[ARTICLE] Development of a Low-Cost, Modular Muscle–Computer Interface for At-Home Telerehabilitation for Chronic Stroke – Full Text

Abstract

Stroke is a leading cause of long-term disability in the United States. Recent studies have shown that high doses of repeated task-specific practice can be effective at improving upper-limb function at the chronic stage. Providing at-home telerehabilitation services with therapist supervision may allow higher dose interventions targeted to this population. Additionally, muscle biofeedback to train patients to avoid unwanted simultaneous activation of antagonist muscles (co-contractions) may be incorporated into telerehabilitation technologies to improve motor control. Here, we present the development and feasibility of a low-cost, portable, telerehabilitation biofeedback system called Tele-REINVENT. We describe our modular electromyography acquisition, processing, and feedback algorithms to train differentiated muscle control during at-home therapist-guided sessions. Additionally, we evaluated the performance of low-cost sensors for our training task with two healthy individuals. Finally, we present the results of a case study with a stroke survivor who used the system for 40 sessions over 10 weeks of training. In line with our previous research, our results suggest that using low-cost sensors provides similar results to those using research-grade sensors for low forces during an isometric task. Our preliminary case study data with one patient with stroke also suggest that our system is feasible, safe, and enjoyable to use during 10 weeks of biofeedback training, and that improvements in differentiated muscle activity during volitional movement attempt may be induced during a 10-week period. Our data provide support for using low-cost technology for individuated muscle training to reduce unintended coactivation during supervised and unsupervised home-based telerehabilitation for clinical populations, and suggest this approach is safe and feasible. Future work with larger study populations may expand on the development of meaningful and personalized chronic stroke rehabilitation.

1. Introduction

Stroke is a leading cause of long-term disability in the United States with almost 800,000 people experiencing a new or recurrent stroke each year [1]. While motor recovery was thought to plateau by the chronic stage after stroke (more than 6 months after the vascular incident), more recent studies have shown that improvement of upper limb function is possible at the chronic stage [2,3]. Recent research suggests that high dose interventions of repeated task-specific practice are effective at inducing significant positive outcomes in this population [3,4,5,6]. However, due to the time and physical constraints of many therapy sessions, common in-clinic interventions only provide on average 32 repetitions of functional upper extremity movements per session [7]. Providing at-home telerehabilitation services with therapist supervision and input is a potential solution to allow clinicians to deliver quality, higher dose interventions. Recent studies suggest that telerehabilitation for stroke rehabilitation is feasible and as effective as in-person therapy [8,9].One effective method for improving upper limb function, which could be combined with telerehabilitation, is the reinforcement of muscle activity using electromyography (EMG) biofeedback. Muscle biofeedback has been shown to reduce spasticity and improve post-stroke arm function, motor control, muscle activity, and strength [10,11]. Specifically, previous research has shown that biofeedback training to avoid unintended simultaneous activation of antagonist muscle groups may be particularly beneficial for reducing unnecessary co-contractions that impede functional motor control [12,13]. However, further research is required to address remaining fundamental questions in biofeedback investigations—for example, what is the required intensity and dosage to significantly improve long-term outcomes?Recently, portable systems have been developed for at-home use to improve accessibility and training time with EMG biofeedback [13,14]. However, proper implementation of home-based EMG biofeedback is critical to prevent low participant adherence, avoid high costs, and account for limitations in terms of required physical space, time, and technical literacy [14,15,16,17,18]. Specifically, it has been suggested that the ability to track patient progress in real-time and the continued involvement of a clinician in the intervention are key factors that could improve patient motivation and adherence to at-home rehabilitation programs [9,17].To address these needs, we developed and tested a low-cost, portable telerehabilitation biofeedback system called Tele-REINVENT. Tele-REINVENT builds upon our previous work in which we developed and tested a system (REINVENT) that could provide biofeedback of brain or muscle activity on a computer screen or in immersive virtual reality (VR) with a head-mounted display (HMD) in a laboratory or clinic setting [19,20]. Tele-REINVENT uses the same modular platform and incorporates a telerehabilitation component for live video and audio conferencing with a clinician who meets regularly with the participant to monitor progress. The clinician can also provide technical support, ensure the electrodes are placed correctly, and monitor EMG signals in real-time to ensure adequate signal quality. In addition, Tele-REINVENT uses a portable laptop with commercial low-cost EMG sensors for greater affordability and accessibility in the home environment. Lastly, Tele-REINVENT has new gamified elements to encourage greater engagement, motivation, and adherence to a home-based program. Overall, we aimed to incorporate benefits from both literature on telerehabilitation and EMG biofeedback into our current system.In this paper, we present a detailed description of the Tele-REINVENT system. Additionally, we provide a validation example with two healthy individuals showing that, for our purposes, the performance of the low-cost sensors can be considered comparable to that of research-grade sensors. Finally, we present the feasibility and results of a case study with a chronic stroke survivor who tested the system for 40 sessions over 10 weeks.

