Extra-Neural Signals From Severed Nerves Enable Intrinsic Hand Movements In Transhumeral Amputations

Robotic prostheses controlled by myoelectric signals can restore limited but important hand function in individuals with upper limb amputation. The lack of individual finger control highlights the yet insurmountable gap to fully replace a biologic hand. Implanted electrodes around severed nerves have been used to elicit sensations perceived as arising from the missing limb, but using such extra-neural electrodes to record motor signals that allow for the decoding of phantom movements has remained elusive. Here, we showed the feasibility of using signals from non-penetrating neural electrodes to decode intrinsic hand and finger movements in individuals with above-elbow amputations. We found that information recorded with extra-neural electrodes alone was enough to decode phantom hand and individual finger movements with high accuracy, and as expected, the addition of myoelectric signals reduced classification errors both in offline and in real-time decoding.

Compact Design of A Lightweight Rehabilitative Exoskeleton for Restoring Grasping Function in Patients with Hand Paralysis

BackgroundMillions of individuals suffer from upper extremity paralysis caused by neurological disorders including stroke, traumatic brain injury, spinal cord injury, or other medical conditions. In order to restore motor control and enhance the quality of life of these patients, daily exercises and strengthening training are necessary. Robotic hand exoskeletons can substitute for the missing motor control and help to restore the functions performed in daily operations. They can also facilitate neuroplasticity to help rehabilitate hand function through routine use. However, most of the hand exoskeletons are bulky, stationary, and cumbersome to use.Methods We have utilized a recent design of a hand exoskeleton (Tenoexo) and modified the design to prototype a motorized, lightweight, fully wearable rehabilitative hand exoskeleton by combining rigid parts with a soft mechanism capable of producing various grasps needed for the execution of daily tasks. We have tested the performance of our developed hand exoskeleton in restoring hand functions in two quadriplegics with chronic cervical cord injury.ResultsMechanical evaluation of our exoskeleton showed that it can produce fingertip force up to 8 N and can cover 91.5 degree of range of motion in just 3 seconds. We further tested the robot in two quadriplegics with chronic hand paralysis, and observed immediate success on independent grasping of different daily objects. ConclusionsThe results suggest that our exoskeleton is a viable option for hand function assistance, allowing patients to regain lost finger control for everyday activities.

Myoelectric digit action decoding with multi-output, multi-class classification: an offline analysis

The ultimate goal of machine learning-based myoelectric control is simultaneous and independent control of multiple degrees of freedom (DOFs), including wrist and digit artificial joints. For prosthetic finger control, regression-based methods are typically used to reconstruct position/velocity trajectories from surface electromyogram (EMG) signals. Unfortunately, such methods have thus far met with limited success. In this work, we propose action decoding, a paradigm-shifting approach for independent, multi-digit movement intent prediction based on multi-output, multi-class classification. At each moment in time, our algorithm decodes movement intent for each available DOF into one of three classes: open, close, or stall (i.e., no movement). Despite using a classifier as the decoder, arbitrary hand postures are possible with our approach. We analyse a public dataset previously recorded and published by us, comprising measurements from 10 able-bodied and two transradial amputee participants. We demonstrate the feasibility of using our proposed action decoding paradigm to predict movement action for all five digits as well as rotation of the thumb. We perform a systematic offline analysis by investigating the effect of various algorithmic parameters on decoding performance, such as feature selection and choice of classification algorithm and multi-output strategy. The outcomes of the offline analysis presented in this study will be used to inform the real-time implementation of our algorithm. In the future, we will further evaluate its efficacy with real-time control experiments involving upper-limb amputees.

Myoelectric digit action decoding with multi-label, multi-class classification: an offline analysis

ABSTRACTThe ultimate goal of machine learning-based myoelectric control is simultaneous and independent control of multiple degrees of freedom (DOFs), including wrist and digit artificial joints. For prosthetic finger control, regression-based methods are typically used to reconstruct position/velocity trajectories from surface electromyogram (EMG) signals. Although such methods have produced highly-accurate results in offline analyses, their success in real-time prosthesis control settings has been rather limited. In this work, we propose action decoding, a paradigm-shifting approach for independent, multi-digit movement intent decoding based on multi-label, multi-class classification. At each moment in time, our algorithm classifies movement action for each available DOF into one of three categories: open, close, or stall (i.e., no movement). Despite using a classifier as the decoder, arbitrary hand postures are possible with our approach. We analyse a public dataset previously recorded and published by us, comprising measurements from 10 able-bodied and two transradial amputee participants. We demonstrate the feasibility of using our proposed action decoding paradigm to predict movement action for all five digits as well as rotation of the thumb. We perform a systematic offline analysis by investigating the effect of various algorithmic parameters on decoding performance, such as feature selection and choice of classification algorithm and multi-output strategy. The outcomes of the offline analysis presented in this study will be used to inform the real-time implementation of our algorithm. In the future, we will further evaluate its efficacy with real-time control experiments involving upper-limb amputees.

Taboo gesticulations as a response to pain

Background and aims Prior research indicates that swearing increases pain tolerance and decreases pain perception in a cold pressor task. In two experiments, we extend this research by testing whether taboo hand gesticulations have a similar effect. Methods Study 1 focused on males and females who, across two trials, submerged an extended middle finger (taboo) and an extended index finger (control) in ice water until discomfort necessitated removal. Study 2 focused exclusively on pain perception in males who, across three trials, submerged their hand, flat, with extended middle finger and with extended index finger, for 45 s each. Results In study 1 taboo gesticulation did not increase pain tolerance or reduce pain perception compared with the index finger control condition, as a main effect or as part of an interaction with condition order. While there was a gesture×gender interaction for pain tolerance, this was driven by an increased pain tolerance for the index finger gesture for women but not men. The results of study 2 again showed that taboo gesticulation did not lower pain perception, although it did increase positive affect compared with both non-taboo gesture conditions. Conclusions Taken together these results provide only limited evidence that taboo gesticulation alters the experience of pain. These largely null findings further our understanding of swearing as a response to pain, suggesting that the activation of taboo schemas is not sufficient for hypoalgesia to occur.