A Systematic Evaluation of Feature Encoding Techniques for Gait Analysis Using Multimodal Sensory Data

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 1; p. 75
• Main Authors: Fatima, Rimsha, Khan, Muhammad Hassan, Nisar, Muhammad Adeel, Doniec, Rafał, Farid, Muhammad Shahid, Grzegorzek, Marcin
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 22.12.2023; MDPI
• Subjects: Algorithms; Biomechanics; classification; Data mining; Datasets; Deep learning; feature encoding; Gait; Gait Analysis; human activity recognition; Human mechanics; Humans; Kinetics; Kurtosis; Locomotion; Machine learning; Methods; Neural networks; Sensors; Skewness; Smartphones; Time series; time series sensory data; Walking; New Jersey; Germany; feature encoding; classification; human activity recognition; gait analysis; time series sensory data

This paper addresses the problem of feature encoding for gait analysis using multimodal time series sensory data. In recent years, the dramatic increase in the use of numerous sensors, e.g., inertial measurement unit (IMU), in our daily wearable devices has gained the interest of the research commun...