The effect of calibration parameters on the control of a myoelectric hand prosthesis using EMG feedback

How to use one surface electromyography sensor to recognize six hand movements for a mechanical hand in real time: a method based on Morse code

Surface electromyography (sEMG) signal is a kind of physiological signal reflecting muscle activity and muscle force. At present, the existing methods of recognizing human motion intention need more than two sensors to recognize more than two kinds of movements, the sensor pasting positions are special, and the hardware conditions for execution are high. In this work, a real-time motion intention recognition method based on Morse code is proposed and applied to the mechanical hand. The short-time and long-term muscle contraction signals collected by a single sEMG sensor were extracted and encoded with the Morse code method, and then the developed mapping method from Morse code to six hand movements were used to recognize hand movements. The average recognition accuracy of hand movements was 94.8704 ± 2.3556%, the average adjusting time was 34.89 s for all subjects, and the execution time of a single movement was 381 ms. The corresponding experiment video can be found in the attachment to show the experiment. The method proposed in this work is a method with one sensor to recognize six movements, low hardware conditions, high recognition accuracy, and insensitive to the difference of sensor pasting position.

Measuring and monitoring skill learning in closed-loop myoelectric hand prostheses using speed-accuracy tradeoffs

Objective.Closed-loop myoelectric prostheses, which combine supplementary sensory feedback and electromyography (EMG) based control, hold the potential to narrow the divide between natural and bionic hands. The use of these devices, however, requires dedicated training. Therefore, it is crucial to develop methods that quantify how users acquire skilled control over their prostheses to effectively monitor skill progression and inform the development of interfaces that optimize this process.Approach.Building on theories of skill learning in human motor control, we measured speed-accuracy tradeoff functions (SAFs) to comprehensively characterize learning-induced changes in skill-as opposed to merely tracking changes in task success across training-facilitated by a closed-loop interface that combined proportional control and EMG feedback. Sixteen healthy participants and one individual with a transradial limb loss participated in a three-day experiment where they were instructed to perform the box-and-blocks task using a timed force-matching paradigm at four specified speeds to reach two target force levels, such that the SAF could be determined.Main results.We found that the participants' accuracy increased in a similar way across all speeds we tested. Consequently, the shape of the SAF remained similar across days, at both force levels. Further, we observed that EMG feedback enabled participants to improve their motor execution in terms of reduced trial-by-trial variability, a hallmark of skilled behavior. We then fit a power law model of the SAF, and demonstrated how the model parameters could be used to identify and monitor changes in skill.Significance.We comprehensively characterized how an EMG feedback interface enabled skill acquisition, both at the level of task performance and movement execution. More generally, we believe that the proposed methods are effective for measuring and monitoring user skill progression in closed-loop prosthesis control.

EMG feedback improves grasping of compliant objects using a myoelectric prosthesis

BACKGROUND

Closing the control loop in myoelectric prostheses by providing artificial somatosensory feedback is recognized as an important goal. However, designing a feedback interface that is effective in realistic conditions is still a challenge. Namely, in some situations, feedback can be redundant, as the information it provides can be readily obtained through hearing or vision (e.g., grasping force estimated from the deformation of a compliant object). EMG feedback is a non-invasive method wherein the tactile stimulation conveys to the user the level of their own myoelectric signal, hence a measurement intrinsic to the interface, which cannot be accessed incidentally.

METHODS

The present study investigated the efficacy of EMG feedback in prosthesis force control when 10 able-bodied participants and a person with transradial amputation used a myoelectric prosthesis to grasp compliant objects of different stiffness values. The performance with feedback was compared to that achieved when the participants relied solely on incidental cues.

RESULTS

The main outcome measures were the task success rate and completion time. EMG feedback resulted in significantly higher success rates regardless of pin stiffness, indicating that the feedback enhanced the accuracy of force application despite the abundance of incidental cues. Contrary to expectations, there was no difference in the completion time between the two feedback conditions. Additionally, the data revealed that the participants could produce smoother control signals when they received EMG feedback as well as more consistent commands across trials, signifying better control of the system by the participants.

