Home Health Monitoring System Research Articles

Innovating Home Health Monitoring with DeepTransferLearning and Edge AI

During an epidemic or pandemic, the healthcare system is under extreme stress. Infected individuals can easily spread a pandemic like COVID-19 to other people. Therefore, an important goal is to reduce this type of stress by providing home care for non-severely infected patients who are in isolation. This method is also very useful for monitoring the health of the elderly at home. Home health monitoring is one of the areas of care that is carried out in the comfort of one's home without compromising the privacy of patients and the elderly. In this article, we propose a home health monitoring system using edge computing and transfer learning. In particular, a model based on a pre-trained convolutional neural network can leverage edge devices with minimal ground-labeled data and fine-tuning techniques.

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.

A health monitoring system with posture estimation and heart rate detection based on millimeter-wave radar

Millimeter-wave radar is a device gaining popularity in many applications such as occupancy detection and people counting, vital sign detection and robotics. It has recently been used for human detection and tracking within home health care as a potential application area. Furthermore, increasing number of researchers utilize mmWave radar for heart rate detection. In this paper, we introduce a new home health monitoring system based on millimeter-wave radar, which is composed of posture estimation and heart rate detection. We focus on categorization of postures (standing, sitting and lying) for a single person in indoor environments. When the person is estimated as lying down, our system can further detect the heart rate. We establish a new point cloud dataset and show that, with simple algorithms employed, the system has an accuracy of around 99% for posture estimation and approximately 91% accuracy for detecting heart rate with only a single millimeter-wave radar.

Smart home health monitoring system for predicting type 2 diabetes and hypertension

Home health monitoring can facilitate patient monitoring remotely for diabetes and blood pressure patients. Early detection of hypertension and diabetes is extremely important, as these chronic diseases often result in life-threatening complications when found at a later stage. This work proposes a smart home health monitoring system that helps to analyze the patient’s blood pressure and glucose readings at home and notifies the healthcare provider in case of any abnormality detected. A combination of conditional decision-making and machine-learning approaches is used to predict hypertension and diabetes status, respectively. The goal is to predict the hypertension and diabetes status using the patient’s glucose and blood pressure readings. Using supervised machine learning classification algorithms, herein a system is trained to predict the patient’s diabetes and hypertension status. After analyzing all the classification algorithms, support vector machine classification algorithm was found to be most accurate and thus chosen to train the model. This proposed work develops an application for a home health monitoring system with a user-friendly easy-to-use graphical user interface to diagnose blood pressure and diabetes status of patients along with sending categorized alerts and real-time notifications to their registered physician or clinic all from home.

TECHNOLOGY FOR HOME MONITORING

Healthcare is increasingly moving away from expensive hospitals into the home. Although there are advantages to managing chronic health conditions in homes, there are also a number of disadvantages, beginning with aging adults’ unfamiliarity with the technologies being deployed. Too often, designs and instructional materials are not developed with aging users’ capabilities in mind. Health technology also confronts users with significant challenges including just-in-time learning when users are under significant stress. Technology support is often lacking. Also, care coordination for multiple chronic conditions is challenging. For instance, patients are often provided with multiple electronic health record portals. I discuss the reliability of and some of the practical challenges for a home health monitoring system used by older adults and those with heart failure over a 6-month interval. The system featured components such as a wrist-worn sensor package, daily tablet surveys, blood pressure cuff, weight scale, and bed sensors.

A sequence-to-sequence model-based deep learning approach for recognizing activity of daily living for senior care

Ensuring the health and safety of independent-living senior citizens is a growing societal concern. Researchers have developed sensor based systems to monitor senior citizens' Activity of Daily Living (ADL), a set of daily activities that can indicate their self-caring ability. However, most ADL monitoring systems are designed for one specific sensor modality, resulting in less generalizable models that is not flexible to account variations in real-life monitoring settings. Current classic machine learning and deep learning methods do not provide a generalizable solution to recognize complex ADLs for different sensor settings. This study proposes a novel Sequence-to-Sequence model based deep-learning framework to recognize complex ADLs leveraging an activity state representation. The proposed activity state representation integrated motion and environment sensor data without labor-intense feature engineering. We evaluated our proposed framework against several state-of-the-art machine learning and deep learning benchmarks. Overall, our approach outperformed baselines in most performance metrics, accurately recognized complex ADLs from different types of sensor input. This framework can generalize to different sensor settings and provide a viable approach to understand senior citizen's daily activity patterns with smart home health monitoring systems.

