Elderly Monitoring System Research Articles

Optimized neural network models for low power elderly monitoring system in Internet of things

This paper proposes a low power consuming system for monitoring elderly people’s activities and their health conditions. The proposed system has two activity recognition modules: smartphone sensor-based wearable module; infrared grid sensor-based remote module. The two activity recognition modules work in a coordinated way. The fraction of the time the person is detected by the infrared sensor, the smartphone remains idle. As a result, energy consumption in the smartphone is reduced significantly, and hence the battery lifetime is increased. In the smartphone, a Feed-forward Neural Network (FNN) based activity recognition algorithm is implemented using fixed-point computation to further reduce energy consumption. A Convolutional Neural Network is used in the infrared sensor-based activity recognition module. The proposed system also has real-time health monitoring capability, which is based on ECG signal classification. A FNN leveraging fixed-point operation is used for ECG signal classification on an embedded ARM processor. Proposed fixed-point implementations of the FNNs are faster than floating-point implementation and require 50% less memory to store the neural network model parameters without loss of classification accuracy.

Indoor Infrared Sensor Layout Optimization for Elderly Monitoring Based on Fused Genetic Gray Wolf Optimization (FGGWO) Algorithm.

With the increasing aging of the global population, the efficiency and accuracy of the elderly monitoring system become crucial. In this paper, a sensor layout optimization method, the Fusion Genetic Gray Wolf Optimization (FGGWO) algorithm, is proposed which utilizes the global search capability of Genetic Algorithm (GA) and the local search capability of Gray Wolf Optimization algorithm (GWO) to improve the efficiency and accuracy of the sensor layout in elderly monitoring systems. It does so by optimizing the indoor infrared sensor layout in the elderly monitoring system to improve the efficiency and coverage of the sensor layout in the elderly monitoring system. Test results show that the FGGWO algorithm is superior to the single optimization algorithm in monitoring coverage, accuracy, and system efficiency. In addition, the algorithm is able to effectively avoid the local optimum problem commonly found in traditional methods and to reduce the number of sensors used, while maintaining high monitoring accuracy. The flexibility and adaptability of the algorithm bode well for its potential application in a wide range of intelligent surveillance scenarios. Future research will explore how deep learning techniques can be integrated into the FGGWO algorithm to further enhance the system's adaptive and real-time response capabilities.

Application of Posture Recognition Model Based on Improved CORDIC Algorithm in Spatial Health Monitoring of the Elderly Living Alone

In an aging society, accidental falls are highly likely to occur due to external factors or the incidence of diseases in the elderly. Serious consequences can arise when an elderly person living alone falls and is unable to get up to call for help. To address the issue of automatic assistance for elderly individuals living alone when they fall by accident, and the problem of behavioral analysis in daily life, this paper designs and develops an elderly monitoring system through the study of feature extraction algorithms and posture recognition algorithms. Furthermore, this paper has also developed a set of three-dimensional spatial angle calculation methods suitable for implementation on a microcontroller. The CORDIC algorithm can compute two-dimensional spatial angles through simple addition, subtraction, and bit shifting operations. Through careful study of the CORDIC algorithm, it was found that applying the CORDIC algorithm twice can achieve the calculation of three-dimensional spatial angles. Through experimental testing, we can see that the algorithms proposed in this paper have achieved good experimental results in terms of computational accuracy and operation time, reaching a relatively advanced level.

