TY  - JOUR
A1  - Leer, Alexandra
A1  - Santa Cruz, Beatriz Garcia
A1  - Hertel, Frank
A1  - Koch, Klaus P.
A1  - Bremm, Rene Peter
T1  - Design of an experimental platform of gait analysis with ActiSense and StereoPi
T2  - Current Directions in Biomedical Engineering
N2  - Gait analysis is a systematic study of human movement. Combining wearable foot pressure sensors and machine learning (ML) solutions for a high-fidelity body pose tracking from RGB video frames could reveal more insights into gait abnormalities. However, accurate detection of heel strike (HS) and toe-off (TO) events is crucial to compute interpretable gait parameters. In this work, we present an experimental platform to study the timing of gait events using a new wearable foot pressure sensor (ActiSense System, IEE S.A., Luxembourg), and Google’s open-source ML solution MediaPipe Pose. For this purpose, two StereoPi systems were built to capture stereoscopic videos and images in real time. MediaPipe Pose was applied to the synchronized StereoPi cameras, and two algorithms (ALs) were developed to detect HS and TO events for gait and analysis. Preliminary results from a healthy subject walking on a treadmill show a mean relative deviation across all time spans of less than 4% for the ActiSense device and less than 16% for AL2 (33% for AL1) employing MediaPipe Pose on StereoPi videos. Finally, this work offers a platform for the development of sensor- and video-based ALs to automatically identify the timing of gait events in healthy individuals and those with gait disorders.
KW  - human gait
KW  - risk of falls
KW  - computer vision
KW  - pose estimation
KW  - stereoscopic cameras
KW  - foot pressure sensors
KW  - Gehen
KW  - Ganganalyse
KW  - Pedografie
KW  - Maschinelles Sehen
KW  - Stereokamera
Y1  - 2022
UR  - https://hst.opus.hbz-nrw.de/frontdoor/index/index/docId/151
UR  - https://nbn-resolving.org/urn:nbn:de:hbz:tr5-1511
VL  - 8
IS  - 2
SP  - 572
EP  - 575
PB  - De Gruyter
ER  -