Energy Harvesting from Human Walking Motion using Pendulum-based Electromagnetic Generators

Wearable devices find many applications in health monitoring, communications, and entertainment devices; however, battery life is still a major limitation to their functionality. The kinetic energy from the walking motion can be employed as an energy source for wearable devices with the use of vibration-based energy harvesters. This paper investigates energy harvesting from a pendulum-based system with an electromagnetic generator when placed on different human body joints. The coupled electromechanical equations of motion for the pendulum-based system with an electromagnetic generator energy harvester under combined in-plane movements and the body segment rotation are derived from the Lagrange formulation. The linearized coupled electromechanical equations for small amplitude oscillations are applied for the system parameter identification. The system's nonlinear response depends on identified parameters from linear frequency response functions, while the system's behavior is explored as a function of several critical parameters: excitation frequency, excitation amplitude, resistance load, and placement on the body. The system jumps from the chaotic non-rotary to the periodic rotary solution at the limit point bifurcation (i.e., saddle-node appears on the limit cycle). After this bifurcation point, the harvested energy increases multiple times. To estimate the output power from the walking motion, the trajectories generated by the walking simulation program are substituted into the coupled electromechanical equations of motion. The system's response is always an oscillatory type with the same period as the walking motion, and the output power can be maximized by varying the external load resistance at each specific relative walking velocity. Moreover, a higher walking velocity results in higher output power from the generator. The output voltage and power from wearable energy harvesters can be predicted accurately by using more precise walking trajectories.