Evaluation of time-frequency features as detectors of lack of balance due to tripping-like perturbations

Unbalancing events during gait can end up in falls and, thus, injury. Detecting events that could bring to fall and consequently activating fall prevention systems before the impact may help to mitigate related injuries. However, there is uncertainty about signals and methods that could offer the best performance. In this paper we investigated a novel trip detection method based on time-frequency features to evaluate the performances of these features as trip detectors. Hip angles of eight healthy young subjects were recorded while performing unexpected tripping trials delivered during steady locomotion. Then the Short-Time Fourier Transform (STFT) of the hip angle was estimated. Median frequency, power, centroidal frequency as well as frequency dispersion were computed for each time sliced power spectrum. These features were used as input for a trip detection algorithm. We assessed detection time (Tdetect), specificity (Spec) and sensitivity (Sens) for each feature. Performances obtained with median frequencies over time(Tdetect 0.91 +/- 0.47 s; Sens 0.96) were better than those obtained using the hip angle signal in time domain (Tdetect 1.19 +/- 0.27 s; Sens 0.83). Other features did not show significant results. Thus, median frequency over time expected to achieve effective real-time event detection systems, with the aim of a future on-board application concerning detection and prevention measures.


Published in:
2019 41St Annual International Conference Of The Ieee Engineering In Medicine And Biology Society (Embc), 2443-2446
Presented at:
41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, GERMANY, Jul 23-27, 2019
Year:
Jan 01 2019
Publisher:
New York, IEEE
ISSN:
1557-170X
ISBN:
978-1-5386-1311-5




 Record created 2020-09-12, last modified 2020-10-29


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