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Context-based fall detection and activity recognition using inertial and location sensors


Accidental 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, not paying enough attention to complex, real-life situations. To achieve robust performance in real life, a combination of body-worn inertial and location sensors for fall detection is studied in this paper. A novel context-based method that exploits the information from the both types of sensors is designed. It considers body accelerations, location and elementary activities to detect a fall. The recognition of the activities is of great importance and also is the most demanding of the three, thus it is treated as a separate task. 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 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 is the best practical solution.