Assessment of human activity and posture with triaxial accelerometers provides insightful information about the functional ability: classification of human activities in rehabilitation and elderly surveillance contexts has been already proposed in the literature. In the meanwhile, recent technological advances allow developing miniaturized wearable devices, integrated within garments, which may extend this assessment to novel tasks, such as real-time remote surveillance of workers and emergency operators intervening in harsh environments. We present an algorithm for human posture and activity-level detection, based on the real-time processing of the signals produced by one wearable triaxial accelerometer. The algorithm is independent of the sensor orientation with respect to the body. Furthermore, it associates to its outputs a "reliability" value, representing the classification quality, in order to launch reliable alarms only when effective dangerous conditions are detected. The system was tested on a customized device to estimate the computational resources needed for real-time functioning. Results exhibit an overall 96.2% accuracy when classifying both static and dynamic activities.

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We present a new electronic insole for wireless monitoring of motor activities and shoe comfort. The proposed device, equipped with both ZigBee transmission and local data storage allows unobtrusive, long term monitoring of subjects outside the laboratory, during natural behavior activites, such as daily living and sports. The system detailed in this work includes humidity and temperature sensors, as well as a three axis accelerometer and four pressure sensors, all fitted within a 3.7 mm thick insole. Preliminary experiments have shown that the device is reliable and may be worn without causing discomfort even for long periods of time, suggesting that it could be useful in applications ranging from ergonomics studies on footwear to sports and rehabilitation.

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