Detecting Falls with Wearable Sensors Using Machine Learning Techniques
| dc.citation.epage | 10708 | en_US |
| dc.citation.issueNumber | 6 | en_US |
| dc.citation.spage | 10691 | en_US |
| dc.citation.volumeNumber | 14 | en_US |
| dc.contributor.author | Ozdemir, A. T. | en_US |
| dc.contributor.author | Barshan, B. | en_US |
| dc.date.accessioned | 2015-07-28T12:01:18Z | |
| dc.date.available | 2015-07-28T12:01:18Z | |
| dc.date.issued | 2014-06-18 | en_US |
| dc.department | Department of Electrical and Electronics Engineering | en_US |
| dc.description.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. | en_US |
| dc.identifier.doi | 10.3390/s140610691 | en_US |
| dc.identifier.issn | 1424-8220 | |
| dc.identifier.uri | http://hdl.handle.net/11693/12404 | |
| dc.language.iso | English | en_US |
| dc.publisher | MDPI | en_US |
| dc.relation.isversionof | http://dx.doi.org/10.3390/s140610691 | en_US |
| dc.source.title | Sensors | en_US |
| dc.subject | Fall Detection | en_US |
| dc.subject | Activities Of Daily Living | en_US |
| dc.subject | Wearable Motion Sensors | en_US |
| dc.subject | Machine Learning | en_US |
| dc.subject | Pattern Classification | en_US |
| dc.subject | Feature Extraction And Reduction | en_US |
| dc.title | Detecting Falls with Wearable Sensors Using Machine Learning Techniques | en_US |
| dc.type | Article | en_US |
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