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Abstract
Transformative sensing is a new research trend that seeks to cross-fertilize sensing functionality in other ubiquitous pieces of electronics, thereby achieving pervasive sensing with ultra-low overhead. Examples include detecting human activity with kinetic energy harvesters embedded in wearable devices, recognizing hand gestures with Wi-Fi, and so on. This thesis makes three new contributions to transformative sensing, which involve gait detection, gesture recognition, and communication channel estimation. In the first contribution, the thesis devises solutions that enable use of the same wearable PEH for both energy harvesting and gait recognition at the same time. This is achieved by designing a special algorithm that minimizes distortions caused to the sensing signal when the harvested energy is being stored in the capacitor. The concept is prototyped in the form factor of a shoe, and its performance is evaluated with 20 subjects, which confirms that the proposed filter helps detect human gait with 8% higher accuracy while consuming 35% less power compared to the state-of-the-art. The second contribution is the investigation of gesture recognition using solar cells, where the power signals from solar cells exhibit distinct patterns under different gestures. To improve the system robustness under different settings, a signal processing pipeline was devised by combining z-score transformation and DTW. Experiments with both transparent and opaque solar cells confirm 96% gesture recognition accuracy. The third contribution investigates the use of inertial sensors for CSI acquisition in the context of vibration-based MIMO communication over human skin. It is first demonstrated that due to slow ramping of vibration motors, conventional channel sounding based CSI acquisition is not practical for vibration MIMO. To solve this problem, this thesis proposed a deep learning-based CSI acquisition framework to accurately predict CSI entirely based on inertial sensor (accelerometer and gyroscope) measurements at the transmitter, thus obviating the need for channel sounding. Based on experimental vibration data, it is demonstrated that the proposed inertial sensor-based CSI acquisition method improves MIMO capacity by a factor of 2.3 compared to open-loop MIMO, which does not have access to CSI.