Automated detection of gait initiation and termination using wearable sensors.
This paper presents algorithms for detection of gait initiation and termination using wearable inertial measurement units and pressure-sensitive insoles. Body joint angles, joint angular velocities, ground reaction force and center of plantar pressure of each foot are obtained from these sensors and input into supervised machine learning algorithms. The proposed initiation detection method recognizes two events: gait onset (an anticipatory movement preceding foot lifting) and toe-off. The termination detection algorithm segments gait into steps, measures the signals over a buffer at the beginning of each step, and determines whether this measurement belongs to the final step. The approach is validated with 10 subjects at two gait speeds, using within-subject and subject-independent cross-validation. Results show that gait initiation can be detected timely and accurately, with few errors in the case of within-subject cross-validation and overall good performance in subject-independent cross-validation. Gait termination can be predicted in over 80% of trials well before the subject comes to a complete stop. Results also show that the two sensor types are equivalent in predicting gait initiation while inertial measurement units are generally superior in predicting gait termination. Potential use of the algorithms is foreseen primarily with assistive devices such as prostheses and exoskeletons.
Laboratory of Robotics, University of Ljubljana, Ljubljana, Slovenia. Electronic address: domen.novak@robo.fe.uni-lj.si.
Domen
D
Novak
Peter
P
Reberšek
Stefano Marco Maria
SM
De Rossi
Marco
M
Donati
Janez
J
Podobnik
Tadej
T
Beravs
Tommaso
T
Lenzi
Nicola
N
Vitiello
Maria Chiara
MC
Carrozza
Marko
M
Munih
D000328:Adult; D001331:Automation; D001696:Biomechanical Phenomena; D005684:Gait; D006801:Humans; D008297:Male; D018670:Monitoring, Ambulatory / Q000295:instrumentation*; D013997:Time Factors
Gait analysis; Machine learning; Motion intention detection; Wearable sensors