Stable Responsive EMG Sequence Prediction and Adaptive Reinforcement With Temporal Convolutional Networks.

Prediction of movement intentions from electromyographic (EMG) signals is typically performed with a pattern recognition approach, wherein a short dataframe of raw EMG is compressed into an instantaneous feature-encoding that is meaningful for classification. However, EMG signals are time-varying, implying that a frame-wise approach may not sufficiently incorporate temporal context into predictions, leading to erratic and unstable prediction behavior. OBJECTIVE: We demonstrate that sequential prediction models and, specifically, temporal convolutional networks are able to leverage useful temporal information from EMG to achieve superior predictive performance. METHODS: We compare this approach to other sequential and frame-wise models predicting 3 simultaneous hand and wrist degrees-of-freedom from 2 amputee and 13 non-amputee human subjects in a minimally constrained experiment. We also compare these models on the publicly available Ninapro and CapgMyo amputee and non-amputee datasets. RESULTS: Temporal convolutional networks yield predictions that are more accurate and stable than frame-wise models, especially during inter-class transitions, with an average response delay of 4.6 ms and simpler feature-encoding. Their performance can be further improved with adaptive reinforcement training. SIGNIFICANCE: Sequential models that incorporate temporal information from EMG achieve superior movement prediction performance and these models allow for novel types of interactive training. CONCLUSIONS: Addressing EMG decoding as a sequential modeling problem will lead to enhancements in the reliability, responsiveness, and movement complexity available from prosthesis control systems.

Prosthesis with neuromorphic multilayered e-dermis perceives touch and pain.

The human body is a template for many state-of-the-art prosthetic devices and sensors. Perceptions of touch and pain are fundamental components of our daily lives that convey valuable information about our environment while also providing an element of protection from damage to our bodies. Advances in prosthesis designs and control mechanisms can aid an amputee's ability to regain lost function but often lack meaningful tactile feedback or perception. Through transcutaneous electrical nerve stimulation (TENS) with an amputee, we discovered and quantified stimulation parameters to elicit innocuous (non-painful) and noxious (painful) tactile perceptions in the phantom hand. Electroencephalography (EEG) activity in somatosensory regions confirms phantom hand activation during stimulation. We invented a multilayered electronic dermis (e-dermis) with properties based on the behavior of mechanoreceptors and nociceptors to provide neuromorphic tactile information to an amputee. Our biologically inspired e-dermis enables a prosthesis and its user to perceive a continuous spectrum from innocuous to noxious touch through a neuromorphic interface that produces receptor-like spiking neural activity. In a Pain Detection Task (PDT), we show the ability of the prosthesis and amputee to differentiate non-painful or painful tactile stimuli using sensory feedback and a pain reflex feedback control system. In this work, an amputee can use perceptions of touch and pain to discriminate object curvature, including sharpness. This work demonstrates possibilities for creating a more natural sensation spanning a range of tactile stimuli for prosthetic hands.

Limb Position Tolerant Pattern Recognition for Myoelectric Prosthesis Control with Adaptive Sparse Representations From Extreme Learning.

Myoelectric signals can be used to predict the intended movements of an amputee for prosthesis control. However, untrained effects like limb position changes influence myoelectric signal characteristics, hindering the ability of pattern recognition algorithms to discriminate among motion classes. Despite frequent and long training sessions, these deleterious conditional influences may result in poor performance and device abandonment. GOAL: We present a robust sparsity-based adaptive classification method that is significantly less sensitive to signal deviations resulting from untrained conditions. METHODS: We compare this approach in the offline and online contexts of untrained upper-limb positions for amputee and able-bodied subjects to demonstrate its robustness compared against other myoelectric classification methods. RESULTS: We report significant performance improvements () in untrained limb positions across all subject groups. SIGNIFICANCE: The robustness of our suggested approach helps to ensure better untrained condition performance from fewer training conditions. CONCLUSIONS: This method of prosthesis control has the potential to deliver real-world clinical benefits to amputees: better condition-tolerant performance, reduced training burden in terms of frequency and duration, and increased adoption of myoelectric prostheses.

Electrode-shift Tolerant Myoelectric Movement-pattern Classification using Extreme Learning for Adaptive Sparse Representations.

Myoelectric signal patterns can be used to predict the intended movements of amputees for prosthesis activation. Real-world prosthesis use introduces a variety of unpredictable conditional influences on these patterns, hindering the performance of classification algorithms and potentially leading to device abandonment. We have discovered a state-of-the-art classification method which is significantly more tolerant to these conditional influences. In our prior work, we presented a robust sparsity-based adaptive classification method that is tolerant to pattern deviations resulting from untrained limb positions and the prosthesis load. Herein, we demonstrate that this method is tolerant to the shifting or misalignment of the contact-electrode array which occurs during prosthesis use. We demonstrate the robustness of this approach in untrained electrode-site locations for amputee and able-bodied subjects, and report significant performance improvements over conventional myoelectric pattern recognition approaches. By showing that a single, unified method is robust across a variety of real-world condition spaces, clinicians are more likely to incorporate this method into myoelectric prosthesis controllers, resulting in improved utility and increased adoption among amputee users.

Limb-position robust classification of myoelectric signals for prosthesis control using sparse representations.

The fundamental objective in non-invasive myoelectric prosthesis control is to determine the user's intended movements from corresponding skin-surface recorded electromyographic (sEMG) activation signals as quickly and accurately as possible. Linear Discriminant Analysis (LDA) has emerged as the de facto standard for real-time movement classification due to its ease of use, calculation speed, and remarkable classification accuracy under controlled training conditions. However, performance of cluster-based methods like LDA for sEMG pattern recognition degrades significantly when real-world testing conditions do not resemble the trained conditions, limiting the utility of myoelectrically controlled prosthesis devices. We propose an enhanced classification method that is more robust to generic deviations from training conditions by constructing sparse representations of the input data dictionary comprised of sEMG time-frequency features. We apply our method in the context of upper-limb position changes to demonstrate pattern recognition robustness and improvement over LDA across discrete positions not explicitly trained. For single position training we report an accuracy improvement in untrained positions of 7.95%, p ≪ .001, in addition to significant accuracy improvements across all multiposition training conditions, p <; .001.