A smartphone-based fall detection system
Pervasive and Mobile Computing
Wearable computing: accelerometers' data classification of body postures and movements
SBIA'12 Proceedings of the 21st Brazilian conference on Advances in Artificial Intelligence
Ensembles of multiple sensors for human energy expenditure estimation
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
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This paper presents an approach to fall detection with accelerometers that exploits posture recognition to identify postures that may be the result of a fall. Posture recognition as a standalone task was also studied. Nine placements of up to four sensors were considered: on the waist, chest, thigh and ankle. The results are compared to the results of a system using ultra wide band location sensors on a scenario consisting of events difficult to recognize as falls or non-falls. Three accelerometers proved sufficient to correctly recognize all the events except one(a slow fall). The location-based system was comparable to two accelerometers, except that it was able to recognize the slow fall because it resulted in lying outside the bed, whose location was known to the system. One accelerometer was able to recognize only the most clear-cut fall. Two accelerometers achieved over 90% accuracy of posture recognition, which was better than the location-based system. Chest and waist accelerometers proved best at both tasks, with the chest accelerometer having a slight advantage in posture recognition.