Detecting falls with wearable sensors using machine learning techniques.

Ahmet Özdemir,Billur Barshan

doi:10.3390/s140610691

Ahmet Özdemir, Billur Barshan

Open Access

https://doi.org/10.3390/s140610691

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Abstract

Falls are a serious public health problem and possibly life threatening for people in fall risk groups. We develop an automated fall detection system with wearable motion sensor units fitted to the subjects' body at six different positions. Each unit comprises three tri-axial devices (accelerometer, gyroscope, and magnetometer/compass). Fourteen volunteers perform a standardized set of movements including 20 voluntary falls and 16 activities of daily living (ADLs), resulting in a large dataset with 2520 trials. To reduce the computational complexity of training and testing the classifiers, we focus on the raw data for each sensor in a 4 s time window around the point of peak total acceleration of the waist sensor, and then perform feature extraction and reduction. Most earlier studies on fall detection employ rule-based approaches that rely on simple thresholding of the sensor outputs. We successfully distinguish falls from ADLs using six machine learning techniques (classifiers): the k-nearest neighbor (k-NN) classifier, least squares method (LSM), support vector machines (SVM), Bayesian decision making (BDM), dynamic time warping (DTW), and artificial neural networks (ANNs). We compare the performance and the computational complexity of the classifiers and achieve the best results with the k-NN classifier and LSM, with sensitivity, specificity, and accuracy all above 99%. These classifiers also have acceptable computational requirements for training and testing. Our approach would be applicable in real-world scenarios where data records of indeterminate length, containing multiple activities in sequence, are recorded.

Highlights

With the world’s aging population, health-enabling technologies and ambulatory monitoring of the elderly has become a prominent area of multi-disciplinary research [1,2]
Because the initial set of features was quite large (1404) and not all features were useful in discriminating between the falls and ADLs, to reduce the computational complexity of training and testing the classifiers, we reduced the number of features from 1404 to M = 30 through principal component analysis (PCA) [28] and normalized the resulting features between 0 and 1
The k-nearest neighbor (k-NN) has 100% sensitivity, indicating that falls are not missed with this method; two to three ADLs were misclassified over 2520 trials in 10 rounds (Table 3)

Summary

Introduction

With the world’s aging population, health-enabling technologies and ambulatory monitoring of the elderly has become a prominent area of multi-disciplinary research [1,2]. An important aspect of context-aware systems is recognizing, interpreting, and monitoring the basic activities of daily living (ADLs) such as standing, sitting, lying down, walking, ascending/descending stairs, and most importantly, emergent events such as falls. If a sudden change in the center of mass of the human body results in a loss of balance, the person falls. Falls are a public health problem and a health threat, especially for adults of age 65 and older [4]. Statistics indicate that one in every three adults of age or older experiences at least one fall every year. Children, disabled individuals, workers, athletes, and patients with visual, balance, gait, orthopedic, neurological, and psychological disorders suffer from falls. The intrinsic factors associated with falls are aging, mental impairment, neurological and orthopedic diseases, vision and balance disorders. Since the consequences of falls can be serious and costly, falls should be detected reliably and promptly to reduce the occurrence of related injuries and the costs of healthcare

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