Abstract
Falls are some of the most common sources of injury among the elderly. A fall is particularly critical when the elderly person is injured and cannot call for help. This problem is addressed by many fall-detection systems, but they often focus on isolated falls under restricted conditions, neglecting complex, real-life situations. In this paper a combination of body-worn inertial and location sensors for fall detection is studied. A novel context-based method that exploits the information from both types of sensors is designed. The evaluation is performed on a real-life scenario, including fast falls, slow falls and fall-like situations that are difficult to distinguish from falls. All the possible combinations of six inertial and four location sensors are tested. The results show that: (i) context-based reasoning significantly improves the performance; (ii) a combination of two types of sensors in a single physical sensor enclosure seems to be the best practical solution.
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Gjoreski, H., Luštrek, M., Gams, M. (2012). Context-Based Fall Detection Using Inertial and Location Sensors. In: Paternò, F., de Ruyter, B., Markopoulos, P., Santoro, C., van Loenen, E., Luyten, K. (eds) Ambient Intelligence. AmI 2012. Lecture Notes in Computer Science, vol 7683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34898-3_1
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DOI: https://doi.org/10.1007/978-3-642-34898-3_1
Publisher Name: Springer, Berlin, Heidelberg
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