Investigation of Sensor Placement for Accurate Fall Detection
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Fall detection is typically based on temporal and spectral analysis of multi-dimensional signals acquired from wearable sensors such as tri-axial accelerometers and gyroscopes which are attached at several parts of the human body. Our aim is to investigate the location where such wearable sensors should be placed in order to optimize the discrimination of falls from other Activities of Daily Living (ADLs). To this end, we perform feature extraction and classification based on data acquired from a single sensor unit placed on a specific body part each time. The investigated sensor locations include the head, chest, waist, wrist, thigh and ankle. Evaluation of several classification algorithms reveals the waist and the thigh as the optimal locations.
KeywordsFall detection Fall classification Wearable sensors Sensor placement Machine learning Classification Accelerometers Gyroscopes
This work was supported by the FrailSafe project funded from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 690140. The paper reflects only the view of the authors and the Commission is not responsible for any use that may be made of the information it contains.
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