Abstract
The overarching goal was to resolve a major barrier to real-life prosthesis usability—the rapid degradation of prosthesis control systems, which require frequent recalibrations. Specifically, we sought to develop and test a motor decoder that provides (1) highly accurate, real-time movement response, and (2) unprecedented adaptability to dynamic changes in the amputee’s biological state, thereby supporting long-term integrity of control performance with few recalibrations. To achieve that, an adaptive motor decoder was designed to auto-switch between algorithms in real-time. The decoder detects the initial aggregate motoneuron spiking activity from the motor pool, then engages the optimal parameter settings for decoding the motoneuron spiking activity in that particular state. “Clear-box” testing of decoder performance under varied physiological conditions and post-amputation complications was conducted by comparing the movement output of a simulated prosthetic hand as driven by the decoded signal vs. as driven by the actual signal. Pearson’s correlation coefficient and Normalized Root Mean Square Error were used to quantify the accuracy of the decoder’s output. Our results show that the decoder algorithm extracted the features of the intended movement and drove the simulated prosthetic hand accurately with real-time performance (<10 ms) (Pearson’s correlation coefficient >0.98 to >0.99 and Normalized Root Mean Square Error <13–5%). Further, the decoder robustly decoded the spiking activity of multi-speed inputs, inputs generated from reversed motoneuron recruitment, and inputs reflecting substantial biological heterogeneity of motoneuron properties, also in real-time. As the amputee’s neuromodulatory state changes throughout the day and the electrical properties and ratio of slower vs. faster motoneurons shift over time post-amputation, the motor decoder presented here adapts to such changes in real-time and is thus expected to greatly enhance and extend the usability of prostheses.
Highlights
State-of-the-art prosthetic limbs are capable of sophisticated, multi-degree-of-freedom movements that mimic many physiological motions
Our results show that an adaptive decoder algorithm based on the MN pool spiking rate operated with a high level of accuracy [Pearson’s correlation coefficient > 0.99 and Normalized Root Mean Square Error (NRMSE) ∼5%] and in real-time, while adapting to a wide range of physiological conditions
We employed a multiscale, high-fidelity motor unit (MU) pool model developed by Allen and Elbasiouny (2018) as a computational platform to aid in decoder development
Summary
State-of-the-art prosthetic limbs are capable of sophisticated, multi-degree-of-freedom movements that mimic many physiological motions. Motor Decoder for Prosthetic Control motor system to fully exploit the advanced capabilities of state-of-the-art prostheses. Two types of biological signals have been commonly used to control prosthetic limbs: electromyographic (EMG) and neural (i.e., electroneurogram, ENG) signals. In both signals, the spiking activity of spinal motoneurons (MNs) is extracted—via threshold-crossings detection methods—which motor decoders decrypt to generate a command signal to the prosthesis that is proportional to the amputee’s motor intent (Warren et al, 2016). The spiking activity of MNs contains highly detailed information on the graded activation of individual muscles (i.e., speed and direction of the intended movement) and is a faithful representation of the amputee’s motor intent
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