Gait Event Detection in Controlled and Real-Life Situations: Repeated Measures From Healthy Subjects

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 26; no. 10; pp. 1945 - 1956
• Main Authors: Figueiredo, Joana, Felix, Paulo, Costa, Luis, Moreno, Juan C., Santos, Cristina P.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: adaptive computational methods; Adult; Angular velocity; Benchmarking; Biomechanical Phenomena; Computer applications; daily locomotion activities; Eletrónica e Informática; Engenharia e Tecnologia; Engenharia Eletrotécnica; Event detection; Female; Finite Element Analysis; Foot; Foot - physiology; Gait; Gait - physiology; Gyroscopes; Healthy Volunteers; Heel - physiology; Human gait analysis; Humans; Legged locomotion; Male; Psychomotor Performance; real-time gait event detection; Real-time systems; Rehabilitation; Reproducibility of Results; Science & Technology; Sensors; Walking - physiology; Wearable Electronic Devices; wearable inertial sensors; Within-subjects design; Young Adult
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2018.2868094

A benchmark and time-effective computational method is needed to assess human gait events in real-life walking situations using few sensors to be easily reproducible. This paper fosters a reliable gait event detection system that can operate at diverse gait speeds and on diverse real-life terrains by detecting several gait events in real time. This detection only relies on the foot angular velocity measured by a wearable gyroscope mounted in the foot to facilitate its integration for daily and repeated use. To operate as a benchmark tool, the proposed detection system endows an adaptive computational method by applying a finite-state machine based on heuristic decision rules dependent on adaptive thresholds. Repeated measurements from 11 healthy subjects (28.27 ± 4.17 years) were acquired in controlled situations through a treadmill at different speeds (from 1.5 to 4.5 km/h) and slopes (from 0% to 10%). This validation also includes heterogeneous gait patterns from nine healthy subjects (27 ± 7.35 years) monitored at three self-selected paces (from 1 ± 0.2 to 2 ± 0.18 m/s) during forward walking on flat, rough, and inclined surfaces and climbing staircases. The proposed method was significantly more accurate (p > 0.9925) and time effective (c 30.53 ± 9.88 ms, p > 0.9314) in a benchmarking analysis with a state-of-the-art method during 5657 steps. Heel strike was the gait event most accurately detected under controlled (accuracy of 100%) and real-life situations (accuracy > 96.98%). Misdetection was more pronounced in middle mid swing (accuracy > 90.12%). The lower computational load, together with an improved performance, makes this detection system suitable for quantitative benchmarking in the locomotor rehabilitation field.