Elderly Health Monitoring System Research Articles

Constructing an elderly health monitoring system using fuzzy rules and Internet of Things

Monitoring the health of the elderly using wearable sensors and Internet of Things (IoT) devices necessitates the collection of data from various sources, thereby increasing the volume of data to be gathered at the monitoring center. As previously elucidated, the system exhibits high accuracy in measuring variables such as blood oxygen saturation (BOS) and heart rate (HR), as well as demonstrating proficient accuracy in implementing fuzzy rules to detect the “stable” or “unstable” condition of elderly patients. Therefore, by integrating wearable sensors and IoT devices into the elderly health monitoring system, we can enhance the quality of monitoring and provide more timely healthcare solutions tailored to the elderly population’s needs. The integration of fuzzy rules and IoT technology into the elderly health monitoring system offers an efficient and effective solution for continuous health surveillance. In this research, we integrated fuzzy rules and IoT technology into the elderly health monitoring system using parameters such as BOS and HR, through the integration of MAX30100 sensors and ESP32 microprocessors. The implementation of fuzzy rules resulted in nine rules indicating whether an elderly patient is in a “stable” or “unstable” condition. The methodology involved (1) component preparation, (2) device accuracy analysis, (3) fuzzy rule development, (4) Android-based mobile application development, (5) fuzzy rule accuracy analysis, and (6) cost analysis. Experimental results indicated a device accuracy of 98.89% in measuring BOS and HR variables compared to medical devices and a fuzzy rule accuracy of 96% in detecting whether the patient’s condition is “stable” or “unstable.” Based on the experimental findings, the elderly health monitoring system provides a user-friendly, precise, and cost-effective solution to enhance the quality of life for the elderly population.

IoT Based Surveillance and Health Monitoring System for Elderly and Physically Challenged People

With the increase in global life expectancy and the advance of technology, the creation of age-friendly environments is a priority in the design of new products for elderly people healthcare. This paper presents a proposal for a real-time health monitoring system of older adults living in geriatric residences. This system was developed to help caregivers to have a better control in monitoring the health of their patients and have closer communication with their patients’ family members. To validate the feasibility and effectiveness of this proposal, a prototype was built, using a biometric bracelet connected to a mobile application, which allows real-time visualization of all the information generated by the sensors (heart rate, body temperature, and blood oxygenation) in the bracelet. Using these data, caregivers can make decisions about the health status of their patients. The evaluation found that the users perceived the system to be easy to learn and use, providing initial evidence that our proposal could improve the quality of the adult’s healthcare.

Nursing Effect of Health Monitoring System on Elderly Patients with Osteoporosis.

Health monitoring can provide scientific and reasonable care for elderly users, professionally monitor the health parameters of the human body, and timely understand the user's own physical condition. By installing sensors with different functions in the rooms where the elderly often move and by installing vital signs sensors on their bodies, the data detected by the sensors are collected and analyzed in real time. Nursing refers to the fact that nursing staff must strictly follow the nursing system and operating procedures in the nursing work, accurately implement the doctor's orders, implement the nursing plan, and ensure that the patient is physically and mentally safe during treatment and recovery. Osteoporosis is a systemic bone disease in which bone density and bone quality are decreased due to various reasons, and the microstructure of bone is destroyed, resulting in increased bone fragility, which is prone to fractures. Osteoporosis is divided into two main categories: primary and secondary. Primary osteoporosis is divided into postmenopausal osteoporosis (Type I), senile osteoporosis (Type II), and idiopathic osteoporosis (including adolescent forms). This paper aims to study the healthcare effect of health monitoring system on elderly patients with osteoporosis, expecting to use the health monitoring system to provide more scientific care for the elderly and reduce the pain caused by osteoporosis. This paper proposes a study from the users of the elderly health monitoring products and the elderly home health products and analyzes the influencing factors of the usability design of the elderly home health monitoring system. This paper designs the overall framework of the elderly health monitoring system and designs the main components and application functions of the system. The experimental results in this paper show that there are 20 patients with osteoporosis due to lack of light, accounting for 16%. There are 10 patients with osteoporosis due to excessive coffee intake, accounting for 8%. There are 90 people who normally eat eggs, accounting for 75%, and 66 people who eat meat normally, accounting for 55%. According to the data, the health monitoring system can effectively control the diet of patients with osteoporosis.

