"This record contains the abstract of a research study titled "Design and Development of a Portable Sensor-Based System for MRC Muscle Strength Grading in Stroke Rehabilitation". The full manuscript is currently under preparation for submission to a peer-reviewed journal.This preprint is shared to establish authorship and research priority."Muscle strength assessment plays a critical role in stroke rehabilitation for evaluating functional recovery and guiding therapeutic interventions. The Medical Research Council (MRC) muscle grading system is widely used in clinical practice; however, manual MRC assessment is subjective and highly dependent on the experience of the examiner, leading to inter-observer variability. This study proposes the design and development of a portable, sensor-based wearable system for objective assessment of MRC muscle strength grades in stroke patients. The proposed system integrates surface electromyography (EMG) sensors to capture muscle activation, an inertial measurement unit (MPU6050) to record joint movement and orientation, and a force/pressure sensing mechanism to quantify resistance applied during higher MRC grades. Sensor data are acquired in real time using an Arduino Nano–based microcontroller and transmitted wirelessly via Bluetooth for visualization and analysis. Signal processing techniques are applied to extract relevant features related to muscle activation, joint range of motion, and resistance response. A grade-wise decision algorithm maps the processed sensor data to MRC grades (0–5), and the highest successfully achieved grade is assigned. Preliminary system development includes successful sensor integration, battery-powered operation, and real-time data acquisition with live graphical visualization. The proposed methodology aims to replicate clinical MRC testing conditions while providing objective, repeatable, and quantifiable assessment. Validation of the system will be carried out by comparing device-assigned MRC grades with manual grading performed by a physiotherapist, followed by statistical analysis using SPSS to evaluate correlation and agreement. The developed system has the potential to support clinicians, caregivers, and patients by enabling consistent monitoring of muscle strength recovery in both clinical and home-based rehabilitation settings."This abstract is shared as a preprint for academic dissemination and to establish priority. The full manuscript will be submitted to a peer - reviewed journal".
Sruti Manda (Sun,) studied this question.