Sizhen Bian

• The development of machine learning algorithms and novel sensing modalities has boosted the exploration of human activity recognition(HAR) in recent years. In this work, we explored field-based sensing solutions and different machine learning models for HAR tasks to address the shortcomings of existing HAR sensing solutions, like the weak robustness of RF-based solution, environment-dependency of the optic-based solution, etc., aiming to supply a competitive and alternative sensing approach for HAR tasks. Field, in physics, describes a region in which each point will be affected by force. Field sensing is potentially a low-cost, low-power, non-intrusive, privacy-respecting HAR solution that is ideal for long-term, wearable activity recording. By directly/indirectly monitoring the field strength or other field variation caused variables, some unsolved HAR problems could be addressed when other sensing solutions fail. An example is the social distance monitoring problem, where the most widely adopted approach is based on the Bluetooth signal strength measurement. However, the signal is so subtle that any object surrounding the signal emitter will cause signal attenuation. To guarantee the accuracy of social distance monitoring, we developed an induced magnetic field-based social distance monitoring system with an accuracy of a sub-ten centimetre. Moreover, the system is robust and resistant to environmental variations. Like Bluetooth, other RF-wave-based sensing modalities also face the multi-path effect caused by refraction. Thus their signal is unreliable for positioning applications where higher accuracy and robustness are needed. Besides the magnetic field, we also explored a natural static passive electric field, the field between the human body and surroundings, namely the human body capacitance(HBC). HBC is a physiological parameter describing the charge distribution difference between the body and the surroundings and is seldomly explored before. We developed a few wearable, low-cost, low power consumption hardware platforms, either based on an oscillating unit or discrete components composed sensing front end followed by a high resolution analog-to-digital module, to monitor the variation of the parameter regarding the body movement and environmental variations. Compared with the inertial sensors, the HBC could deliver full-body movement perceiving, meaning that the movement of the legs could be perceived by a wrist-worn HBC sensing unit, which is far beyond the sensing ability of an inertial sensing unit. To summarize, we introduced two competitive field sensing modalities for HAR tasks, the magnetic field sensing for position-related services and the passive electric field sensing for full-body action and environmental variation sensing. Both of which were still in an infant stage and not fully explored in the community. The advantages of the two field sensing modalities were demonstrated with a series of position-related and motion-related experiments.