Development of the elderly health monitoring system through families and community health workers during the COVID-19 pandemic

Elderly people have a high risk of severe and fatal COVID-19 condition. Controlling the risk factors among the elderly is important, but health service provision during outbreaks is challenging. The elderly need increased attention to be monitored for their health status routinely. The elderly must also adjust daily activities according to the COVID-19 protocols. This program aimed to provide a practical example of a feasible system to empower community health workers and families for monitoring health among the elderly. This study used quantitative and qualitative data to identify the problems and needs of elderly health monitoring in Caturtunggal, Sleman, Indonesia. Descriptive quantitative methods used secondary data from the Sleman Health and Demographic Surveillance System and elderly health checklists to identify the issues of non-communicable diseases (NCDs) before and during COVID-19. The community health workers were interviewed to explore the needs and feasibility of programs among the elderly. As much as 16% of Caturtunggal population is elderly. One-third are 60-65 years old (33.30%) and retired (30.60%). The most common NCDs were hypertension (25.60%), diabetes mellitus (13.30%), stroke and coronary heart disease (4.40% for each). Almost all elderly (88.89%) have health insurance. Health counselling, online consultation, and monitoring were agreed on after discussion with the community health workers. Serial consultations (COVID-19 protocol for elderly, regular self-monitoring, the national health insurance mobile application, and health services during the pandemic situation) were conducted through WhatsApp groups. Free counseling was provided throughout the program for the elderly care during the outbreak. The monitoring was achieved through the WhatsApp groups by delivering instructional photos or videos of physical activity, blood pressure measurements, dietary record and stress management. The elderly and families felt more confident to check their health status and reported the results. In conclusion, development of health monitoring system was conducted through health checklists. Family and community cadres were also involved to make the program more feasible and sustainable.

Research on health management and risk warning based on sensor data

In the context of the deepening degree of population aging, the attention of intelligent medical treatment and intelligent elderly care has gradually increased, wearable health devices and telemedicine have also developed rapidly, and medical big data technology has also been widely used in relevant fields. Using the real-time sensor detection data of a nursing home equipped with intelligent mattresses in real life, this paper analyzes the variation law of the elderly's heart rate and respiratory rate in bed, and tries to obtain the parameters in the uninterrupted and automatic sudden and chronic health condition early warning mechanism based on this Law and relevant domain knowledge, also realizes uninterrupted real-time automatic health management and risk early warning without any impact on users' activities, and designs the framework of elderly health monitoring system including intelligent equipment, guardian and data center, which is of great significance to the daily health management and risk early warning of the elderly.

An elderly health monitoring system based on biological and behavioral indicators in internet of things

Advancement of sensor technologies has conducted to the rapid evolution of platforms, tools and approaches such as Internet of Things (IoT) for developing behavioral and physiological monitoring systems. Nowadays, According to growing number of elderlies living alone without their relatives scattered over the wide geographical areas, it is significantly essential to track their health function status continuously. In this paper, an IoT-based health monitoring system is proposed to check vital signs and detect biological and behavioral changes via smart elderly care technologies. It provides a health monitoring system for the involved medical teams to continuously monitor and assess a disabled or elderly’s behavioral activity as well as the biological parameters, applying sensor technology through the IoT devices. In this approach, vital data is collected via IoT monitoring objects and then, data analysis is carried out through different machine learning methods such as Decision Tree (J48), Sequential Minimal Optimization (SMO), Multi-Layer Perceptron (MLP) and Naive Bayes (NB) classifiers for detecting the level of probable risks of elderly’s physiological and behavioral changes. The experimental results confirm that the SMO, MLP and NB classifiers meet approximately close performance considering the accuracy, precision, recall, and f-score factors. However, the J48 method shows the highest performance for health function status predicting in our scenario with 99%, of accuracy and precision, 100% of recall and 97% of f-score. Moreover, the J48 performs with the lowest execution time in comparison to the other applied classifiers.

