Design and Performance of an Optimal Inertial Power Harvester for Human-Powered Devices

We present an empirical study of the long-term practicality of using human motion to generate operating power for body-mounted consumer electronics and health sensors. We have collected a large continuous acceleration data set from eight experimental subjects going about their normal daily routine for three days each. Each subject is instrumented with a data collection apparatus that simultaneously logs 3-axis, 80 Hz acceleration data from six body locations. We use this data set to optimize a first-principles physical model of the commonly used velocity damped resonant generator (VDRG) by selecting physical parameters such as resonant frequency and damping coefficient to maximize the harvested power. Our results show that with reasonable assumptions on size, mass, placement, and efficiency of VDRG harvesters, most body-mounted wireless sensors and even some consumer electronics devices can be powered continuously and indefinitely from everyday motion. We have optimized the power harvesters for each individual and for each body location. In addition, we present the potential of designing a damping- and frequency-tunable power harvester that could mitigate the power reduction of a generator generalized for "average” subjects. We present the full details on the collection of the acceleration data sets, the development of the VDRG model, and a numerical simulator, and discuss some of the future challenges that remain in this promising field of research.

[1]  Gerhard Tröster,et al.  Towards Wearable Autonomous Microsystems , 2004, Pervasive.

[2]  Joseph A. Paradiso,et al.  Human Generated Power for Mobile Electronics , 2004 .

[3]  M. G. Prasad,et al.  A vibration energy harvesting device with bidirectional resonance frequency tunability , 2008 .

[4]  Jan M. Rabaey,et al.  Improving power output for vibration-based energy scavengers , 2005, IEEE Pervasive Computing.

[5]  D. Wenzel,et al.  Low power integrated pressure sensor system for medical applications , 1999 .

[6]  A. Chapuis,et al.  The history of the self-winding watch, 1770-1931 , 1956 .

[7]  Yang Zhang,et al.  Toward self-tuning adaptive vibration-based microgenerators , 2005, SPIE Micro + Nano Materials, Devices, and Applications.

[8]  Frank T. Fisher,et al.  High efficiency energy harvesting device with magnetic coupling for resonance frequency tuning , 2008, SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[9]  H. Harry Asada,et al.  Artifact-resistant, power-efficient design of finger-ring plethysmographic sensors. I. Design and analysis , 2000, Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Cat. No.00CH37143).

[10]  Eric M. Yeatman,et al.  Advances In Power Sources For Wireless Sensor Nodes , 2004 .

[11]  Taeseung D. Yoo,et al.  Generating Electricity While Walking with Loads , 2022 .

[12]  Guang Zhu,et al.  Converting biomechanical energy into electricity by a muscle-movement-driven nanogenerator. , 2009, Nano letters.

[13]  M. Strasser,et al.  Miniaturized Thermoelectric Generators Based on Poly-Si and Poly-SiGe Surface Micromachining , 2002 .

[14]  Doris Schmitt-Landsiedel,et al.  A 0.5V, 1µW successive approximation ADC , 2002 .

[15]  Joseph A. Paradiso,et al.  Energy scavenging for mobile and wireless electronics , 2005, IEEE Pervasive Computing.

[16]  T.C. Green,et al.  Architectures for vibration-driven micropower generators , 2004, Journal of Microelectromechanical Systems.

[17]  Jens Sauerbrey,et al.  A 0.5-V 1-μW successive approximation ADC , 2003, IEEE J. Solid State Circuits.

[18]  G. Troster,et al.  Optimization of inertial micropower Generators for human walking motion , 2006, IEEE Sensors Journal.

[19]  Joseph A. Paradiso,et al.  Systems for human-powered mobile computing , 2006, 2006 43rd ACM/IEEE Design Automation Conference.

[20]  P. Wright,et al.  Resonance tuning of piezoelectric vibration energy scavenging generators using compressive axial preload , 2006 .

[21]  Rajeevan Amirtharajah,et al.  Self-powered signal processing using vibration-based power generation , 1998, IEEE J. Solid State Circuits.

[22]  Joseph A. Paradiso,et al.  Energy Scavenging with Shoe-Mounted Piezoelectrics , 2001, IEEE Micro.

[23]  J A Hoffer,et al.  Biomechanical Energy Harvesting: Generating Electricity During Walking with Minimal User Effort , 2008, Science.

[24]  M. Strasser,et al.  Miniaturized Thermoelectric Generators Based on Poly-Si and Poly-SiGe Surface Micromachining , 2002 .

[25]  Peter Desnoyers,et al.  Ultra-low power data storage for sensor networks , 2009, TOSN.

[26]  A. Alivisatos,et al.  Hybrid Nanorod-Polymer Solar Cells , 2002, Science.

[27]  Joseph A. Paradiso,et al.  A Compact, Wireless, Self-Powered Pushbutton Controller , 2001, UbiComp.

[28]  Thad Starner,et al.  Human-Powered Wearable Computing , 1996, IBM Syst. J..

[29]  J. S. Yuan,et al.  Low-power CMOS wireless MEMS motion sensor for physiological activity monitoring , 2005, IEEE Transactions on Circuits and Systems I: Regular Papers.

[30]  R. B. Yates,et al.  Analysis Of A Micro-electric Generator For Microsystems , 1995, Proceedings of the International Solid-State Sensors and Actuators Conference - TRANSDUCERS '95.