Wearables and detection of falls : a comparison of machine learning methods and sensors positioning.

Abstract

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.