Use of machine learning in the field of prosthetics and orthotics: A systematic narrative review

Developing RPC-Net: Leveraging High-Density Electromyography and Machine Learning for Improved Hand Position Estimation

OBJECTIVE

The purpose of this study was to develop and evaluate the performance of RPC-Net (Recursive Prosthetic Control Network), a novel method using simple neural network architectures to translate electromyographic activity into hand position with high accuracy and computational efficiency.

METHODS

RPC-Net uses a regression-based approach to convert forearm electromyographic signals into hand kinematics. We tested the adaptability of the algorithm to different conditions and compared its performance with that of solutions from the academic literature.

RESULTS

RPC-Net demonstrated a high degree of accuracy in predicting hand position from electromyographic activity, outperforming other solutions with the same computational cost. Including previous position data consistently improved results across subjects and conditions. RPC-Net showed robustness against a reduction in the number of electromyography electrodes used and shorter input signals, indicating potential for further reduction in computational cost.

CONCLUSION

The results demonstrate that RPC-Net is capable of accurately translating forearm electromyographic activity into hand position, offering a practical and adaptable tool that may be accessible in clinical settings.

SIGNIFICANCE

The development of RPC-Net represents a significant advancement. In clinical settings, its application could enable prosthetic devices to be controlled in a way that feels more natural, improving the quality of life for individuals with limb loss.

Convolutional neural network algorithm trained on lumbar spine radiographs to predict outcomes of transforaminal epidural steroid injection for lumbosacral radicular pain from spinal stenosis

Little is known about the therapeutic outcomes of transforaminal epidural steroid injection (TFESI) in patients with lumbosacral radicular pain due to lumbar spinal stenosis (LSS). Using lumbar spine radiographs as input data, we trained a convolutional neural network (CNN) to predict therapeutic outcomes after lumbar TFESI in patients with lumbosacral radicular pain caused by LSS. We retrospectively recruited 193 patients for this study. The lumbar spine radiographs included anteroposterior, lateral, and bilateral (left and right) oblique views. We cut each lumbar spine radiograph image into a square shape that included the vertebra corresponding to the level at which the TFESI was performed and the vertebrae juxta below and above that level. Output data were divided into "favorable outcome" (≥ 50% reduction in the numeric rating scale [NRS] score at 2 months post-TFESI) and "poor outcome" (< 50% reduction in the NRS score at 2 months post-TFESI). Using these input and output data, we developed a CNN model for predicting TFESI outcomes. The area under the curve of our model was 0.920. Its accuracy was 87.2%. Our CNN model has an excellent capacity for predicting therapeutic outcomes after lumbar TFESI in patients with lumbosacral radicular pain induced by LSS.

Advances in prosthetic technology: a perspective on ethical considerations for development and clinical translation

Technological advancements of prostheses in recent years, such as haptic feedback, active power, and machine learning for prosthetic control, have opened new doors for improved functioning, satisfaction, and overall quality of life. However, little attention has been paid to ethical considerations surrounding the development and translation of prosthetic technologies into clinical practice. This article, based on current literature, presents perspectives surrounding ethical considerations from the authors' multidisciplinary views as prosthetists (HG, AM, CLM, MGF), as well as combined research experience working directly with people using prostheses (AM, CLM, MGF), wearable technologies for rehabilitation (MGF, BN), machine learning and artificial intelligence (BN, KKQ), and ethics of advanced technologies (KKQ). The target audience for this article includes developers, manufacturers, and researchers of prosthetic devices and related technology. We present several ethical considerations for current advances in prosthetic technology, as well as topics for future research, that may inform product and policy decisions and positively influence the lives of those who can benefit from advances in prosthetic technology.