An Autonomous Wireless Health Monitoring System Based on Heartbeat and Accelerometer Sensors

Abstract

Falls are a main cause of injury for patients with certain diseases. Patients who wear health monitoring systems can go about daily activities without limitations, thereby enhancing their quality of life. In this paper, patient falls and heart rate were accurately detected and measured using two proposed algorithms. The first algorithm, abnormal heart rate detection (AHRD), improves patient heart rate measurement accuracy and distinguishes between normal and abnormal heart rate functions. The second algorithm, TB-AIC, combines an acceleration threshold and monitoring of patient activity/inactivity functions to accurately detect patient falls. The two algorithms were practically implemented in a proposed autonomous wireless health monitoring system (AWHMS). The AWHMS was implemented based on a GSM module, GPS, microcontroller, heartbeat and accelerometer sensors, and a smartphone. The measurement accuracy of the recorded heart rate was evaluated based on the mean absolute error, Bland–Altman plots, and correlation coefficients. Fourteen types of patient activities were considered (seven types of falling and seven types of daily activities) to determine the fall detection accuracy. The results indicate that the proposed AWHMS succeeded in monitoring the patient’s vital signs, with heart rate measurement and fall detection accuracies of 98.75% and 99.11%, respectively. In addition, the sensitivity and specificity of the fall detection algorithm (both 99.12%) were explored.

Development of an IoT-Based Construction Worker Physiological Data Monitoring Platform at High Temperatures

“…However, the WBGT can only provide general guidelines for preventing the adverse effects of heat. Although many nations still accept this measurement because of its convenience, limitations and errors do exist [ 35 ]. The heat index used in our proposed platform, i.e., the thermal comfort index (TCI), is developed based on the WBGT of the Korea Meteorological Administration (KMA).…”

“…Two nodes are connected via the LoRa network in order to provide a higher accuracy and larger coverage while also reducing costs. Previous studies have used GSM or Bluetooth as transmitting networks [ 6 , 10 , 35 ]. However, in construction sites, which typically include wide areas and environmental obstacles, the range of these technologies is limited to a few hundred meters.…”

Cluster-Analysis-Based User-Adaptive Fall Detection Using Fusion of Heart Rate Sensor and Accelerometer in a Wearable Device

“…Another objective is to design a fall detector that has high applicability in an actual environment. For fall detection, wearable-based approaches traditionally use accelerometer signals; [6], [11], [12], [18], [19] proposed a threshold-based fall detection approach using an accelerometer. In general, these approaches have relied on analyzing the peak signal of an acceleration caused by falling itself [6].…”

“…In addition, wearable devices are essential because many elderly people want to live autonomously at home without locational restrictions [16]. Wearable-based fall detection systems typically use accelerometers [6]- [13], [17]- [19], gyroscopes [20], heart rate sensors [21], or a combination of these [22]- [25]. Recently, several smartwatches have started to provide fall detection services.…”

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

“…More than 1 million elderly people fall and are treated in emergency departments due to a fall that causes a head injury or hip fracture each year in the USA. The measurement accuracy of elderly fall detection was experimentally tested for different ages (23 to 66 years), as presented in our previous work [36].…”