Machine Learning Architectures to Classify Activities of Daily Living and Fall Types From Wearable Accelerometer Data

• Published in: IEEE Signal Processing in Medicine and Biology Symposium pp. 1 - 6
• Main Author: Antonietti, A.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 02.12.2023
• Subjects: Accelerometers; CatBoost; Classification algorithms; Computational efficiency; Generalizability; IMU; Inertial sensors; Random Forest; Real-time systems; Signal processing; Signal processing algorithms; Support Vector Machine; Support vector machine classification

This study compares different machine learning (ML) approaches to classify motion-based activities of daily living based on accelerometer measurements, focusing on identifying different types of falls. Falls are a significant cause of injuries and deaths in older adults, emphasizing the need for rea...