![]() ![]() Using the acceleration data obtained at the wrist, various methods for detecting cycling are presented. ![]() An approach is introduced by which a person's energy expenditure can be estimated independently of the measurement position. Four volunteers participated in collecting a first dataset of 190 measurements by performing ten everyday activities. In preparation to this thesis, a wrist-worn 10-axis human activity tracker combining a 3-axis accelerometer, a 3-axis gyroscope, a 3-axis magnetometer, and a barometric pressure sensor was developed. ![]() This thesis investigates the recognition of human activities from the position of the wrist and the feasibility of occupancy detection based on environmental sensors. ![]()
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