IoT-Based Heartbeat Rate-Monitoring Device Powered by Harvested Kinetic Energy

Abstract

Remote patient-monitoring systems are helpful since they can provide timely and effective healthcare facilities. Such online telemedicine is usually achieved with the help of sophisticated and advanced wearable sensor technologies. The modern type of wearable connected devices enable the monitoring of vital sign parameters such as: heart rate variability (HRV) also known as electrocardiogram (ECG), blood pressure (BLP), Respiratory rate and body temperature, blood pressure (BLP), respiratory rate, and body temperature. The ubiquitous problem of wearable devices is their power demand for signal transmission; such devices require frequent battery charging, which causes serious limitations to the continuous monitoring of vital data. To overcome this, the current study provides a primary report on collecting kinetic energy from daily human activities for monitoring vital human signs. The harvested energy is used to sustain the battery autonomy of wearable devices, which allows for a longer monitoring time of vital data. This study proposes a novel type of stress- or exercise-monitoring ECG device based on a microcontroller (PIC18F4550) and a Wi-Fi device (ESP8266), which is cost-effective and enables real-time monitoring of heart rate in the cloud during normal daily activities. In order to achieve both portability and maximum power, the harvester has a small structure and low friction. Neodymium magnets were chosen for their high magnetic strength, versatility, and compact size. Due to the non-linear magnetic force interaction of the magnets, the non-linear part of the dynamic equation has an inverse quadratic form. Electromechanical damping is considered in this study, and the quadratic non-linearity is approximated using MacLaurin expansion, which enables us to find the law of motion for general case studies using classical methods for dynamic equations and the suitable parameters for the harvester. The oscillations are enabled by applying an initial force, and there is a loss of energy due to the electromechanical damping. A typical numerical application is computed with Matlab 2015 software, and an ODE45 solver is used to verify the accuracy of the method.

Keywords: IoT server; Schenkel doubler; biomedical signal processing; electromagnetic energy harvester; kinetic energy harvesting; long-term ECG monitoring; quadratic non-linearity; wearable IoT devices.

Figure A1

Schematic diagram illustrating the interconnection between electrocardiogram sensor, micro-controller, energy harvester and WI-FI device.

Figure 1

Architecture of IoT based ECG monitoring device powered by energy harvester.

Figure 2

Voltage multiplier and rectifier.

Figure 3

Printed circuit board for IoT-based heart rate-monitoring device: (a) designed board, (b) manufactured board.

Figure 4

Design (a) and physical model (b) of electromagnetic harvester.

Figure 5

Displacement of central magnet using ODE45 and classical method for ODE.

Figure 6

Amplitude ratio vs. frequency ratio response curve.

Figure 7

The measurement process of the voltage produced by the harvester, with the harvester attached to the arm (a) or lower extremity (b). The spectrum analyzer (c) and Harvester (d).

Figure 8

Voltage ${\mathrm{V}}_{\mathrm{h}}$ generated by the harvester.

Figure 9

Voltage U across the capacitor.

Figure 10

Measurement process (a), and experimental prototype power for IoT-based heartbeat rate-monitoring device (b).

Figure 11

Measurement process (a); harvested power (b).

Figure 12

Experimental setup: using the mobile phone as Wi-Fi hotspot for connecting experimental setup (a); experimental setup connected to Wi-Fi (b). a: Screen; b: adjust contrast for screen; c: microcontroller; d: Wi-Fi serial transceiver; e: reset switch; f: pins for ECG sensor; g: power pins for harvester; (c) h: ECG sensor; i: ECG electrodes.

Figure 13

Experimental ECG wave form (a); conventional ECG wave form (b).