Digital Biomarker for Muscle Function Assessment Using Surface Electromyography With Electrical Stimulation and a Non-Invasive Wearable Device.

Sarcopenia is a comprehensive degenerative disease with the progressive loss of skeletal muscle mass with age, accompanied by the loss of muscle strength and muscle dysfunction. Individuals with unmanaged sarcopenia may experience adverse outcomes. Periodically monitoring muscle function to detect muscle degeneration caused by sarcopenia and treating degenerated muscles is essential. We proposed a digital biomarker measurement technique using surface electromyography (sEMG) with electrical stimulation and wearable device to conveniently monitor muscle function at home. When motor neurons and muscle fibers are electrically stimulated, stimulated muscle contraction signals (SMCSs) can be obtained using an sEMG sensor. As motor neuron activation is important for muscle contraction and strength, their action potentials for electrical stimulation represent the muscle function. Thus, the SMCSs are closely related to muscle function, presumptively. Using the SMCSs data, a feature vector concatenating spectrogram-based features and deep learning features extracted from a convolutional neural network model using continuous wavelet transform images was used as the input to train a regression model for measuring the digital biomarker. To verify muscle function measurement technique, we recruited 98 healthy participants aged 20-60 years including 48 [49%] men who volunteered for this study. The Pearson correlation coefficient between the label and model estimates was 0.89, suggesting that the proposed model can robustly estimate the label using SMCSs, with mean error and standard deviation of -0.06 and 0.68, respectively. In conclusion, measuring muscle function using the proposed system that involves SMCSs is feasible.

Sarcopenia Diagnostic Technique Based on Artificial Intelligence Using Bio-signal of Neuromuscular System: A Proof-of-Concept Study.

In this paper, we propose an artificial intelligence (AI)-based sarcopenia diagnostic technique for stroke patients utilizing bio-signals from the neuromuscular system. Handgrip, skeletal muscle mass index, and gait speed are prerequisite components for sarcopenia diagnoses. However, measurement of these parameters is often challenging for most hemiplegic stroke patients. For these reasons, there is an imperative need to develop a sarcopenia diagnostic technique that requires minimal volitional participation but nevertheless still assesses the muscle changes related to sarcopenia. The proposed AI diagnostic technique collects motor unit responses from stroke patients in a resting state via stimulated muscle contraction signals (SMCSs) recorded from surface electromyography while applying electrical stimulation to the muscle. For this study, we extracted features from SMCS collected from stroke patients and trained our AI model for sarcopenia diagnosis. We validated the performance of the trained AI models for each gender against other diagnostic parameters. The accuracy of the AI sarcopenia model was 96%, and 95% for male and females, respectively. Through these results, we were able to provide preliminary proof that SMCS could be a potential surrogate biomarker to reflect sarcopenia in stroke patients.

Feasibility of Sarcopenia Diagnosis Using Stimulated Muscle Contraction Signal in Hemiplegic Stroke Patients.

Sarcopenia, a condition characterized by muscle weakness and mass loss, poses significant risks of accidents and complications. Traditional diagnostic methods often rely on physical function measurements like handgrip strength which can be challenging for affected patients, including those with stroke. To address these challenges, we propose a novel sarcopenia diagnosis model utilizing stimulated muscle contraction signals captured via wearable devices. Our approach achieved impressive results, with an accuracy of 93% and 100% in sarcopenia classification for male and female stroke patients, respectively. These findings underscore the significance of our method in diagnosing sarcopenia among stroke patients, offering a non-invasive and accessible solution.

An Exercise Prescription for Patients with Stroke and Sarcopenia Based on the Modified Delphi Study.

BACKGROUND: We aimed to develop a consensus on the need for and priorities of exercise to treat preexisting sarcopenia with hemiplegic stroke. METHODS: A modified three-round Delphi study was conducted. The panelists responded to the questionnaire on a 7-point Likert scale. Responses were returned with descriptive statistics in the next round. Consensus was defined as >75% agreement (score of 5-7) with a median > 5. The percentage of strong agreement (score of 6-7) and Kendall's coefficient of concordance were calculated to demonstrate a more refined interpretation of the consensus. RESULTS: Fifteen panelists contributed to all rounds. The need for exercise was demonstrated. The consensus was reached on 53 of 58 items in the first round and all items in the second and final rounds. The percentage of strong agreement was high for all but eight items. CONCLUSIONS: This study is the first Delphi study to investigate the need for and priorities of exercise for treating preexisting sarcopenia in stroke hemiplegia. We present a standard recommendation including 57 priorities and a strong recommendation including 49 priorities. The eight items that were excluded reflected factors that are less important to hemiplegic patients with poor balance, cognitive decline, or mental vulnerability.