Two-tiered relay node placement for WSN-based home health monitoring system

Motivated by the needs of health monitoring at home (or a senior center) using a sensor network system, we study the problem of how to place the relay nodes so that the data collection and localization requirements of the monitoring system can be satisfied. By exploiting the inherent nature of the problem, we model it as finding a minimum connected k-dominating (k ≥ 3) set. Instead of using an idealistic disk radio model, we explicitly take into account the obstacles’ effect on the radio propagation in an indoor environment. We prove that the problem is NP-hard and propose an efficient greedy algorithm ORPA (Optimal Relay Placement Algorithm) to compute in polynomial time the best locations to place the relays. Results of extensive simulations have shown that by using our proposed algorithm ORPA, the number of relays required can be substantially reduced in comparison to the random placement and two-stage placement strategies. We also study the impact of the transmission power and the grid size on the algorithm and system performance. The result and method presented in the paper is useful to today’s indoor deployment of practical WSN-based monitoring system and to ensure network connectivity with minimal relay nodes.

Need Assessment for Smartphone-Based Cardiac Telerehabilitation

ObjectivesTo identify the current status of smartphone usage and to describe the needs for smartphone-based cardiac telerehabilitation of cardiac patients.MethodsIn 2016, a questionnaire survey was conducted in a supervised ambulatory cardiac rehabilitation (CR) program in a university affiliated hospital with the participation of heart failure or heart transplantation patients who were smartphone users. The questionnaire included questions regarding smartphone usage, demands for smartphone-based disease education, and home health monitoring systems. Results were described and analyzed according to principal diagnosis.ResultsNinety-six patients (66% male; mean age, 53 ± 11 years), including 56 heart failure and 40 heart transplantation patients, completed the survey (completion rate, 95%). The median daily smartphone usage time was 120 minutes (interquartile range, 60–300), and the most frequently used smartphone function was text messaging (61.5%). Of the patients, 26% stated that they searched for health-related information using their smartphones more than 1 time per week. The major source of health-related information was Internet browsing (50.0%), and the least sought source was the hospital's website (3.1%). Patients with heart failure expressed significantly higher needs for disease education on treatment plan, home health monitoring of blood pressure, and body weight (χ2 = 5.79, 6.27, 4.50, p < 0.05). Heart transplantation patients expressed a significant need for home health monitoring of body temperature (χ2 = 5.25, p < 0.05).ConclusionsHeart failure and heart transplantation patients show high usage of and interest in mobile health technology. A smartphone-based cardiac telerehabilitation program should be developed based on high demand areas and modified to suit to each principal diagnosis.

Family Health Monitoring System Based on the Four Sessions Internet of Things

The accelerating pace of modern life results in the lack of effective care of people’s health status. Nowadays, resorting to the technology of the Internet of Things, we can provide home health monitoring services to minimize the impact of the disease brought to people. In this article, we proposed the realization method for the architecture of the four sections of the Internet of Things oriented to home health monitoring service, furthermore, the secondary the smoothness index method is applied to the monitoring of human health index, data from body temperature detection experiments verified the feasibility of the four sessions system, which laid firm foundations for the requirement of real-time and accuracy of the Internet of Things based home health monitoring system with a common reference significance and value in use.

Increasing Patient Adherence to Home Health-Monitoring Systems

Research indicates that behavior change interventions can assist patients’ performance with respect to their health, and empower them to take a proactive role in their well-being. The objective of this research was to identify features that make remote health-monitoring systems more effective. To that end, a persuasive design approach was used to design a prototype application that encourages patients to consult their home monitoring devices appropriately. An exploratory user study was conducted to evaluate participants’ opinions about the developed prototype, their preferences, as well as their concerns regarding the different components of the mockup. In addition to the exploratory study, a comparative study was conducted, in which a conventionally designed health application was compared with a newly designed engagement application. The results of the study showed no difference on quantitative measures but participant preferences still indicated the engagement design may have potential.

Distributed Coordinator Election Scheme for QoS Support and Seamless Connectivity in WPANs

Wireless personal area networks (WPANs) are formed in a relatively small area, and coordinator that serves as a central control device plays an important role in the operation and organization of a piconet. Typical applications of WPANs include home automation systems, security systems and health monitoring systems. In these types of systems, guaranteeing seamless connectivity is very important, and communications cannot take place when the coordinator malfunctions. Thus, in the case of a breakdown, it is necessary to elect a new coordinator as soon as possible. For this reason, a distributed coordinator election scheme (DCES) is proposed that considers not only QoS support but also network connectivity in an effort to avoid possible network partition. Simulation results show that the proposed scheme can elect a new coordinator while maintaining QoS and guaranteeing connectivity for a limited period.

