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 individual's 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 μIMUs 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 μIMUs attached to the subject's 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%. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study was funded by the Theme-based Research Scheme under The University Grants Committee [#T42-717/20-R]. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethics committee of The University of Hong Kong gave ethical approval for this work [EA1903040]. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors.