High Accuracy Machine Learning Model for Sarcopenia Severity Diagnosis based on Sit-to-stand Motion Measured by Two Micro Motion Sensors

In this ageing society, sarcopenia as a geriatric condition that can have significant negative impacts on an individuals quality of life. Sarcopenia is a kind of aged syndrome associated with loss of muscle mass and function, which may lead to falls, fractures, gait disorders or even mortality. There are multiple ways to diagnose sarcopenia, such as using Magnetic resonance imaging (MRI), Dual-energy X-ray absorptiometry (DEXA) and Bioelectrical impedance analysis (BIA) etc. to calculate muscle mass; using handgrip or sit-to-stand to measure muscle strength; using short physical performance battery (SPPB), gait, and 5-time sit-to-stand to evaluate physical performance. In this work, we use two IMUs worn on subjects to record their sit-to-stand motion, and then used several machine learning models to diagnose the severity of sarcopenia of the subjects. We recruited 53 elderly subjects in total for this work. The youngest subject is 65 years old and the oldest is 84 years old. Their average age is 70 years old. Among these 53 subjects, there are 12 healthy ones and 41 sarcopenia patients with different severity. The subject is instructed to do the single sit-to-stand (STS) three times, and two IMUs attached to the subjects waist and thigh transfer the data to a computer by Bluetooth. We separated the STS motion process into 4 phases based on the angle and angular velocity, extracted a total of 510 features for motion analytics. These features were futher analyzed by sequential feature selection with 5 different machine learning models (SVM, KNN, decision tree, LDA, and multilayer perceptron). With our proposed methodology, all 53 subjects could be classified as healthy or having sarcopenia with risk level 1, 2, or 3. The best accuracy to distinguish the healthy or sarcopenia subjects is 98.32%, and the best results to distinguish sarcopenia risk levels from 0 (healthy) to 3 (most severe) is 90.44%.