Fall detection and classification using wearable motion sensors
Author(s)
Advisor
Barshan, BillurDate
2017-08Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
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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.
Keywords
Wearable sensorsMotion sensors
Fall detection
Fall classification
Fall-detection algorithms
Heuristic (rule-based) methods
Machine learning