The consumption of low-power electronic devices has increased rapidly, where almost all applications use power electronic devices. Due to the increase in portable electronic devices’ energy consumption, the piezoelectric material is proposed as one of the alternatives of the significant alternative energy harvesters. This study aims to create a prototype of “Smart Shoes” that can generate electricity using three different designs embedded by piezoelectric materials: ceramic, polymer, and a combination of both piezoelectric materials. The basic principle for smart shoes’ prototype is based on the pressure produced from piezoelectric material converted from mechanical energy into electrical energy. The piezoelectric material was placed into the shoes’ sole, and the energy produced due to the pressure from walking, jogging, and jumping was measured. The energy generated was stored in a capacitor as piezoelectric material produced a small scale of energy harvesting. The highest energy generated was produced by ceramic piezoelectric material under jumping activity, which was 1.804 mJ. Polymer piezoelectric material produced very minimal energy, which was 55.618 mJ. The combination of both piezoelectric materials produced energy, which was 1.805 mJ from jumping activity.
A scalable tracking human model was proposed for recognizing human jogging and walking activities. The model aims to detect and track a particular subject by using wearable sensor. Data collected are in accelerometer readings in three axes and gyroscope readings in three axes. The development of proposed human model is based on the moderating effects on human movements. Two moderators were proposed as the moderating factors of human motion and they are angular velocity and elevation angle. Linear regression is used to investigate the relationship among inputs, moderators and outputs of the model. The result of this study showed that the angular velocity and elevation angle moderators are affecting the relation of research output. Acceleration in x-axis (Ax) and angular velocity in y-axis (Gy) are the two main components in directing
a motion. Classification between jogging and walking motions was done by measuring the magnitude of angular velocity and elevation angle. Jogging motion was classified and identified with larger angular velocity and elevation angle. The two proposed hypotheses were supported and proved by research output. The result is expected to be beneficial and able to assist researcher in investigating human motions.