Current Solutions and Future Trends for Robotic Prosthetic Hands

Abstract

The desire for functional replacement of a missing hand is an ancient one. Historically, humans have replaced a missing limb with a prosthesis for cosmetic, vocational, or personal autonomy reasons. The hand is a powerful tool, and its loss causes severe physical and often mental debilitation. Technological advancements have allowed the development of increasingly effective artificial hands, which can improve the quality of life of people who suffered a hand amputation. Here, we review the state of the art of robotic prosthetic hands (RPHs), with particular attention to the potential and current limits of their main building blocks: the hand itself, approaches to decoding voluntary commands and controlling the hand, and systems and methods for providing sensory feedback to the user. We also briefly describe existing approaches to characterizing the performance of subjects using RPHs for grasping tasks and provide perspectives on the future of different components and the overall field of RPH development. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 4 is May 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

An Adaptive Multi-Modal Control Strategy to Attenuate the Limb Position Effect in Myoelectric Pattern Recognition

“…The desired quality of adapted data points led previous studies to conclude that supervised calibration sets with high-confidence labels are more feasible than unsupervised [35,43]. This study did not require a supervised dataset for adaptation, but nevertheless shows relative performance improvements for three subjects (1,5,7). The analysis of feature map representations helps to explain that in these cases the new data points resulted in a distinct separation between the classes.…”

“…Most of the latter are designed to match the appearance and functionalities of the human hand, and allow one to perform several hand motions through coordinated activation of independently motorized fingers [2][3][4]. These dexterous bionic devices use a pair of surface electromyography (sEMG) sensors to control one degree of freedom (DOF) at a time and adopt switching techniques via muscle co-activation, a mobile application and short-range proximity sensors to select the desired grip pattern [5]. Despite their potential, many individuals still consider such sophisticated myoelectric devices unreliable and difficult to control [6,7], which has drawn attention to the large discrepancy between the solutions available and users' real needs.…”

MorphoSONIC: A morphologically structured intramembrane cavitation model reveals fiber-specific neuromodulation by ultrasound

“…To the best of our knowledge, this feature has never been achieved with standard electrical stimulation techniques. The encoding of temperature information would be particularly interesting to enrich the sensory feedback in neuroprosthetic devices and improve user experience ( Mendez et al., 2021 ). Conversely, the absence of clear dependency of LIFUS excitation thresholds on fiber diameter represents a disadvantage, as it excludes the possibility to discriminate across different populations of myelinated fibers and, hence, to target a specific peripheral pathway.…”

A modular strategy for next-generation upper-limb sensory-motor neuroprostheses

“…[3][4][5] Decoding from the PNS When the decoding is based on readouts of muscle activity via electromyography (EMG), the most common feature is the time-varying amplitude of its envelope (e.g., Raspopovic et al 49 and D'Anna et al 50 ). Both surface and implanted electrodes 51,52 recording EMGs are well documented 53,54 (see Mendez et al 55 for a review). The decoding algorithms range from simple multilinear regression to complex, recurrent neural-network architectures, 56,57 and include classifiers that identify the intended movement type 39,58,59 and single-finger classification.…”