Energy-Efficient Elderly Fall Detection System Based on Power Reduction and Wireless Power Transfer

Abstract

Elderly fall detection systems based on wireless body area sensor networks (WBSNs) have increased significantly in medical contexts. The power consumption of such systems is a critical issue influencing the overall practicality of the WBSN. Reducing the power consumption of these networks while maintaining acceptable performance poses a challenge. Several power reduction techniques can be employed to tackle this issue. A human vital signs monitoring system (HVSMS) has been proposed here to measure vital parameters of the elderly, including heart rate and fall detection based on heartbeat and accelerometer sensors, respectively. In addition, the location of elderly people can be determined based on Global Positioning System (GPS) and transmitted with their vital parameters to emergency medical centers (EMCs) via the Global System for Mobile Communications (GSM) network. In this paper, the power consumption of the proposed HVSMS was minimized by merging a data-event (DE) algorithm and an energy-harvesting-technique-based wireless power transfer (WPT). The DE algorithm improved HVSMS power consumption, utilizing the duty cycle of the sleep/wake mode. The WPT successfully charged the HVSMS battery. The results demonstrated that the proposed DE algorithm reduced the current consumption of the HVSMS to 9.35 mA compared to traditional operation at 85.85 mA. Thus, an 89% power saving was achieved based on the DE algorithm and the battery life was extended to 30 days instead of 3 days (traditional operation). In addition, the WPT was able to charge the HVSMS batteries once every 30 days for 10 h, thus eliminating existing restrictions involving the use of wire charging methods. The results indicate that the HVSMS current consumption outperformed existing solutions from previous studies.

Design of Powering Wireless Medical Sensor Based on Spiral-Spider Coils

“…Figure 8a-d shows that the output power, transfer efficiency, output voltage, and output current were 0.55 W, 2.24%, 5 V, and 110 mA, respectively, in a 12 cm air gap. [44] for the nRF24L01, Arduino nano, and heart rate sensor-based pulse sensor (SEN-11574), respectively. In this context, an output power of 0.166 W (33.2 mA × 5 V (operating voltage)) is sufficient for operating the measurement component of the proposed system.…”

“…All of the components of the measurement system consume approximately 33.2 mA. The current consumptions are 19 mA [42], 12 mA [43], and 2.2 mA [44] for the nRF24L01, Arduino nano, and heart rate sensor-based pulse sensor (SEN-11574), respectively. In this context, an output power of 0.166 W (33.2 mA × 5 V (operating voltage)) is sufficient for operating the measurement component of the proposed system.…”

A Novel Portable Real-Time Low-Cost Sleep Apnea Monitoring System based on the Global System for Mobile Communications (GSM) Network

“…However, the patient's sleeping position during sleep cannot be compatible with the sensor used. In addition, Gharghan et al [40] developed a human vital signs monitoring system (HVSMS) that includes Global Positioning System (GPS), GSM, and acceleration sensors. But this study mostly focused on power consumption and battery life of the system, and the main purpose of the study was to minimize the power consumption.…”

Wireless Power Transfer Based on Spider Web–Coil for Biomedical Implants

“…To enhance the resulting powertransfer efficiency and coupling between transmitter and receiver coils of WPT for BMI devices, researchers proposed several techniques and methods: coil geometry [11]- [13] different closed-loop and open-loop power control [14], power amplification [15] , [16] an impedance-matching network [17], [18] , misalignment conditions [19], and coupling configuration [20], [21]. In the biomedical field, WPT is essential as an energy source for biosensing and BMI devices [22] such as vital human-signs monitoring [23], heart-rate sensors [24], capsule endoscopy [25], left ventricular assist devices (LVAD) [26], deep micro-implants [27], and optogenetics applications [28].…”