CONCLUSIONS

The results presented in this study further support the efficacy of EMG feedback when closing the prosthesis control loop by demonstrating its benefits in particularly challenging conditions which maximized the utility of intrinsic feedback sources.

Nonlinear Mapping From EMG to Prosthesis Closing Velocity Improves Force Control With EMG Biofeedback

When using EMG biofeedback to control the grasping force of a myoelectric prosthesis, subjects need to activate their muscles and maintain the myoelectric signal within an appropriate interval. However, their performance decreases for higher forces, because the myoelectric signal is more variable for stronger contractions. Therefore, the present study proposes to implement EMG biofeedback using nonlinear mapping, in which EMG intervals of increasing size are mapped to equal-sized intervals of the prosthesis velocity. To validate this approach, 20 non-disabled subjects performed force-matching tasks using Michelangelo prosthesis with and without EMG biofeedback with linear and nonlinear mapping. Additionally, four transradial amputees performed a functional task in the same feedback and mapping conditions. The success rate in producing desired force was significantly higher with feedback (65.4±15.9%) compared to no feedback (46.2±14.9%) as well as when using nonlinear (62.4±16.8%) versus linear mapping (49.2±17.2%). Overall, in non-disabled subjects, the highest success rate was obtained when EMG biofeedback was combined with nonlinear mapping (72%), and the opposite for linear mapping with no feedback (39.6%). The same trend was registered also in four amputee subjects. Therefore, EMG biofeedback improved prosthesis force control, especially when combined with nonlinear mapping, which showed to be an effective approach to counteract increasing variability of myoelectric signal for stronger contractions.

Forearm sEMG data from young healthy humans during the execution of hand movements

This work provides a complete dataset containing surface electromyography (sEMG) signals acquired from the forearm with a sampling frequency of 1000 Hz. The dataset is named WyoFlex sEMG Hand Gesture and recorded the data of 28 participants between 18 and 37 years old without neuromuscular diseases or cardiovascular problems. The test protocol consisted of sEMG signals acquisition corresponding to ten wrist and grasping movements (extension, flexion, ulnar deviation, radial deviation, hook grip, power grip, spherical grip, precision grip, lateral grip, and pinch grip), considering three repetitions for each gesture. Also, the dataset contains general information such as anthropometric measures of the upper limb, gender, age, laterally of the person, and physical condition. Likewise, the implemented acquisition system consists of a portable armband with four sEMG channels distributed equidistantly for each forearm. The database could be used for the recognition of hand gestures, evaluation of the evolution of patients in rehabilitation processes, control of upper limb orthoses or prostheses, and biomechanical analysis of the forearm.

Continuous Transition Impairs Discrimination of Electrotactile Frequencies

Just-noticeable difference (JND), indicating the ability to accurately identify small differences in stimulation parameters, can be used to choose more sensitive stimulation methods as well as to calibrate tactile feedback in closed-loop human-machine interfacing. The JND is typically estimated using a forced-choice-discrimination task, in which two stimuli with different intensities are delivered separated by a brief pause. In the applications of tactile feedback, however, the stimulation parameters are typically modulated continuously. It is unclear if the discriminability of stimuli separated in time characterizes the ability to distinguish continuous changes in stimulation intensity. The present study compared the JND when pairs of frequency-modulated electrotactile stimuli were separated in time and presented continuously at two different baseline frequencies (20 and 60 Hz). The results showed that the JND was significantly smaller with time-separation between stimuli, but that the JND obtained with different types of transitions were in most cases linearly associated. In conclusion, the discriminability of time-separated stimuli is systematically better compared to that of the stimuli presented continuously. This can have an impact when calibrating the tactile feedback where the conventional method of the JND assessment might lead to an overly optimistic estimate of detectable changes.

