System prototype for a triboelectric harvester in a smart knee implant

Abstract

Total knee replacement (TKR) surgeries have been increasing tremendously in the past few years particularly among active young people and elderly people suffering from knee pain. Hence, continuous monitoring of the load on the knee after the knee surgery is highly desirable for designing an efficient and more functional smart knee implant. In this paper, we demonstrate a smart knee implant system which consists of a triboelectric harvester and a front-end electronic system which continuously monitors the load on the knee by relying only on the power harvested by the triboelectric harvester. The TKR system consists of the femoral, tibial tray, and the ultra high mechanical polyethylene (UHMWPE) bearing parts. The designed triboelectric harvesters are placed between the tibial tray and the UHMWPE bearing for perfect load monitoring. The frontend electronic system is placed on the tibial tray to be powered by the harvester. The triboelectric harvester produces an AC signal which is processed using our proposed frontend electronic system to monitor the load on the knee. For instance, at a knee cyclic load of around 230 N, the harvester produces 6.5 μW power and 18 V RMS signal at a frequency of 1 Hz. The frontend electronic system consists of a LC filter to process the high voltages from the harvester, a rectifier to convert the AC signal into a DC signal, a regulator to convert this DC signal into a stabilized and ripple free DC signal to provide biasing to the final stage, Delta-Sigma ADC, which finally converts the analog signal into digital bits. The power consumption of the proposed design is approximately 5 uW. According to the proposed design, monitoring the load several times a day is feasible by relying only on the harvested power. The prototype of the proposed system has been fabricated on a printed circuit board (PCB) and tested with the designed harvester. The test results demonstrate that triboelectric energy harvesting is a promising technique for self-monitoring the load inside knee implants. Through this research, the knee implants could be improved and failures can be detected at an early stage.