Wearable technology for the automatic detection of gait events has recently gained growing interest, enabling advanced analyses that were previously limited to specialist centres and equipment (e.g., instrumented walkway). In this study, we present a novel method based on dilated convolutions for an accurate detection of gait events (initial and final foot contacts) from wearable inertial sensors. A rich dataset has been used to validate the method, featuring 71 people with Parkinson's disease (PD) and 67 healthy control subjects. Multiple sensors have been considered, one located on the fifth lumbar vertebrae and two on the ankles. The aims of this study were: (i) to apply deep learning (DL) techniques on wearable sensor data for gait segmentation and quantification in older adults and in people with PD; (ii) to validate the proposed technique for measuring gait against traditional gold standard laboratory reference and a widely used algorithm based on wavelet transforms (WT); (iii) to assess the performance of DL methods in assessing high-level gait characteristics, with focus on stride, stance and swing related features. The results showed a high reliability of the proposed approach, which achieves temporal errors considerably smaller than WT, in particular for the detection of final contacts, with an inter-quartile range below 70 ms in the worst case. This study showes encouraging results, and paves the road for further research, addressing the effectiveness and the generalization of data-driven learning systems for accurate event detection in challenging conditions.

Gadaleta, M., Cisotto, G., Rossi, M., Ur Rehman, R., Rochester, L., Del Din, S. (2019). Deep Learning Techniques for Improving Digital Gait Segmentation. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (pp.1834-1837). IEEE [10.1109/EMBC.2019.8856685].

Deep Learning Techniques for Improving Digital Gait Segmentation

Cisotto, Giulia
Secondo
;
2019

Abstract

Wearable technology for the automatic detection of gait events has recently gained growing interest, enabling advanced analyses that were previously limited to specialist centres and equipment (e.g., instrumented walkway). In this study, we present a novel method based on dilated convolutions for an accurate detection of gait events (initial and final foot contacts) from wearable inertial sensors. A rich dataset has been used to validate the method, featuring 71 people with Parkinson's disease (PD) and 67 healthy control subjects. Multiple sensors have been considered, one located on the fifth lumbar vertebrae and two on the ankles. The aims of this study were: (i) to apply deep learning (DL) techniques on wearable sensor data for gait segmentation and quantification in older adults and in people with PD; (ii) to validate the proposed technique for measuring gait against traditional gold standard laboratory reference and a widely used algorithm based on wavelet transforms (WT); (iii) to assess the performance of DL methods in assessing high-level gait characteristics, with focus on stride, stance and swing related features. The results showed a high reliability of the proposed approach, which achieves temporal errors considerably smaller than WT, in particular for the detection of final contacts, with an inter-quartile range below 70 ms in the worst case. This study showes encouraging results, and paves the road for further research, addressing the effectiveness and the generalization of data-driven learning systems for accurate event detection in challenging conditions.
Si
paper
gait; gait segmentation; gait events; walking; deep learning; analysis;
English
41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - JUL 23-27, 2019
978-153861311-5
https://ieeexplore.ieee.org/document/8856685
Gadaleta, M., Cisotto, G., Rossi, M., Ur Rehman, R., Rochester, L., Del Din, S. (2019). Deep Learning Techniques for Improving Digital Gait Segmentation. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (pp.1834-1837). IEEE [10.1109/EMBC.2019.8856685].
Gadaleta, M; Cisotto, G; Rossi, M; Ur Rehman, R; Rochester, L; Del Din, S
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/367506
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