2. Materials and Methods

2.1. Participants

To confirm that low-cost EMG sensors produced valid and appropriate measurements, for our purposes, as compared with research-grade equipment, we compared measurements collected from 2 healthy male right-handed participants (ages 28 and 37 years old) using both systems.For the case study, we recruited a 67-year-old male stroke survivor, 11 years after stroke onset, to test our developed system for 10 weeks. The participant had upper extremity hemiparesis, was not taking anti-spasticity medication, had no receptive aphasia, had corrected vision, and did not have a secondary neurological disease. The participant had less than 15 degrees of active wrist or finger extension in the more impaired hand and was unable to grasp and release a ball unassisted.Protocols were approved by the University of Southern California Health Sciences Campus Institutional Review Board (IRB) and written informed consent was obtained from all participants in accordance with the Declaration of Helsinki.

2.2. System Architecture

Tele-REINVENT is a portable stroke telerehabilitation system for at-home, therapist-driven, personalized, and gamified training. We designed the system to acquire reliable EMG signals and provide realistic feedback as game control. We designed and developed hardware and software that allowed us to acquire, process, and store the participant’s EMG signals on a laptop computer (Razer Blade RZ09-01953; Operating System: Windows, Processor: Intel Core i7 7700, RAM: 16 GB; Razer Inc., Irvine, CA, USA). Furthermore, the architecture of the system allows for remote update, control, and data retrieval. Figure 1a shows the overall architecture of the system. Briefly, a C# application developed in Visual Studio (Community 2019, Microsoft, Redmond, WA, USA) controlled the information flow between the system modules and graphical user interfaces (GUI), while the Labstreaming Layer protocol (LSL) [21] transmitted data between modules for processing, game interaction, and storage. Importantly, connecting modules through the LSL network provides an architecture that allows for the functional independence of each component. This is advantageous for continued development since different configurations of sensors, processing pipelines, and environments can be implemented without necessitating updates to other modules. Specifically, while we only tested EMG biofeedback on a laptop screen in the current study, the system builds on the modular architecture we developed in previous work, allowing for the input of electroencephalography or movement data, and visual output to either a laptop screen or HMD-VR system as well as proprioceptive feedback via handheld controllers [19,20]. Additionally, we incorporated a NeuroPype script (Intheon, San Diego, CA, USA) for real-time visualization of the digitized EMG signals.

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Figure 1. Tele-REINVENT architecture, prototype, and interfaces. (a) Software architecture consisting of an acquisition client for electromyography (EMG), signal processing scripts, and a game engine for feedback visualization. Each module is managed by the main application and all data is internally streamed and stored using the Labstreaming Layer (LSL) protocol. This architecture supports both remote data retrieval and updates. Green arrows represent data and blue arrows configurations and commands. (b) Participant graphical interface. Here, the participant can select the game to play and set user configurations accordingly. (c) Case and Myoware sensor with connectors for disposable EMG electrodes. (d) Tele-REINVENT prototype consisting of 2 Myoware EMG sensors, a Teensy 4.0 development board, and a USB isolator enclosed in 3D-printed cases. (e) Clinician graphical interface. Here, the clinician can set up configurations for the biofeedback game, such as number of trials, trial duration, and difficulty. Additionally, the interface can show data from previously recorded sessions.

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