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• W2980012053 abstract "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." @default.
• W2980012053 created "2019-10-18" @default.
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• W2980012053 date "2019-07-01" @default.
• W2980012053 modified "2023-09-23" @default.
• W2980012053 title "Evaluation of time-frequency features as detectors of lack of balance due to tripping-like perturbations" @default.
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• W2980012053 doi "https://doi.org/10.1109/embc.2019.8857442" @default.
• W2980012053 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/31946392" @default.
• W2980012053 hasPublicationYear "2019" @default.
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