Spatio-temporal warping for myoelectric control: an offline, feasibility study

Objective: The efficacy of an adopted feature extraction method directly affects the classification of the electromyographic (EMG) signals in myoelectric control applications. Most methods attempt to extract the dynamics of the multi-channel EMG signals in the time domain and on a channel-by-channel, or at best pairs of channels, basis. However, considering multi-channel information to build a similarity matrix has not been taken into account. Approach: Combining methods of long and short-term memory (LSTM) and dynamic temporal warping (DTW), we developed a new feature, called spatio-temporal warping (STW), for myoelectric signals. This method captures the spatio-temporal relationships of multi channels EMG signals. Main Results: Across four online databases, we show that in terms of average classification error and standard deviation values, the STW feature outperforms traditional features by 5% to 17%. In comparison to the more recent deep learning models, e.g. convolutional neural networks (CNN), STW outperformed by 5% to 18%. Also, STW showed enhanced performance when compared to the CNN+LSTM model by 2% to 14%. All differences were statistically significant with a large effect size. Significance: This feasibility study provides evidence supporting the hypothesis that spatio-temporal warping of the EMG signals can enhance the classification accuracy in an explainable way when compared to recent deep learning methods. Future work includes real-time implementation of the method and testing for prosthesis control.

On the Sensing and Decoding of Phantom Motions for Control of the Cybernetics of the Upper-Limb Prosthesis

The cybernetic interface within an upper-limb prosthesis facilitates a Human–Machine interaction and ultimately control of the prosthesis limb. A coherent flow between the phantom motion and subsequent actuation of the prosthesis limb to produce the desired gesture hinges heavily upon the physiological sensing source and its ability to acquire quality signals, alongside appropriate decoding of these intent signals with the aid of appropriate signal processing algorithms. In this paper, we discuss the sensing and signal processing aspects of the overall prosthesis control cybernetics, with emphasis on transradial, transhumeral, and shoulder disarticulate amputations, which represent considerable upper-limb amputees typically encountered within the population.

Muscle as a tunable material: implications for achieving muscle-like function in robotic prosthetic devices

ABSTRACT An ideal prosthesis should perform as well as or better than the missing limb it was designed to replace. Although this ideal is currently unattainable, recent advances in design have significantly improved the function of prosthetic devices. For the lower extremity, both passive prostheses (which provide no added power) and active prostheses (which add propulsive power) aim to emulate the dynamic function of the ankle joint, whose adaptive, time-varying resistance to applied forces is essential for walking and running. Passive prostheses fail to normalize energetics because they lack variable ankle impedance that is actively controlled within each gait cycle. By contrast, robotic prostheses can normalize energetics for some users under some conditions. However, the problem of adaptive and versatile control remains a significant issue. Current prosthesis-control algorithms fail to adapt to changes in gait required for walking on level ground at different speeds or on ramps and stairs. A new paradigm of ‘muscle as a tunable material’ versus ‘muscle as a motor’ offers insights into the adaptability and versatility of biological muscles, which may provide inspiration for prosthesis design and control. In this new paradigm, neural activation tunes muscle stiffness and damping, adapting the response to applied forces rather than instructing the timing and amplitude of muscle force. A mechanistic understanding of muscle function is incomplete and would benefit from collaboration between biologists and engineers. An improved understanding of the adaptability of muscle may yield better models as well as inspiration for developing prostheses that equal or surpass the functional capabilities of biological limbs across a wide range of conditions.