EMG feedback outperforms force feedback in the presence of prosthesis control disturbance

Closing the prosthesis control loop by providing artificial somatosensory feedback can improve utility and user experience. Additionally, closed-loop control should be more robust with respect to disturbance, but this might depend on the type of feedback provided. Thus, the present study investigates and compares the performance of EMG and force feedback in the presence of control disturbances. Twenty able-bodied subjects and one transradial amputee performed delicate and power grasps with a prosthesis in a functional task, while the control signal gain was temporarily increased (high-gain disturbance) or decreased (low-gain disturbance) without their knowledge. Three outcome measures were considered: the percentage of trials successful in the first attempt (reaction to disturbance), the average number of attempts in trials where the wrong force was initially applied (adaptation to disturbance), and the average completion time of the last attempt in every trial. EMG feedback was shown to offer significantly better performance compared to force feedback during power grasping in terms of reaction to disturbance and completion time. During power grasping with high-gain disturbance, the median first-attempt success rate was significantly higher with EMG feedback (73.3%) compared to that achieved with force feedback (60%). Moreover, the median completion time for power grasps with low-gain disturbance was significantly longer with force feedback than with EMG feedback (3.64 against 2.48 s, an increase of 32%). Contrary to our expectations, there was no significant difference between feedback types with regards to adaptation to disturbances and the two feedback types performed similarly in delicate grasps. The results indicated that EMG feedback displayed better performance than force feedback in the presence of control disturbances, further demonstrating the potential of this approach to provide a reliable prosthesis-user interaction.

Estimating speed-accuracy trade-offs to evaluate and understand closed-loop prosthesis interfaces

OBJECTIVE

Closed-loop prosthesis interfaces, which combine electromyography (EMG)-based control with supplementary feedback, represent a promising direction for developing the next generation of bionic limbs. However, we still lack an understanding of how users utilize these interfaces and how to evaluate competing solutions. In this study, we used the framework of speed-accuracy trade-off functions (SAF) to understand, evaluate, and compare the performance of two closed-loop user-prosthesis interfaces.

APPROACH

Ten able-bodied participants and an amputee performed a force-matching task in a functional box-and-block setup at three different speeds. All participants were subjected to both interfaces in a crossover study design with a one-week washout period. Importantly, both interfaces used (identical) direct proportional control but differed in the feedback provided to the participant (EMG feedback vs. Force feedback). Therefore, we estimated the SAFs afforded by the two interfaces and sought to understand how the participants planned and executed the task under the various conditions.

MAIN RESULTS

We found that execution speed significantly influenced performance, and that EMG feedback afforded better overall performance, especially at medium speeds. Notably, we found that there was a difference in the SAF between the two interfaces, with EMG feedback enabling participants to attain higher accuracies faster than Force feedback. Furthermore, both interfaces enabled participants to develop flexible control policies, while EMG feedback also afforded participants the ability to generate smoother, more repeatable EMG commands.

SIGNIFICANCE

Overall, the results indicate that the performance of closed-loop prosthesis interfaces depends critically on the feedback approach and execution speed. This study showed that the SAF framework could be used to reveal the differences between feedback approaches, which might not have been detected if the assessment was performed at a single speed. Therefore, we argue that it is important to consider the speed-accuracy trade-offs to rigorously evaluate and compare user-prosthesis interfaces.

Low-Cost Wearable Band Sensors of Surface Electromyography for Detecting Hand Movements