Development of u-House Gateway for u-Healthcare

Objective: Ubiquitous healthcare (u-Healthcare) is an emerging paradigm in the healthcare environment. One of the most promising applications for u-Healthcare is the ubiquitous home health monitoring system. This paper addresses two significant challenges in the successful application of the ubiquitous home health monitoring system: the uniform integration of measured biosignal data and easy access to monitored biosignal data. Methods: We used the Medical waveform description Format Encoding Rule (MFER) standard to encode biosignal data. A web-based MFER upload ActiveX control was designed and implemented to transfer MFER files to the central repository server in a near real-time basis. All of the integrated biosignal data were then accessed and managed through the central repository server. Results: We developed a u-House server that can serve as a uniform data transferer to integrate measured biosignal data from u-House homes into the remote central repository server. We developed user-friendly web services that allow users to easily search and view monitored biosignal data. Conclusion: The results of this study suggest that the MFER standard can be easily adapted to u-Healthcare systems and that a web-based ubiquitous home health monitoring system has advantages of ubiquitous access and scalability. (Journal of Korean Society of Medical Informatics 15-4, 465-474 2009)

Older Adultsʼ Perception and Use of PDAs, Home Automation System, and Home Health Monitoring System

Older Adultsʼ Perception and Use of PDAs, Home Automation System, and Home Health Monitoring System

Applying the ISO/IEEE 11073 Standards to Wearable Home Health Monitoring Systems

The goal of this effort was to investigate the feasibility of applying the ISO/IEEE 11073 (a.k.a. X73) standards, originally intended for bedside monitoring in hospital environments, to wearable, multi-sensor monitoring systems designed for home healthcare. The X73 upper-layer sub-standards (i.e., nomenclature specification, domain information model, application profiles, and vital sign device descriptions) were adopted and implemented on microcontroller-based sensor hardware to provide plug-and-play medical components. Three types of system elements (base stations, data loggers, and sensor units) perform the functionality required in this standards-based home health monitoring system and communicate using Bluetooth wireless modules. The base station incorporates a LabVIEW interface running on a personal computer. Each data logger and sensor unit is implemented on a microcontroller-driven embedded platform. Sensor units include wearable sensors (e.g., electrocardiograph, pulse oximeter) and nearby sensors (e.g., weight scale, ambient environment sensors). The standards-based prototype system with an open architecture achieves plug-and-play performance suitable for a home environment. Each wireless element in the body/home area network can automatically detect other nearby devices, associate with them, and exchange data with them as appropriate. With minor modifications, the X73 standards can be successfully applied to wearable, wireless, point-of-care systems in the home.

An Application of Neural Network Modeling to Diagnose Eating Behavior of Seniors in Smart Houses

The concept of a Smart House has received research and consumer attention as a promising solution to provide safe and independent living for an aging population. These benefits can be enriched with the incorporation of Senior Healthwatch, a home health monitoring system that collects data from the sensors installed in the house and provides critical information about the residents' health condition during their daily activity. This study introduces a neural network modeling approach combined with cluster and decision tree analyses as a means to develop the inferential logic of Senior Healthwatch. Its role is to convert the data obtained from the Smart House into more meaningful information about the residents' daily activity such as the times for meal preparation, eating, and cleaning. The results of this study can become a major feature in the Smart House technology.

Fully automated health monitoring system in the home

The aim of this study was to provide health monitoring for elderly and disabled persons at home by using fully automated signal measurement with personal identification to support daily health care and improve quality of life. We attempted to develop a home health monitoring system that did not provide any restrictions during sleep, bathing or elimination. A bedroom, bathroom, and separate toilet were built. The system consisted of monitoring devices and a computer terminal for collecting data. Data were automatically collected from monitoring devices placed in the bed, bath, and toilet and were transferred to a data terminal in the bedroom and stored for further analysis. The system was tested by a young healthy subject who stayed overnight in the rooms. Data were obtained without any trouble. The data acquisition system is now being used in experimental rooms in our laboratory and in a house located in Takaoka City, Toyama Pref. Japan. Data are being extensively collected.