RehabEmbed: ReInHand Android App

Improving Hand Function of Severely Impaired Chronic Hemiparetic Stroke Individuals Using Task-Specific Training With the ReIn-Hand System: A Case Series

Front Neurol. 2018; 9: 923. Published online 2018 Nov 7. doi: 10.3389/fneur.2018.00923

PMCID: PMC6234834PMID: 30464754

Introduction

The purpose of this study is to determine the effect of device-assisted task-specific training on hand motor function and sensation (stereognosis and cutaneous sensory touch threshold) in individuals with chronic stroke and severe UE impairment.

Methods

Devices

ReIn-Hand is a recently developed EMG-FES device that uses the combination of an EMG collection unit (Avatar physiological recorder, Electrical Geodesics, Inc., Eugene, OR, United States), an intelligent detection software the ReIn-HAND platform, and an electrical stimulator (Empi 300, Vista, CA, United States). The ReIn-Hand platform wirelessly and simultaneously measures surface EMG activities from eight upper limb muscles, including deltoid, biceps brachii, triceps, extensor communis digitorum, extensor carpi radialis, flexor digitorum profundus, flexor carpi radialis, and abductor pollicis. The device uses subject-dependent coherence-based notch filter to increase the signal-to-noise ratio of the collected EMG signals; it then uses the mean absolute value, zero crossing, slope sign changes, waveform length values to perform real-time detection of hand opening with or without activation of the shoulder/elbow muscles during functional upper limb motor tasks. Once hand opening is detected, a signal is sent to trigger the electrical stimulator to assist paretic hand opening. In all the subjects, including those with abnormal synergistic muscle activity and spasticity, the average detection accuracies were >90%. The stimulation electrodes were placed over finger/wrist extensors; the stimulation was set with the following parameters: amplitude sufficient for maximal hand opening without discomfort, biphasic waveform, frequency 50 ± 20%, 300 μs pulse width, and duration time of 3s.

Conclusions

These results suggest that using the ReIn-Hand device during functional reaching and grasping activities may contribute to improvements in gross motor function and stereognosis sensation of the paretic arm in individuals with moderate to severe impairment following chronic stroke.

Clinical Messages

• Task-specific training aided by the ReIn-Hand device might improve motor and sensory function in severely impaired chronic stroke.
• Further research is needed to assess the effectiveness of this intervention for improving clinical outcomes in randomized controlled trials.

About technology advancements and installation steps

Hardware Updates

In light of ongoing technological advancements, we have incorporated the BITalino MuscleBIT bundle for electromyography (EMG) signal acquisition. This bundle is tailored for individuals seeking to measure muscle activity through the evaluation of EMG signals. Furthermore, we have customized GPIO output ports on this hardware, aligning the trigger signal for electrical stimulators with the EMG reception signal. Both signals are transmitted via Bluetooth, establishing a wireless connection that seamlessly integrates control devices (such as tablets or smartphones) with the acquisition and stimulation devices.

A Step-by-Step Guide to Software Installation

1. Open https://github.com/Achillesy/Android_ReInHandv3/releases
2. Download Dr-ReIn-Hand.apk ReIn-Hand.apk and reinhand.zip
3. Open Settings -> Connections -> Bluetooth and pair BITalino
4. Open My Files -> Downloads
5. Install Dr-ReIn-Hand.apk
6. Install ReIn-Hand.apk
7. Move reinhand.zip to the Internal storage and Extract to current folder
8. Test Dr-ReIn-Hand.apk
9. Test ReIn-Hand.apk

One Bluetooth device cannot connect to multiple apps at the same time, when testing, be sure to completely close the running Dr.ReInHand or ReInHand first.

Version Control

Patient v3.11.10 & Clinician v3.11.10 _Latest

android {
    compileSdkVersion 34

    defaultConfig {
        applicationId "edu.northwestern.feinberg.clinician"
        minSdkVersion 29
        targetSdkVersion 33
        versionCode 31110
        versionName "3.11.10"
    }
}

Tested successfully on SAMSUNG Galaxy Tab A8 10.5” Android 12

Patient v3.1.3rts & Clinician v3.1.3x

android {
    compileSdkVersion 29
    buildToolsVersion "29.0.2"
    defaultConfig {
        applicationId "edu.northwestern.feinberg.clinician"
        minSdkVersion 27
        targetSdkVersion 29
        versionCode 310
        versionName "3.1.3x"
    }
}

Tested successfully on SAMSUNG Galaxy Tab S6 Lite Android 10