Abstract
The continuous monitoring of human gait would allow to more objectively verify the abnormalities that arise from the most common pathologies. Therefore, this manuscript proposes a real-time tool for human gait detection from lower trunk acceleration. The vertical acceleration signal was acquired through an IMU mounted on a waistband, a wearable device. The proposed algorithm was based on a finite state machine (FSM) which includes a set of suitable decision rules and the detection of Heel-Strike (HS), Foot-flat (FF), Toe-off (TO), Mid-Stance (MS) and Heel-strike (HS) events for each leg. Results involved 7 healthy subjects which had to walk 20 m three times with a comfortable speed. The results showed that the proposed algorithm detects in real-time all the mentioned events with a high accuracy and time-effectiveness character. Also, the adaptability of the algorithm has also been verified, being easily adapted to some gait conditions, such as for different speeds and slopes. Further, the developed tool is modular and therefore can easily be integrated in another robotic control system for gait rehabilitation. These findings suggest that the proposed tool is suitable for the real-time gait analysis in real-life activities.
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Acknowledgments
This work is supported by the FCT Fundação para a Ciência e Tecnologia - with the reference project UID/EEA/04436/2013, by FEDER funds through the COMPETE 2020 - Programa Operacional Competitividade e Internacionalização (POCI) - with the reference project POCI-01-0145-FEDER-006941; and the LIACC Project PEst/UID/CEC/00027/2013.
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Gonçalves, H.R., Moreira, R., Rodrigues, A., Minas, G., Reis, L.P., Santos, C.P. (2018). Real-Time Tool for Human Gait Detection from Lower Trunk Acceleration. In: Rocha, Á., Adeli, H., Reis, L., Costanzo, S. (eds) Trends and Advances in Information Systems and Technologies. WorldCIST'18 2018. Advances in Intelligent Systems and Computing, vol 747. Springer, Cham. https://doi.org/10.1007/978-3-319-77700-9_2
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DOI: https://doi.org/10.1007/978-3-319-77700-9_2
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