Detecting Falls with Wearable Sensors Using Machine Learning Techniques

dc.citation.epage10708en_US
dc.citation.issueNumber6en_US
dc.citation.spage10691en_US
dc.citation.volumeNumber14en_US
dc.contributor.authorOzdemir, A. T.en_US
dc.contributor.authorBarshan, B.en_US
dc.date.accessioned2015-07-28T12:01:18Z
dc.date.available2015-07-28T12:01:18Z
dc.date.issued2014-06-18en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractFalls 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.doi10.3390/s140610691en_US
dc.identifier.issn1424-8220
dc.identifier.urihttp://hdl.handle.net/11693/12404
dc.language.isoEnglishen_US
dc.publisherMDPIen_US
dc.relation.isversionofhttp://dx.doi.org/10.3390/s140610691en_US
dc.source.titleSensorsen_US
dc.subjectFall Detectionen_US
dc.subjectActivities Of Daily Livingen_US
dc.subjectWearable Motion Sensorsen_US
dc.subjectMachine Learningen_US
dc.subjectPattern Classificationen_US
dc.subjectFeature Extraction And Reductionen_US
dc.titleDetecting Falls with Wearable Sensors Using Machine Learning Techniquesen_US
dc.typeArticleen_US

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