Extending SysML to Integrate Cost Analysis Into Model-Based Systems Engineering

Model-based systems design (MBSD), a current trend adopted by INCOSE, employs the systems modeling language (SysML), a standard introduced by OMG and INCOSE. Though there are numerous works on integrating performance exploitation in SysML, cost is not sufficiently explored as a driving design parameter. By integrating cost analysis in a popular modeling language like SysML, the proposed approach may be applied to any system designed using standardized languages and tools. In this work, we integrate cost analysis within SysML models at a generic level to explore design alternatives under specific cost and performance restrictions and perform tradeoff analysis. The proposed SysML extensions provide: 1) cost-related entities to encode cost aspects, such as capital and operating expenses, and 2) functions that enable the automatic computation of costs; these extensions are contained in a custom SysML cost profile. The feasibility and benefits of the approach are explored in two distinct real-world case studies with different purpose and characteristics. 1) Configuring a remote elderly monitoring system, taking into consideration patients' budgetary and operational concerns regarding the equipment installed in their homes. In this case, patients had the opportunity to evaluate and prioritize their concerns prior to using the system. 2) Exploring the improvement of the passengers' comfort as a level of service indicator in the Athens Metro railway system, taking into account operational cost constraints. In this case, the operator obtained forecasts of performance and cost of the metro system operation in order to choose between different operational policies.

A Mobile IoT-based Elderly Monitoring System for Senior Safety

A Mobile IoT-based Elderly Monitoring System for Senior Safety

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.

PATROL: Participatory Activity Tracking and Risk Assessment for Anonymous Elderly Monitoring.

There has been a subsequent increase in the number of elderly people living alone, with contribution from advancement in medicine and technology. However, hospitals and nursing homes are crowded, expensive, and uncomfortable, while personal caretakers are expensive and few in number. Home monitoring technologies are therefore on the rise. In this study, we propose an anonymous elderly monitoring system to track potential risks in everyday activities such as sleep, medication, shower, and food intake using a smartphone application. We design and implement an activity visualization and notification strategy method to identify risks easily and quickly. For evaluation, we added risky situations in an activity dataset from a real-life experiment with the elderly and conducted a user study using the proposed method and two other methods varying in visualization and notification techniques. With our proposed method, 75.2% of the risks were successfully identified, while 68.5% and 65.8% were identified with other methods. The average time taken to respond to notification was 176.46 min with the proposed method, compared to 201.42 and 176.9 min with other methods. Moreover, the interface analyzing and reporting time was also lower (28 s) in the proposed method compared to 38 and 54 s in other methods.

An Elderly Monitoring System Based on Multiple Ultra-Sensitive Vibration and Pneumatic Sensors

In recent years, aging of the population has become a major social problem. Various types of sensor networks have been applied to elderly monitoring for addressing the care problems of the elderly living alone. We have also proposed elderly monitoring systems based on wireless sensor network devices. However, vision-based sensors can also cause a mental burden on the elderly’s privacy. Furthermore, the number of sensors must be reduced, if possible. Therefore, this study proposes an elderly monitoring system composed of two vibration sensors placed on the floor and a pneumatic sensor placed on the bed. Because both sensors include considerable measurement noise, we propose a human behavior estimation method that includes anomaly detection from time-series measurement data using an autocorrelation coefficient. Finally, we discuss the effectiveness and usability of the proposed system through several experimental results. The accuracy of walking detection reaches 94.4%, while the error of heartbeat detection is 3.01 bpm.

Saliency based human fall detection in smart home environments using posture recognition.

The implementation of people monitoring system is an evolving research theme. This paper introduces an elderly monitoring system that recognizes human posture from overlapping cameras for people fall detection in a smart home environment. In these environments, the zone of movement is limited. Our approach used this characteristic to recognize human posture fastly by proposing a region-wise modelling approach. It classifies persons pose in four groups: standing, crouching, sitting and lying on the floor. These postures are obtained by calculating an estimation of the human bounding volume. This volume is estimated by obtaining the height of the person and its surface that is in contact with the ground according to the foreground information of each camera. Using them, we distinguish each postures and differentiate lying on floor posture, which can be considered as the falling posture from other postures. The global multiview information of the scene is obtaining by using homographic projection. We test our proposed algorithm on multiple cameras based fall detection public dataset and the results prove the efficiency of our method.

