JOURNAL OF L A T E X CLASS FILES, VOL. 11, NO. 4, DECEMBER 2012 1 Non-Resonant Kinetic Energy Harvesting using Macro-Fiber Composite Patch Giulia Bassani, Student, IEEE Alessandro Filippeschi, Member, IEEE Emanuele Ruffaldi, Member, IEEE Abstract—Over the past decades, thanks to the progresses being made in low power microelectronics, wireless technology, and energy harvesting techniques, we are observing an impressive increase in the use of wearable devices. Kinetic human energy harvesting is the most efficient and practical method to power them reducing the need of batteries replacement since walking or running is how humans already expend much of their daily energy. The present energy harvesting technologies still have several limitations. In this work, thanks to a mechanical framework specifically designed to reproduce the kinematic of a knee joint and actuated using recorded human motion patterns, we demonstrate the feasibility of the non-resonant employment of the Macro Fiber Composites (MFCs) to scavenge energy from the various human body movements. Both the energy of periodic and aperiodic motions can be harvested. The electrical characteristics of the whole system focusing on the maximum power point of the MFC have been investigated to optimize the system power output. Index Terms—Energy harvesting, Macro Fiber Composite, Piezoelectric fibers, Wearable, Human motion, Body sensor network. I. I NTRODUCTION T HE remarkable progresses in science and technologies over the past decades are making us increasingly de- pendent on portable electronic devices [?]. With the advances being made in wireless technology and low power microelec- tronics, we are observing a dramatic growth of interest for wearable technology [?], [?]. Smart wearable systems, also named as Wireless Body Area Networks (WBANs), are wireless networks used for com- munication among sensor nodes operating on, in, or around the human body in order to monitor vital body parameters and movements. WBAN enables ubiquitous monitoring of a person and they are increasingly used in several applications such as health-care, well-being, sport, entertainment, assisted living, protection and safety. [?], [?], [?], [?], [?], [?] Low power microelectronics and power management approaches to minimize the energy consumption while meeting required performance constraints, are going to have considerably effects on the everyday used devices. For instance, Magno et al. [?] developed a low power WBAN platform focusing on the minimization of the power consumption. The possibilities of switching off the sensors and even the main radio while the ultra low power wake up radio keeps listening for asyn- chronous commands allow to significantly reduce the power consumption, achieving even 1.8 μW in deep sleep. Power Authors are with the PERCRO laboratory of TeCiP Institute, Scuola Superiore Sant’Anna, Pisa, Italy e-mail: (n.lastname@sssup.it). Manuscript received XXXX, 2016;XXXX. autonomy of the sensor nodes is essential for their success, and this requires, beyond the development of low-power electronics, the research of long-life energy sources. Nowadays the majority of the devices are powered by batteries, which dominate weight, size and need to be constantly charged. Therefore, it is necessary to encourage different power sources. There are four possible ways to realize a distributed sensor network with adequate performance, as following: enhance the energy density of the storage systems; reduce the power consumption of the sensor; develop self-powered sensors by generating or harvesting energy; or transmit the power from a centralized source to the sensor. Among these various possible solutions the most efficient and practical method is to develop self-powered sensors by harvesting energy from the nearby available energy sources [?], [?]. There are few sources from which we can harvest energy for wearable wireless sensors, among them we can point to kinetic energy harvested from the human body motion [?], [?]. Among human activities, walking has the greatest potential for mechanical energy harvesting. It is indeed a routine ac- tivity which involves legs, upper limbs and center of mass motion. Therefore, knees, ankles, shoulder and elbow joints are promising targets for energy harvesting while walking. One possible division is to distinguish between active and passive energy harvesting methods. The active powering of electronic devices takes place when the user has to do a specific activity in order to power the product, on the contrary the passive powering takes place when the user does not have to do any task different from the normal use of the product. For instance, the self-winding watch, which utilizes the motion of the user’s arm to accelerate a small internal mass, produces 1 mW when it is vigorously shaken and 5μW when the watch is worn [?]. The energy output of active powering is often greater than that of passive powering, but this is at the cost of fully employing the user’s attention and strength, so passive energy harvesting methods, in which the energy is harvested from the user’s everyday actions, needs to be investigated more deeply. Various devices have been developed to harvest energy from human motion [?], [?]. They can be grouped into three categories based on the principle used in their energy conversion: inertia-based harvesters; impact force- based harvesters; and motion driven harvesters. The inertia- based harvesters use the inertia force of a proof mass, e.g. the suspended-load backpack that converts mechanical energy from the up-and-down movement of the carried load when walking. It generates 7.4 W during fast walking carrying a 38 kg load [?]. The impact force-based harvesters use the forces from a large moving mass (e.g. body weight) and the research