Fall detection and classification using wearable motion sensors

Abstract

Effective fall-detection systems are vital in mitigating severe medical and economical consequences of falls to people in the fall risk groups. One class of such systems is wearable sensor based fall-detection systems. While there is a vast amount of academic work on this class of systems, the literature still lacks effective and robust algorithms and comparative evaluation of state-of-the-art algorithms on a common basis, using an extensive dataset. In this thesis, falldetection and fall direction classification systems that use a motion sensor unit, worn at the waist of the subject, are presented. A comparison of a variety of falldetection algorithms on an extensive dataset, comprising a total of 2880 trials, is undertaken. A novel heuristic fall-detection algorithm (fuzzy-augmented double thresholding: FADoTh) using two simple features is proposed and compared to 15 state-of-the-art heuristic fall-detection algorithms, among which it displays the highest average accuracy (98:45%), sensitivity, and F-measure values. A learner version of the same algorithm (k-NN classifier-augmented tree: kAT) is developed and compared to eight machine learning (ML) classifiers based on the same dataset: Bayesian decision making (BDM), least squares method (LSM), k-nearest neighbor classifier (k-NN), artificial neural networks (ANN), support vector machines (SVM), decision tree classifier (DTC), random forest (RF), and adaptive boosting (AdaBoost). The kAT algorithm yields an average accuracy of 98:85% and performs on par with BDM, k-NN, ANN, SVM, DTC, RF, and AdaBoost, whereas LSM produces inferior results. Finally, the same eight ML classifiers are implemented for fall direction classification into four basic directions (forward, backward, right, and left) and evaluated on a reduced version of the same dataset consisting of only fall trials. BDM achieves perfect classification, followed by k-NN, SVM, and RF. BDM, LSM, k-NN, and ANN are modified to work in the presence of data from an unknown class and evaluated on the reduced dataset. In this robustness analysis, ANN and k-NN yield accuracies above 96:2%. The results obtained in this study are promising in developing real-world fall-detection systems.