Elderly Health Monitoring System with Fall Detection Using Multi-Feature Based Person Tracking

In this study, we proposed an intelligent health monitoring system based on smart clothing. The system consisted of smart clothing and sensing component, care institution control platform, and mobile device. The smart clothing is a wearable device for electrocardiography signal collection and heart rate monitoring. The system integrated our proposed fast empirical mode decomposition algorithm for electrocardiography denoising and hidden Markov model–based algorithm for fall detection. Eight kinds of services were provided by the system, including surveillance of signs of life, tracking of physiological functions, monitoring of the activity field, anti-lost, fall detection, emergency call for help, device wearing detection, and device low battery warning. The performance of fast empirical mode decomposition and hidden Markov model were evaluated by experiment I (fast empirical mode decomposition evaluation) and experiment II (fall detection), respectively. The accuracy and sensitivity of R-peak detection using fast empirical mode decomposition were 96.46% and 98.75%, respectively. The accuracy, sensitivity, and specificity of fall detection using hidden Markov model were 97.92%, 90.00%, and 99.50%, respectively. The system was evaluated in an elderly long-term care institution in Taiwan. The results of the satisfaction survey showed that both the caregivers and the elders are willing to use the proposed intelligent health monitoring system. The proposed system may be used for long-term health monitoring.

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6LoWPAN-enabled fall detection and health monitoring system with Android smartphone

2016 IEEE International Conference on Electro Information Technology (EIT) ◽

10.1109/eit.2016.7535234 ◽

2016 ◽

Cited By ~ 4

Author(s):

Won-Jae Yi ◽

Oishee Sarkar ◽

Thomas Gonnot ◽

Ehsan Monsef ◽

Jafar Saniie

Keyword(s):

Monitoring System ◽

Health Monitoring ◽

Fall Detection ◽

Health Monitoring System

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Mobile Health-monitoring System with Inference, Fall Detection, and Cardiovascular Prediction

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY ◽

10.24297/ijct.v17i2.7621 ◽

2018 ◽

Vol 17 (2) ◽

pp. 7284-7296 ◽

Cited By ~ 1

Author(s):

Yin-Fu Huang

Keyword(s):

Monitoring System ◽

Mobile Health ◽

Health Monitoring ◽

Medical Center ◽

Mobile Network ◽

Fall Detection ◽

Health Monitoring System ◽

Traditional Health ◽

Mobile Health Monitoring

As the lifetime of human being gets longer, the problems of chronic diseases grow more. In order to make sure the health statuses of patients are not getting worse, they must be health-monitored continuously in a long term. In this paper, a mobile health-monitoring system is built for patients in place of traditional health-caring manners, which not only gives patients more free spaces, but also can save medical resources, diagnose and predict diseases earlier. In the procedures of health-caring in-house and emergency treatment, a series of vital sensors are combined by integrating sensor network and wireless/mobile network technology to continuously transmit physiological signals of patients to a medical center in a real time, and then doctors can monitor the health statuses of patients exactly, thereby proceeding with diagnosing, recovering, and treatments.

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

Journal of Sensor and Actuator Networks ◽

10.3390/jsan8030039 ◽

2019 ◽

Vol 8 (3) ◽

pp. 39 ◽

Cited By ~ 3

Author(s):

Saif Saad Fakhrulddin ◽

Sadik Kamel Gharghan

Keyword(s):

Heart Rate ◽

Monitoring System ◽

Health Monitoring ◽

Measurement Accuracy ◽

Fall Detection ◽

Daily Activities ◽

Rate Measurement ◽

Wireless Health ◽

Health Monitoring System ◽

Patient Falls

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.

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