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2024 (engelsk)Inngår i: Sensors, E-ISSN 1424-8220, Vol. 24, nr 15, artikkel-id 4769Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]
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.
sted, utgiver, år, opplag, sider
MDPI, 2024
Emneord
smartphone sensors; IMU sensors; person recognition; machine learning classification; human motion analysis
HSV kategori
Identifikatorer
urn:nbn:se:hig:diva-45273 (URN)10.3390/s24154769 (DOI)001287010600001 ()39123816 (PubMedID)2-s2.0-85200860251 (Scopus ID)
Prosjekter
PID2022-1375250B-C21
2024-07-252024-07-252024-10-04bibliografisk kontrollert