Digital Biomarkers for Diagnosis of Muscle Disorders Using Stimulated Muscle Contraction Signal.

We propose a digital biomarker related to muscle strength and muscle endurance (DB/MS and DB/ME) for the diagnosis of muscle disorders based on a multi-layer perceptron (MLP) using stimulated muscle contraction. When muscle mass is reduced in patients with muscle-related diseases or disorders, measurement of DBs that are related to muscle strength and endurance is needed to suitably recover damaged muscles through rehabilitation training. Furthermore, it is difficult to measure DBs using traditional methods at home without an expert; moreover, the measuring equipment is expensive. Additionally, because traditional measurements depend on the subject's volition, we propose a DB measurement technique that is unaffected by the subject's volition. To achieve this, we employed an impact response signal (IRS) based on multi-frequency electrical stimulation (MFES) using an electromyography sensor. The feature vector was then extracted using the signal. Because the IRS is obtained from stimulated muscle contraction, which is caused by electrical stimulation, it provides biomedical information about the muscle. Finally, to estimate the strength and endurance of the muscle, the feature vector was passed through the DB estimation model learned through the MLP. To evaluate the performance of the DB measurement algorithm, we collected the MFES-based IRS database for 50 subjects and tested the model with quantitative evaluation methods using the reference for the DB. The reference was measured using torque equipment. The results were compared with the reference, indicating that it is possible to check for muscle disorders which cause decreased physical performance using the proposed algorithm.

Therapeutic Effects of Functional Electrical Stimulation on Physical Performance and Muscle Strength in Post-stroke Older Adults: A Review.

Stroke-related disabilities cause poor physical performance, especially among older adults, and can lead to sarcopenia. Functional electrical stimulation (FES) has been used to improve physical performance in individuals with neurological disorders and increase muscle mass and strength to counteract muscle atrophy. This review covers the principles, underlying mechanisms, and therapeutic effects of FES on physical performance and skeletal muscle function in post-stroke older adults. We found that FES restored weakened dorsiflexor and hip abductor strength during the swing and stance phases of gait, respectively, to help support weight-bearing and upright posture and facilitate static and dynamic balance in this population. FES may also be effective in improving muscle mass and strength to prevent muscle atrophy. However, previous studies on this topic in post-stroke older adults are scarce, and further studies are needed to confirm this supposition.

Home-based rehabilitation using smart wearable knee exercise device with electrical stimulation after anterior cruciate ligament reconstruction: A study protocol for a randomized controlled trial.

INTRODUCTION: Rehabilitation after anterior cruciate ligament (ACL) reconstruction is critical to patient outcome. Despite its importance; however, hospital-based rehabilitation is limited, with barriers, including distance and cost. With recent technological advancements, wearable devices have actively been used to address these barriers. In this study, we propose a randomized controlled trial protocol investigating the efficacy and feasibility of home-based rehabilitation after ACL reconstruction using a smart wearable device providing electrical stimulation that allows knee exercise. METHODS AND ANALYSIS: This is a protocol proposal for a prospective, single-center, randomized, controlled study. We plan to recruit adults discharged after ACL reconstruction; the recruited subjects will be randomly allocated to 1 of 2 groups, using a computer-generated randomization method: the intervention (n = 20) or control group (n = 20). The intervention group will receive a 6-week home-based rehabilitation program using smart wearable device. The control group will undergo a 6-week self-exercise program as normal. The following outcomes will be assessed at baseline, 2 weeks, and 6 weeks post the 6-week intervention program: quadriceps strength of the affect side as measured by a dynamometer (primary outcome); range of motion; root mean square of quadriceps muscle using surface electromyography; knee function using questionnaire; quality of life; subject's satisfaction score using questionnaire; frequency and duration of exercise; and knee pain. An intention-to-treat analysis will be conducted for the primary outcome. DISCUSSION: This study is a prospective, single-center, randomized, controlled study. This study aims to research the feasibility and efficacy of a 6-week, structured home-based rehabilitation program for patients after ACL reconstruction using a smart wearable device. The findings of this study will help to establish a home-based rehabilitation program to better recovery in patients with ACL reconstruction. TRIAL REGISTRATION NUMBER: This protocol was registered in ClinicalTrials.gov, under the number NCT04079205.