Dual-modality Smart Shoes for Quantitative Assessment of Hemiplegic Patients' Lower Limbs' Muscle Strength

Stroke can lead to the impaired motor ability of the patient's lower limbs and hemiplegia. Accurate assessment of the lower limbs' motor ability is important for diagnosis and rehabilitation. To digitalize such assessment so that each test can be traced back any time and subjectivity can be avoided, we test how dual-modality smart shoes equipped with pressure-sensitive insoles and inertial measurement units can be used for this purpose. A 5m walking test protocol, including the left and right turns, is designed. Data are collected from 23 patients and 17 healthy subjects. For the lower limbs' motor ability, the tests are observed by two physicians and assessed using the five graded Medical Research Council scale for muscle examination. The average of two physicians' scores for the same patient is used as the ground truth. Using the feature set we developed, 100% accuracy is achieved in classifying the patients and healthy subjects. For patients' muscle strength, a mean absolute error of 0.143 and a maximum error of 0.395 is achieved using our feature set and the regression method, closer to the ground truth than the scores from each physician (mean absolute error: 0.217, maximum error: 0.5). We thus validate the possibility of using such smart shoes to objectively and accurately evaluate the lower limbs' muscle strength of the stroke patients.