Detecting Falls with Wearable Sensors Using Machine Learning Techniques

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.

Investigation of Sensor Placement for Accurate Fall Detection

“…Each unit comprises three tri-axial devices (accelerometer, gyroscope, and magnetometer/compass) with respective ranges of ±120 m/s 2 , ±1200 o /s, and ±1.5 Gs, and an atmospheric pressure meter with 300-1100 hPa operating range, which we did not use. We recorded raw motion data along three perpendicular axes (x, y, z) from each unit with a sampling frequency of 25 Hz [11]. A set of trials consists of 20 fall actions (front-lying, frontprotection-lying, front-knees, front-knees-lying, front-right, front-left, front-quickrecovery, front-slow-recovery, back-sitting, back-lying, back-right, back-left, rightsideway, right-recovery, left-sideway, left-recovery, syncope, syncope-wall, podium, rolling-out-bed) and 16 ADLs (lying-bed, rising-bed, sit-bed, sit-chair, sit-sofa, sit-air, walking-forward, jogging, walking-backward, bending, bending-pick-up, stumble, limp, squatting-down, trip-over, coughing-sneezing).…”

“…To achieve this, we evaluate the activity and fall dataset acquired by Özdemir and Barshan [11] with respect to several classification algorithms using only the data acquired from a single sensor location each time. The classification performance in terms of accuracy is used as the criterion to reveal the optimal sensor location.…”

“…Machine learning techniques have been used to achieve more reliable results. Every recorded movement in the fall and activity database [11] has its own pattern. By extracting features from the raw data, these patterns can be classified by different classification methods.…”

“…Resolving this issue through experiments that follow standardized procedures will be a valuable contribution. Özdemir and Barshan [11] acquired data from sensors placed on six body parts including the head, chest, waist, wrist, thigh and ankle. In order to proceed with classification, the features extracted from each location are concatenated to a single feature vector leading to a high-dimensional feature space.…”

“…The multilayer perceptron yielded the best sensitivity (90.15%). Özdemir and Barshan [11] added autocorrelation coefficients and discrete Fourier transform (DFT) coefficients extracted from data acquired by sensors placed at different body parts. Six classifiers (k-NN, SVM, ANN, least-squares method, Bayesian decision making, dynamic time warping) were used to assign a fall or ADL class label to the feature vectors concatenated from all sensors.…”