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On-Line Fall Detection via Mobile Accelerometer Data

Abstract : Mobile devices have entered our daily life in several forms, such as tablets, smartphones, smartwatches and wearable devices, in general. The majority of those devices have built-in several motion sensors, such as accelerometers, gyroscopes, orientation and rotation sensors. The activity recognition or emergency event detection in cases of falls or abnormal activity conduce a challenging task, especially for elder people living independently in their homes. In this work, we present a methodology capable of performing real time fall detect, using data from a mobile accelerometer sensor. To this end, data taken from the 3-axis accelerometer is transformed using the Incremental Principal Components Analysis methodology. Next, we utilize the cumulative sum algorithm, which is capable of detecting changes using devices having limited CPU power and memory resources. Our experimental results are promising and indicate that using the proposed methodology, real time fall detection is feasible.
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Submitted on : Friday, October 21, 2016 - 11:37:39 AM
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S. V. Georgakopoulos, S. K. Tasoulis, I. Maglogiannis, V. P. Plagianakos. On-Line Fall Detection via Mobile Accelerometer Data. 11th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI 2015), Sep 2015, Bayonne, France. pp.103-112, ⟨10.1007/978-3-319-23868-5_8⟩. ⟨hal-01385348⟩



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