Wearables and detection of falls : a comparison of machine learning methods and sensors positioning.
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2022
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Resumo
Wearable sensors have many applications to provide assistance for older adults. We aimed
to identify the best combination of machine learning algorithms and body regions to attach
one wearable for real-time falls detection from a public dataset where volunteers performed
daily activities and simulated falls. Accuracy and comfort of the combination of wearables
and algorithms were assessed. Raw data from the accelerometer and gyroscope were used
for both training and testing stages. We evaluated the confusion matrix between all wear-
ables at each of the different body regions (Ankle, Right Pocket, Belt, Neck, and Wrist) for
the following machine learning algorithms: Multilayer Perceptron (MLP), Random Forest,
XGBoost, and Long Short Term Memory (LSTM) deep neural network. The accuracy was
compared by ANOVA two-way repeated measures statistical test. This work has two main
technical contributions. First, our results demonstrated the highest accuracy in identifying
falls when the sensors were positioned on the neck or ankle. Second, when the machine learning algorithms to detect fall was compared, LSTM deep neural network and Random Forest
showed statistically higher accuracy than MLP and XGBoost. Besides, a comfort analysis
based on the literature concluded that neck and wrist are the most comfortable regions to
wear wearables.
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Fall detection, Inertial sensors
Citação
ARTHUR, B. A. et al. Wearables and detection of falls: a comparison of machine learning methods and sensors positioning. Neural Processing Letters, v. 54, p. 2165-2179, 2022. Disponível em: <https://link.springer.com/article/10.1007/s11063-021-10724-2>. Acesso em: 29 abr. 2022.