Gamification applied to an elderly monitoring system during the COVID-19 pandemic

Faced of a global COVID-19 pandemic, many people are going through a period of social isolation and with that, many disorders has been developed. One of the vulnerabe and high risk groups is the elderly and special attention is needed with that group, in order to prevent the situation from getting worse. Gamification is an excellent tool for engaging people in different contexts. Thus, the aim of this work was to propose a gamification for an elderly monitoring system and to validate it with some users. Two Gamification Design Frameworks were used: the MDA Framework and the 6D Framework, with six steps to develop the proposal and create the names of patents, levels, scores and ratings. After that, a survey on Google Forms was carried out with 50 people to validate all the names suggested and 94.67% considered the proposed names very interesting for the gamification of the platform. Some people had certain dificulty to understand the proposal because they were not using and testing the plataform. Thats the importance of prototyping, for people have a complete user experience and to validate all the proposals aspects. The positive result of this work was important, because now we can advance in the development of future works, developing the system and aplicating the gamification on it.

Human Skeleton Estimation Method Focusing on the Difference of Human Position and Action from an Extremely Low-Resolution FIR Image Sequence

Elderly monitoring systems are gaining attention in the modern aging society. For the purpose, Far-InfraRed (FIR) sensors are often used, because they can avoid privacy concerns and are robust to environmental lightings. The authors have previously proposed several methods for human skeleton estimation from an extremely low-resolution FIR image sequence whose resolution is 16 × 16 pixels. For more accurate estimation, this paper proposes a method that is robust to variations of human positions and actions in the FIR sequences. Specifically, to extract features robust to the human positions from the images by using a Convolutional Neural Network (CNN), a global max-pooling layer is inserted into the last layer instead of multiple pooling layers which are not suitable for low-resolution inputs. Also, a network with two branches is introduced that focuses on capturing spatial and temporal information respectively. Moreover, the network has a weighted sum mechanism of their outputs, which depends on the human actions. For evaluation, a dataset was created by capturing action sequences of a human at various positions in the FIR images. Through an experiment, we confirmed that the human motion can be smoothly estimated and that the estimation accuracy is improved by the proposed method.

An enhanced, efficient and affordable wearable elderly monitoring system with fall detection and indoor localisation

According to the statistics of National Council on Aging (NCOA) nowadays, the rate of death among old-aged people has reached a critical state that, for every 11 seconds an older adult is being treated for a fall and for every 19 minutes a death is reported. Hence, it is obvious that an innovation is required to detect these falls in the elderly to provide them timely treatment. We have built a wearable device for the fall detection. The wearable device can detect falls indoor and outdoor. Also, we provide exact location of fall, health attributes of the user to the caretaker and with hospital suggestions. Indoor localisation is also implemented. Importantly, without any wearables or camera, the rest room falls have been identified in an innovative yet economical way.

An enhanced, efficient and affordable wearable elderly monitoring system with fall detection and indoor localisation

An enhanced, efficient and affordable wearable elderly monitoring system with fall detection and indoor localisation

Data Analysis Model Design of Health Service Monitoring System for China's Elderly Population: The Proposal of the F-W Model Based on the Collaborative Governance Theory of Healthy Aging.

In the era of artificial intelligence, big data and 5G, health care for elderly people is facing an important digital transformation. The objective of this study is to design the data analysis module of the elderly health service monitoring system (HSMS) and attempt to put forward a new healthy aging (HA) model that is applicable not only to the individual HA, but also to the regional HA system. Based on the HA theory of collaborative governance, we divided the elderly HSMS into four modules, including physical health, mental health, ability of daily activity, and social participation. Then, factors that influence HA were assessed by stepwise logistic regression to build the analysis model, using the public micro-panel data of the China Health and Retirement Longitudinal Survey (CHARLS). Age (odds ratio (OR) = 1.55 (95% confidence interval (CI): 1.06–2.27)), living in urban areas (OR = 1.57 (95% CI: 1.03–2.39)), being literate (OR = 1.51 (95% CI: 1.01–2.23)), expecting to get long-term health care in the future from their grown children (OR = 1.69 (95% CI: 1.10–2.61)) and having literate grown children (OR = 2.01 (95% CI: 0.26–0.97)) had a significant positive impact on HA of elderly people. Therefore, the F-W (factors and weighs, also family and welfare) model is proposed in this paper. The outcomes can contribute with designing HSMS for different provinces and several different regions in China and leave a door open to improve the model and algorithm application for HSMS in the future studies.