An Elderly Health Monitoring System Using Machine Learning and In-Depth Analysis Techniques on the NIH Stroke Scale

Recently, with the rapid change to an aging society and the increased interest in healthcare, disease prediction and management through various healthcare devices and services is attracting much attention. In particular, stroke, represented by cerebrovascular disease, is a very dangerous disease, in which death or mental and physical aftereffects are very large in adults and the elderly. The sequelae of such stroke diseases are very dangerous, because they make social and economic activities difficult. In this paper, we propose a new system to prediction and in-depth analysis stroke severity of elderly over 65 years based on the National Institutes of Health Stroke Scale (NIHSS). In addition, we use the algorithm of decision tree of C4.5, which is a methodology of prediction and analysis of machine learning techniques. The C4.5 decision trees are machine learning algorithms that provide additional in-depth rules of the execution mechanism and semantic interpretation analysis. Finally, in this paper, it is verified that the C4.5 decision tree algorithm can be used to classify and predict stroke severity, and to obtain additional NIHSS features reduction effects. Therefore, during the operation of an actual system, the proposed model uses only 13 features out of the 18 stroke scale features, including age, so that it can provide faster and more accurate service support. Experimental results show that the system enables this by reducing the patient NIH stroke scale measurement time and making the operation more efficient, with an overall accuracy, using the C4.5 decision tree algorithm, of 91.11%.

Low-Cost Elderly Healthcare Monitoring System

One of the most common problems we face today is the lack of a proper home-based health care system for the elderly. In the Philippines, there are several elderly people living alone in their homes, with no one to look after them. This is a problem because it’s going to be difficult for them to ask for help if they ever get into an accident. Some systems are already available to monitor a person, but they are limited to only one or two parameters, namely the pulse rate and the temperature. In this study, a low-cost elderly health monitoring system was designed and developed using Arduino Uno, ADXL345 (Accelerometer), LM35 (Temperature Sensor), Pulse sensor and GSM module. This system includes pulse rate monitoring, temperature monitoring and fall detection with the addition of an SMS notification system that will notify the contact person if there is an accident or if the patient’s vital signs are below normal. The initial results showed that the system was able to accurately measure the temperature, the pulse rate, detect falls and to notify the registered number via SMS.

A personalized health-monitoring system for elderly by combining rules and case-based reasoning.

Health-monitoring system for elderly in home environment is a promising solution to provide efficient medical services that increasingly interest by the researchers within this area. It is often more challenging when the system is self-served and functioning as personalized provision. This paper proposed a personalized self-served health-monitoring system for elderly in home environment by combining general rules with a case-based reasoning approach. Here, the system generates feedback, recommendation and alarm in a personalized manner based on elderly's medical information and health parameters such as blood pressure, blood glucose, weight, activity, pulse, etc. A set of general rules has used to classify individual health parameters. The case-based reasoning approach is used to combine all different health parameters, which generates an overall classification of health condition. According to the evaluation result considering 323 cases and k=2 i.e., top 2 most similar retrieved cases, the sensitivity, specificity and overall accuracy are achieved as 90%, 97% and 96% respectively. The preliminary result of the system is acceptable since the feedback; recommendation and alarm messages are personalized and differ from the general messages. Thus, this approach could be possibly adapted for other situations in personalized elderly monitoring.

A ZigBee-based elderly health monitoring system: design and implementation

Internet of Things and its associated innovative technologies such as ZigBee, wireless sensor network, radio frequency identification, smart technologies (robotics), distributed databases and energy saving technologies have huge potentials in remote healthcare monitoring system and medical services to augment aging citizens' living style, safety, healthcare services and also reshape the future of our healthcare environments. This paper explores the intended system to achieve elderly health monitoring system. The designed system obtains, transmits, stores and analyses the elderly physiological conditions for healthcare providers exactly in real time intrusive or non-intrusively through tiny human wearable multi-hop sensor network. Hence collaborative remote healthcare services to identify common or critical health problems, sickness symptoms and remote diagnosis. It also provides supportive healthcare service with indigenous assimilation and booming out efficient service to prevent chronic disease and edifice a robust elderly healthcare monitoring system with appraisal, inspiration and assurance, etc. It guarantees the elderly life of integrated care and comprehensive remote health information management. The proposed system primarily depends on Internet of Things, ZigBee, wireless sensor network, body sensors, web applications and databases.

Ubiquitous Mobile Health Monitoring System for Elderly (UMHMSE)

Recent research in ubiquitous computing uses technologies of Body Area Networks (BANs) to monitor the person's kinematics and physiological parameters. In this paper we propose a real time mobile health system for monitoring elderly patients from indoor or outdoor environments. The system uses a biosignal sensor worn by the patient and a Smartphone as a central node. The sensor data is collected and transmitted to the intelligent server through GPRS/UMTS to be analyzed. The prototype (UMHMSE) monitors the elderly mobility, location and vital signs such as Sp02 and Heart Rate. Remote users (family and medical personnel) might have a real time access to the collected information through a web application.