Data that can be collected with the use of instrumented implants can help in diagnosis and treatment of complications during the lifetime of total joint replacements. They can give an accurate reading of the status of the implant from within the body. In this research project an electronic system is designed to feed sensors with enough energy to be able to collect the data of interest. For the power generation a piezoelectric element is used and placed inside the neck of the implant. The communication from within the implant is achieved with a micro control unit with Bluetooth low energy. As sensors, two thermistors are used and the piezoelectric element can also be used as a force sensor. Multiple experiments were performed to investigate the power generation and power consumption of the circuit. The piezoelectric element was able to harvest 877µW on average during one gait cycle. From the data of the power management integrated circuit it was seen that there was 778mJ available every day. With this energy the circuit is able to measure data from two thermistors every minute and send it three times per day. There is also an option to use the piezoelectric element as a force sensor. The Nominal Root Mean Square Error(NRMSE) when using the element as a force sensor is 0.0314 in the best case. The end result is an instrumented hip implant which completely fits inside a customized hip implant and is able to communicate with a phone via Bluetooth Low Energy.

In this paper we will research the possibility, reliability and accuracy of calculating the heart rate of a patient at rest using Ballistocardiography in clinical settings. The vital signs of the patient will be extracted by using piezoelectric sensor which is embedded in a Bedsense device developed by Momomedical. The device developed by Momomedical is placed under the mattress and has no direct contact with the patient. The piezoelectric sensor generates a signal that can be separated into motion, respiration and ballistocardiogram (BCG) waveforms, from which movement, respiration rate (RR) and heart rate (HR) can be obtained respectively. To improve the accuracy of the measurement several signal processing methods will be applied to reduce noise and outliers or sharpen the waveform before calculating the heart rate using peak detection. The reliability is determined by comparing the percentage difference with a reference heart rate. The accuracy was found to be 55.74% of the time within 5% and 91.8% within 10% when lying on your back. This was found to be 84.43% of the time within 5% when lying on your right side and for the left side this was 80.33% of the time within 5%.