A Multi-Static Radar Network with Ultra-Wideband Radio-Equipped Devices.

A growing number of devices, from car key fobs to mobile phones to WiFi-routers, are equipped with ultra-wideband radios. In the network formed by these devices, communicating modules often estimate the channel impulse response to employ a matched filter to decode transmitted data or to accurately time stamp incoming messages when estimating the time-of-flight for localization. This paper investigates how such measurements of the channel impulse response can be utilized to augment existing ultra-wideband communication and localization networks to a multi-static radar network. The approach is experimentally evaluated using off-the-shelf hardware and simple, distributed filtering, and shows that a tag-free human walking in the space equipped with ultra-wideband modules can be tracked in real time. This opens the door for various location-based smart home applications, ranging from smart audio and light systems to elderly monitoring and security systems.

Elderly monitoring system in a smart city environment using LoRa and MQTT

With the rapid surge of M-Health and smart city systems, the need to accommodate sensors and actuators to achieve effective automation turns out to be imposed, for an efficient machine-to-machine communication to be maintained. In this regard, the key elements necessary for a successful M-Health system to take place are discovered to be the power consumption and interoperability maintaining factors. In this context, the present study is conducted to devise a special elderly tracking and monitoring system, closely connected to a walking stick device. The proposed design involves a cane cased transmitter node, enabling to send data related to the position and the state of the elderly to a base station, through implementation of the LoRa technology. The latter would then apply the message queuing telemetry transport (MQTT) protocol to interact with the environment once a fall proves to take place. For the purpose of evaluating the advanced design associated coverage range and power consumption rate, as enhanced via LoRa network, several experimental tests have been administered. The achieved results appear to reveal that the suggested architecture recorded average covered area turns out to be of the rate of 6 km 2 .

RETRACTED ARTICLE: Fall detection based on posture classification for smart home environment

Automatic human fall detection is one of the major components in the elderly monitoring system. Detection of human fall in a smart home environment can be utilized as a means to improve the quality of care offered to elderly people thus reducing the risk factor when they are alone. Recently various fall detection approaches have been proposed, among which computer vision based approaches offer promising and effective solutions. In this paper, an analysis of fall detection is carrier out based on the automatic, feature learning in a hybrid approach. Initially, a model is generated using the training dataset that contains samples of both fall and normal active events. Then key frames are extracted from the video sequence that is subjected to two stream classification. The classification results are approved if both the streams project the same results, failing so, additional information are used to classify the fall from the normal activity. The selection of key frames depends on the displacement in the centroid of the detected object have threshold greater than the predefined value. Experiments show that the proposed approach achieves reliable results compared with other methods, and a better result is achieved in our method even when training with fewer training samples.

IOT based Blood Booking for Hospitals and Remote Elderly Monitoring System

IOT based Blood Booking for Hospitals and Remote Elderly Monitoring System

Fall Detection System Using Global System for Mobile Communication

Background: In this modern and fast-moving world, elderly’s safety and security have become an important issue. According to the World Population Prospects of the United Nations 2015, there is 12.3 per cent population aged 60 and above globally and it is the fastest growing population at a rate of 3.26 per cent per year. In order to reduce the worries about the elderly living alone at home, Elderly Monitoring System is required for continuous monitoring. “Fall” is one of the critical incidents for the elderly living alone as it causes serious injuries. A fall detection system using global system for mobile communication can help to reduce the time of unaware of their next of kin.
 Keywords: online database, fall detection, Global Positioning System (GPS), Global System for Mobile Communication (GSM)

The urgency of testing standard for elderly’s monitoring systems in Indonesia

Due to several aspects, such as safety, applicability, and reliability of the elderly monitoring systems, it is urgently need a testing standard for this system. Commonly, the user, especially caregivers or nurse, do not know exactly that procedure. In order to educate both, consumer and producer, preventing problems which may arise among them, and guarantee that instrument is operate as well as stated on the instruction manual, which conform to a certain testing standard which introduced by legal institution (SNI or ISO), establishment a testing (physical and/or written) for this device, especially in Indonesia, is urgently needed.