Are you going to do followup research that will create protocols that deliver wrist and hand recovery? WHY NOT? If not, I'd have you all fired for incompetence! We have to remove a lot of dead wood in stroke, there must be some suitable researchers out there actually trying to solve stroke to 100% recovery, but they are not sticking their necks out for fear of the incompetent leadership in stroke.
Laziness? Incompetence? Or just don't care? NO leadership? NO strategy? Not my job? Not my Problem?
Decoding hand and wrist movement intention from chronic stroke survivors with hemiparesis using a user-friendly, wearable EMG-based neural interface
Journal of NeuroEngineering and Rehabilitation volume 21, Article number: 7 (2024)
Abstract
Objective
Seventy-five percent of stroke survivors, caregivers, and health care professionals (HCP) believe current therapy practices are insufficient, specifically calling out the upper extremity as an area where innovation is needed to develop highly usable prosthetics/orthotics for the stroke population. A promising method for controlling upper extremity technologies is to infer movement intention non-invasively from surface electromyography (EMG). However, existing technologies are often limited to research settings and struggle to meet user needs.
Approach
To address these limitations, we have developed the NeuroLife® EMG System, an investigational device which consists of a wearable forearm sleeve with 150 embedded electrodes and associated hardware and software to record and decode surface EMG. Here, we demonstrate accurate decoding of 12 functional hand, wrist, and forearm movements in chronic stroke survivors, including multiple types of grasps from participants with varying levels of impairment. We also collected usability data to assess how the system meets user needs to inform future design considerations.
Main results
Our decoding algorithm trained on historical- and within-session data produced an overall accuracy of 77.1 ± 5.6% across 12 movements and rest in stroke participants. For individuals with severe hand impairment, we demonstrate the ability to decode a subset of two fundamental movements and rest at 85.4 ± 6.4% accuracy. In online scenarios, two stroke survivors achieved 91.34 ± 1.53% across three movements and rest, highlighting the potential as a control mechanism for assistive technologies. Feedback from stroke survivors who tested the system indicates that the sleeve’s design meets various user needs, including being comfortable, portable, and lightweight. The sleeve is in a form factor such that it can be used at home without an expert technician and can be worn for multiple hours without discomfort.
Significance
The NeuroLife EMG System represents a platform technology to record and decode high-resolution EMG for the real-time control of assistive devices in a form factor designed to meet user needs. The NeuroLife EMG System is currently limited by U.S. federal law to investigational use.
Introduction
Stroke is a leading cause of long-term disability in the United States, affecting more than 800,000 people per year [1]. Unilateral paralysis (hemiparesis) affects up to 80% of stroke survivors, leaving many to struggle with activities of daily living (ADLs) including the ability to manipulate objects such as doors, utensils, and clothing due to decreased upper-extremity muscle coordination and weakness [2]. Restoration of hand and arm function to improve independence and overall quality of life is a top priority for stroke survivors and caregivers [3]. Intensive physical rehabilitation is the current gold standard for improving motor function after stroke. Unfortunately, 75% of stroke survivors, caregivers, and health care providers report that current upper extremity training practice is insufficient [4]. The development of user-centric neurotechnologies to restore motor function in stroke survivors could address these unmet clinical needs through a range of different mechanisms, such as improving motivation, enhancing neuroplasticity in damaged sensorimotor networks, and enabling at-home therapy.
Assistive technologies (AT) hold potential to restore hand function and independence to individuals with paralysis [5]. ATs, including exoskeletons and functional electrical stimulation (FES), can assist with opening the hand and also evoke grips strong enough to hold and manipulate objects [6]. Additionally, these systems have been used therapeutically during rehabilitation to strengthen damaged neural connections to restore function [7]. A wide variety of mechanisms to control ATs have been investigated including voice [8], switch [9], position sensors [10], electroencephalography (EEG) [11], electrocorticography (ECoG) [12], intracortical microelectrode arrays (MEA) [13], and electromyography (EMG) [14]. Unfortunately, no single system has simultaneously delivered an intuitive, user-friendly system with a high degree-of-freedom (DoF) control for practical use in real-world settings [4].
Recent advances in portable, high-density EMG-based (HDEMG) systems have the potential to overcome several of these barriers and deliver an intuitive and entirely non-invasive AT control solution [15, 16]. While various EMG-based ATs exist, including the commercially available MyoPro Orthosis [15], most of these systems use a small number of electrodes and rely on threshold-based triggering [14]. Consequently, these systems have limited DoF control which constrains their practical use. Conversely, HDEMG systems consisting of dozens of electrodes and leveraging machine learning approaches to infer complex movement intention can provide high DoF control, significantly expanding functional use cases as well as increasing the proportion of the stroke population that could benefit from these technologies [16,17,18,19]. Currently, HDEMG systems are primarily research systems and are not optimized for usability, including being difficult to set up, requiring manual placement of electrodes, and being non-portable and bulky, which can hinder the successful translation of technologies [4].
To address these limitations, we developed the NeuroLife® EMG System to decode complex forearm motor intention in chronic stroke survivors while simultaneously addressing end user needs. The EMG system was designed to be used as a control device for various end effectors, such as FES systems and exoskeletons. Additionally, the system was specifically designed to meet user needs in domains previously identified as high-value for stroke survivors: donning/doffing simplicity, device setup and initialization, portability, robustness, comfortability, size and weight, and intuitive usage [4]. The sleeve is a wearable garment consisting of up to 150 embedded electrodes that measure muscle activity in the forearm to decode the user’s motor intention. A single zipper on one edge of the sleeve allows for a simplified and streamlined donning and doffing by the user and/or a caregiver. The sleeve design facilitates an intuitive setup process as embedded electrodes that span the entire forearm are consistently placed, eliminating the need for manual electrode placement on specific muscles. The lightweight stretchable fabric, similar to a compression sleeve, was chosen to enhance comfort for long-term use. The sleeve connects to backend Intan hardware housed in a lightweight, 8 × 10″ signal acquisition module appropriate for tabletop upper-extremity rehabilitation. Overall, these design features help address critical usability factors for ATs [4].
In this work, we demonstrate that our EMG system can extract task-specific myoelectric activity at high temporal and spatial resolution to resolve individual movements. Based on EMG data collected from seven individuals with upper extremity hemiparesis due to stroke, trained neural network machine learning models can accurately decode muscle activity in the forearm to infer movement intention, even in the absence of overt motion. We demonstrate the viability of this technique for online decoding, as two subjects used the system for closed-loop control of a virtual hand. This online demonstration is a promising step towards using HDEMG sleeves for high DoF control of ATs based on motor intention. Finally, we present usability data collected from study participants that highlight the user-centric design of the sleeve. These data will be used to inform future developments to deliver an effective EMG-based neural interface that meets end user needs.
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