, a supervised method is commonly adopted. This requires simultaneous recording of sEMG signals and muscle force measured by additional devices to tune the variables involved. However, recording the muscle force of the lost limb of an amputee is challenging, and the supervised method has limitations in this regard. Although the unsupervised method does not require muscle force recording, it suffers from low accuracy due to a lack of reference data. To achieve accurate and easy estimation of muscle force by the unsupervised method, we propose a decomposition of one-channel sEMG signals into constituent motor unit action potentials (MUAPs) in two steps: (1) learning an orthogonal basis of sEMG signals through reconstruction independent component analysis; (2) extracting spike-like MUAPs from the basis vectors. Nine healthy subjects were recruited to evaluate the accuracy of the proposed approach in estimating muscle force of the biceps brachii. The results demonstrated that the proposed approach based on decomposed MUAPs explains more than 80% of the muscle force variability recorded at an arbitrary force level, while the conventional amplitude-based approach explains only 62.3% of this variability. With the proposed approach, we were also able to achieve grip force control of a prosthetic hand, which is one of the most important clinical applications of the unsupervised method. Experiments on two trans-radial amputees indicated that the proposed approach improves the performance of the prosthetic hand in grasping everyday objects.

sEMG Sensor Using Polypyrrole-Coated Nonwoven Fabric Sheet for Practical Control of Prosthetic Hand.

One of the greatest challenges of using a myoelectric prosthetic hand in daily life is to conveniently measure stable myoelectric signals. This study proposes a novel surface electromyography (sEMG) sensor using polypyrrole-coated nonwoven fabric sheet as electrodes (PPy electrodes) to allow people with disabilities to control prosthetic limbs. The PPy electrodes are sewn on an elastic band to guarantee close contact with the skin and thus reduce the contact electrical impedance between the electrodes and the skin. The sensor is highly customizable to fit the size and the shape of the stump so that people with disabilities can attach the sensor by themselves. The performance of the proposed sensor was investigated experimentally by comparing measurements of Ag/AgCl electrodes with electrolytic gel and the sEMG from the same muscle fibers. The high correlation coefficient (0.87) between the two types of sensors suggests the effectiveness of the proposed sensor. Another experiment of sEMG pattern recognition to control myoelectric prosthetic hands showed that the PPy electrodes are as effective as Ag/AgCl electrodes for measuring sEMG signals for practical myoelectric control. We also investigated the relation between the myoelectric signals' signal-to-noise ratio and the source impedances by simultaneously measuring the source impedances and the myoelectric signals with a switching circuit. The results showed that differences in both the norm and the phase of the source impedance greatly affect the common mode noise in the signal.