Surface electromyography (sEMG) is a non-invasive measure of electrical activity generated due to muscle contraction. In recent years, sEMG signals have been increasingly used in diverse applications such as rehabilitation, pattern recognition, and control of orthotic and prosthetic systems. This study presents the development of a versatile multi-channel sEMG low-cost wearable band system to acquire 4 signals. In this case, the signals acquired with the proposed device have been used to detect hand movements. However, the WyoFlex band could be used in some sections of the arm or the leg if the section's diameter matches the diameter of the WyoFlex band. The designed WyoFlex band was fabricated using three-dimensional (3D) printing techniques employing thermoplastic polyurethane and polylactic acid as manufacturing materials. Then, the proposed wearable electromyographic system (WES) consists of 2 WyoFlex bands, which simultaneously allow the wireless acquisition of 4 sEMG channels of each forearm. The collected sEMG can be visualized and stored for future post-processing stages using a graphical user interface designed in Node-RED. Several experimental tests were conducted to verify the performance of the WES. A dataset with sEMG collected from 15 healthy humans has been obtained as part of the presented results. In addition, a classification algorithm based on artificial neural networks has been implemented to validate the usability of the collected sEMG signals.

Combined spatial and frequency encoding for electrotactile feedback of myoelectric signals

Electrotactile stimulation has been commonly used in human–machine interfaces to provide feedback to the user, thereby closing the control loop and improving performance. The encoding approach, which defines the mapping of the feedback information into stimulation profiles, is a critical component of an electrotactile interface. Ideally, the encoding will provide a high-fidelity representation of the feedback variable while being easy to perceive and interpret by the subject. In the present study, we performed a closed-loop experiment wherein discrete and continuous coding schemes are combined to exploit the benefits of both techniques. Subjects performed a muscle activation-matching task relying solely on electrotactile feedback representing the generated myoelectric signal (EMG). In particular, we investigated the performance of two different coding schemes (spatial and spatial combined with frequency) at two feedback resolutions (low: 3 and high: 5 intervals). In both schemes, the stimulation electrodes were placed circumferentially around the upper arm. The magnitude of the normalized EMG was divided into intervals, and each electrode was associated with one interval. When the generated EMG entered one of the intervals, the associated electrode started stimulating. In the combined encoding, the additional frequency modulation of the active electrode also indicated the momentary magnitude of the signal within the interval. The results showed that combined coding decreased the undershooting rate, variability and absolute deviation when the resolution was low but not when the resolution was high, where it actually worsened the performance. This demonstrates that combined coding can improve the effectiveness of EMG feedback, but that this effect is limited by the intrinsic variability of myoelectric control. Our findings, therefore, provide important insights as well as elucidate limitations of the information encoding methods when using electrotactile stimulation to convey a feedback signal characterized by high variability (EMG biofeedback).

Proposal of a Wearable Multimodal Sensing-Based Serious Games Approach for Hand Movement Training After Stroke

Stroke often leads to hand motor dysfunction, and effective rehabilitation requires keeping patients engaged and motivated. Among the existing automated rehabilitation approaches, data glove-based systems are not easy to wear for patients due to spasticity, and single sensor-based approaches generally provided prohibitively limited information. We thus propose a wearable multimodal serious games approach for hand movement training after stroke. A force myography (FMG), electromyography (EMG), and inertial measurement unit (IMU)-based multi-sensor fusion model was proposed for hand movement classification, which was worn on the user’s affected arm. Two movement recognition-based serious games were developed for hand movement and cognition training. Ten stroke patients with mild to moderate motor impairments (Brunnstrom Stage for Hand II-VI) performed experiments while playing interactive serious games requiring 12 activities-of-daily-living (ADLs) hand movements taken from the Fugl Meyer Assessment. Feasibility was evaluated by movement classification accuracy and qualitative patient questionnaires. The offline classification accuracy using combined FMG-EMG-IMU was 81.0% for the 12 movements, which was significantly higher than any single sensing modality; only EMG, only FMG, and only IMU were 69.6, 63.2, and 47.8%, respectively. Patients reported that they were more enthusiastic about hand movement training while playing the serious games as compared to conventional methods and strongly agreed that they subjectively felt that the proposed training could be beneficial for improving upper limb motor function. These results showed that multimodal-sensor fusion improved hand gesture classification accuracy for stroke patients and demonstrated the potential of this proposed approach to be used as upper limb movement training after stroke.