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2019.11.33

Before applying

Kalman filtering, the false positives for the case of falls were 60% of the total classified instances, and after applying Kalman filtering were minimized to 33%, respectively. For annotation purposes, the three movement types were associated with three integers: 1 for walk; 2 for run; and 3 for fall, respectively. Actual run and fall events are also annotated on the diagrams. For each experiment, two different diagrams were generated: one illustrating classification results based exclusively on acceleration data and one illustrating classification based on both acceleration and sound data. As it is indicated, Kalman filtering improves the overall detection by smoothing the sequential occurrences of run or fall events, respectively. In addition, the use of sound as additional classification feature has increased the accuracy of fall detections by minimizing the false ones in cases of simple walk and of walk and run. A threshold t = 10 has been selected for determining the occurrence of fall or run events from the total sequence of classified movement types (i.e., if sequential occurrence of fall movement types >10, then a fall is detected).

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