Smartphone IMU Sensors for Human Identification through Hip Joint Angle Analysis

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 15; p. 4769
• Main Authors: Andersson, Rabé, Bermejo-García, Javier, Agujetas, Rafael, Cronhjort, Mikael, Chilo, José
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.08.2024
• Subjects: Accelerometry - instrumentation; Accelerometry - methods; Adult; Algorithms; Data mining; Female; Gait; Gait - physiology; Gait Analysis - instrumentation; Gait Analysis - methods; Hip Joint - physiology; human motion analysis; Humans; IMU sensors; Machine Learning; machine learning classification; Male; Neural networks; person recognition; Sensors; Smart phones; Smartphone; smartphone sensors; Smartphones; Walking - physiology; Young Adult; Taiwan; person recognition; IMU sensors; machine learning classification; smartphone sensors; human motion analysis
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24154769

Gait monitoring using hip joint angles offers a promising approach for person identification, leveraging the capabilities of smartphone inertial measurement units (IMUs). This study investigates the use of smartphone IMUs to extract hip joint angles for distinguishing individuals based on their gait patterns. The data were collected from 10 healthy subjects (8 males, 2 females) walking on a treadmill at 4 km/h for 10 min. A sensor fusion technique that combined accelerometer, gyroscope, and magnetometer data was used to derive meaningful hip joint angles. We employed various machine learning algorithms within the WEKA environment to classify subjects based on their hip joint pattern and achieved a classification accuracy of 88.9%. Our findings demonstrate the feasibility of using hip joint angles for person identification, providing a baseline for future research in gait analysis for biometric applications. This work underscores the potential of smartphone-based gait analysis in personal identification systems.