Abstract
Histogram features are extracted by calculating the distribution of orientations of small fragments or quanta of sliding windows on the sensors continuously acceleration data stream. Bins of the histogram is automatically computed based on clusters of similar orientations of quanta, making it less sensitive to parameters used in selection of bins than a heuristic approach. We also present a finer representation of the sliding window by applying the above extraction method to extract local feature vectors of small data segments instead of calculating features from the whole sliding window. Extracted features are used with support vector machines trained to classify frames of data streams into containing falls or non-falls. We evaluated the proposed method on three public datasets with acceleration data including falls and other activities of daily living. On all three datasets, performance of the proposed method is substantially higher than two other fall detection methods.
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Diep, N.N., Pham, C., Phuong, T.M. (2016). An Orientation Histogram Based Approach for Fall Detection Using Wearable Sensors. In: Booth, R., Zhang, ML. (eds) PRICAI 2016: Trends in Artificial Intelligence. PRICAI 2016. Lecture Notes in Computer Science(), vol 9810. Springer, Cham. https://doi.org/10.1007/978-3-319-42911-3_30
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