![]() ![]() Features were extracted from the environmental sensor data and subsets selected using correlation-based feature selection. For this study, measurements of carbon dioxide, volatile organic compounds, air temperature, and relative air humidity were conducted in four student apartments for a total of 49 days. Environmental sensors, as opposed to motion detectors, are nonintrusive, easy to install, low cost, detect nonmoving occupants, do not have dead spots, and can even infer the number of occupants. ![]() To detect and count occupants, an approach using a fusion of environmental sensors from an indoor air quality measurement system is presented. It is demonstrated that the examined activity set can be discriminated by fusing the data of either the accelerometer and the barometer or the gyroscope and the barometer. The potential of magnetic field sensing is derived from the signal characteristics of the magnetic interference as experienced during cycling and riding an elevator. It is also shown that the barometer allows discriminating activities that involve a vertical displacement, such as climbing stairs or riding an elevator, as well as helps to reduce the false alarm rate of a fall detector. ![]() 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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