Fall Detection System Design by Smart Phone

Wu, Yung-Gi; Tsai, Sheng-Lun

doi:10.17781/p001343

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…A similar approach is followed by Rungnapakan et al in 35 as the threshold values to detect the impact force of the falls were set separately for each weight range of the experimental users. Wu and Tsai present in 36 another smartphone-based FDS using a threshold-based algorithm in which the threshold level is tuned and ‘heuristically’ adapted for each user. Age, weight, height and level of activity of the user are considered into the equation that computes the decision threshold in the SP-based FDS presented by Sposaro in 37 .…”

Section: Related Workmentioning

confidence: 99%

“…Age, weight, height and level of activity of the user are considered into the equation that computes the decision threshold in the SP-based FDS presented by Sposaro in 37 . Wu and Tsai propose in 36 an adaptive threshold based on user’s gender to detect falls based on the energy of the movements calculated from the DCT (Discrete Cosine Transform) of the acceleration magnitude. In all these works, authors do not evaluate the advantages (e.g.…”

Section: Related Workmentioning

confidence: 99%

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A study on the impact of the users’ characteristics on the performance of wearable fall detection systems

Santoyo-Ramón

Casilari

Cano-García

2021

Sci Rep

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Wearable Fall Detection Systems (FDSs) have gained much research interest during last decade. In this regard, Machine Learning (ML) classifiers have shown great efficiency in discriminating falls and conventional movements or Activities of Daily Living (ADLs) based on the analysis of the signals captured by transportable inertial sensors. Due to the intrinsic difficulties of training and testing this type of detectors in realistic scenarios and with their target audience (older adults), FDSs are normally benchmarked against a predefined set of ADLs and emulated falls executed by volunteers in a controlled environment. In most studies, however, samples from the same experimental subjects are used to both train and evaluate the FDSs. In this work, we investigate the performance of ML-based FDS systems when the test subjects have physical characteristics (weight, height, body mass index, age, gender) different from those of the users considered for the test phase. The results seem to point out that certain divergences (weight, height) of the users of both subsets (training ad test) may hamper the effectiveness of the classifiers (a reduction of up 20% in sensitivity and of up to 5% in specificity is reported). However, it is shown that the typology of the activities included in these subgroups has much greater relevance for the discrimination capability of the classifiers (with specificity losses of up to 95% if the activity types for training and testing strongly diverge).

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%