MediaPipe-based LSTM-Autoencoder Sarcopenia Anomaly Detection and Requirements for Improving Detection Accuracy

Abstract

MediaPipe is a leaning-based human pose detection technology that detects the position and movement of a person’s body, face, fingers, etc., from videos. Nowadays, many orthopedic studies put efforts into finding a biomarker of orthopedic diseases from the correlation between gait and orthopedic, using MediaPipe. This paper presents the results of applying the LSTM(Long Short Term Memory)-Autoencoder-based anomaly detection technique for orthopedic diseases, e.g., sarcopenia disease and the capability of distinguishing the normal and abnormal gait. We compare the sensitivity of the anomaly detection based on 5 human body points in predicting sarcopenia so as to find the primary gait features of human body. In addition, we present four environmental factors affecting MediaPipe Recognition that can improve the accuracy of anomaly detection using MediaPipe. Our anomaly detection approach detects 92% (35) of sarcopenia patients from 38 patients. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).