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Session: Poster session I
Session starts: Thursday 24 January, 15:00

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D.I. Beekers (Applied Physics, Faculty of Applied Sciences, Delft University of Technology, The Netherlands)
V.J.M. Gast (Life Science and Technology, Faculty of Applied Sciences, Delft University of Technology, The Netherlands)
E.J. Peuscher (Life Science and Technology, Faculty of Applied Sciences, Delft University of Technology, The Netherlands)
J.Q. Rusman (Mechanical Engineering, Faculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology, The Netherlands)
N. Tolou (Department of Precision and Microsystems Engineering, Faculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology, The Netherlands)

Abstract:
There is a hidden source of energy in our everyday motion, yet not fully explored. A small amount of the mechanical energy from human motion can be captured and converted into electric energy, using energy harvesters. This offers the possibility of lifelong power supply for implantable medical devices, such as hearing-aids, pacemakers and deep-brain-stimulation. However, in most cases, energy harvesters only perform efficiently if the resonance frequency of the harvester is adjusted to the frequency of the human body motion. Therefore the goal of this research is to identify the frequencies and body locations at which most of the human motion occurs and the largest power can be harvested. The measurement equipment consisted of several smart phones to measure the acceleration in three orthogonal axes, defined with respect to the human body. The data acquisition was performed on four individuals, subject to different daily activities and at different body locations. The activities were classified into specific activities, such as walking and cycling, and short day-to-day activities, such as having a coffee break or doing groceries. The smart phones were located at the wrist, upper arm, waist and ankle for all activities. The resulting data was processed in MATLAB using the Welch function to identify the frequencies and the related power density. We have shown that the highest power at which the body moves can be harvested between 0 and 11 Hz. The highest power density was measured during cycling in the frequency band of 0 to 1 Hz, in the direction from inferior to superior, with the device located at the ankle. This research contributes to further development of the use of energy harvesters in medical devices.