Study of the Detection of Falls Using the SVM Algorithm, Different Datasets of Movements and ANOVA

Santoyo-Ramón, José Antonio; Casilari, E.; Cano-García, José Manuel

doi:10.1007/978-3-030-17938-0_37

Cited by 4 publications

(1 citation statement)

References 22 publications

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“…reduce the false alarm. 37 We have presented a review on recent works which employs various ML algorithm to classify fall from nonfall. Figure 2 gives the summary of FDS.…”

Section: Fall Detection Methodsmentioning

confidence: 99%

A systematic review on machine learning for fall detection system

Rastogi

Singh

2021

Computational Intelligence

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Fall is a major threat to the health and life of the elders. A Fall Detection System (FDS) assist the elders by identifying the fall and save their life. Machine Learning-(ML) based FDS has turned into a major research area due to its capability to assist the elders automatically. The efficiency of a FDS depends on its strength to identify the fall from nonfall accurately. The initial fall detection scheme depends on the threshold-based classification to classify the fall from the Activity of Daily Living (ADL) but this classification method has failed to reduce the false alarm rate, which raises a question on the efficiency of the technique. This review work identifies the problems in threshold-based classification from existing works and finds the need for an efficient ML-based classification technique to accurately identify the fall. Then, presents a comprehensive literature review on various ML-based classification in fall detection. Moreover, the scrutiny investigates the shortcomings associated with the ML-based techniques for future research. This study finds that present ML-based FDS has not addressed problems like data preprocessing and data dimensionality reduction techniques even though ML-based techniques are far superior to threshold-based techniques.The study concludes that Self-Adaptive-based FDS, as

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“…reduce the false alarm. 37 We have presented a review on recent works which employs various ML algorithm to classify fall from nonfall. Figure 2 gives the summary of FDS.…”

Section: Fall Detection Methodsmentioning

confidence: 99%

A systematic review on machine learning for fall detection system

Rastogi

Singh

2021

Computational Intelligence

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Features Selection for Fall Detection Systems Based on Machine Learning and Accelerometer Signals

Silva

García−Bermúdez

Casilari

2021

Advances in Computational Intelligence

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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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Study of the Detection of Falls Using the SVM Algorithm, Different Datasets of Movements and ANOVA

Cited by 4 publications

References 22 publications

A systematic review on machine learning for fall detection system

A systematic review on machine learning for fall detection system

Features Selection for Fall Detection Systems Based on Machine Learning and Accelerometer Signals

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

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