Monitoring Of Elderly People Research Articles

UWB/BLE Tracking System for Elderly People Monitoring.

Localization systems are the source of data that allows to evaluate elderly person’s behaviour, to draw conclusions concerning his or her health status and wellbeing, and to detect emergency situations. The article contains a description of a system intended for elderly people tracking. Two novel solutions have been implemented in the system: a hybrid localization algorithm and a method for wireless anchor nodes synchronization. The algorithm fuses results of time difference of arrival and received signal strength measurements in ultrawideband (UWB) and Bluetooth Low Energy (BLE) radio interfaces, respectively. The system allows to change the intensity of UWB packets transmission to adapt localization accuracy and energy usage to current needs and applications. In order to simplify the system installation, communication between elements of the system infrastructure instead of wire interfaces is performed over wireless ones. The new wireless synchronization method proposed in the article consists in retransmission of UWB synchronization packets by selected anchor nodes. It allows for extension of the system coverage, which is limited by the short range of UWB transmission. The proposed solution was experimentally verified. The synchronization method was tested in a laboratory, and the whole system’s performance was investigated in a typical flat. Exemplary results of the tests performed with older adult participation in their own homes are also included.

Wi‐Fi sensing: applications and challenges

Wi-Fi technology has strong potentials in indoor and outdoor sensing applications; it has several important features which makes it an appealing option compared to other sensing technologies. This study presents a survey on different applications of Wi-Fi-based sensing systems such as elderly people monitoring, activity classification, gesture recognition, people counting, through the wall sensing, behind the corner sensing, and many other applications. The challenges and interesting future directions are also highlighted.

Detection of fall for the elderly in an indoor environment using a tri-axial accelerometer and Kinect depth data

Monitoring elderly people who are living alone is a crucial task as they are at great risk of fall occurrence. In this paper, we present a robust framework for fall detection that makes use of two different signals namely tri-axial data from an accelerometer and depth maps from a Kinect sensor. Our approach functions at two stages. At the first stage, the accelerometer data is continuously being monitored and is used to indicate fall whenever the sum vector magnitude of the tri-axial data crosses a specific threshold. This fall indication denotes a high probability of fall occurrence. To confirm this and to avoid false alarms, the depth maps of a predefined window length captured prior to the instant of fall indication are obtained and processed. We propose a new descriptor, Entropy of Depth Difference Gradient Map that acts as a discriminative descriptor in differentiating fall from other daily activities. Finally, fall confirmation is done by employing a sparse representation-based classifier using the extracted descriptors. To ascertain the proposed model, we have performed experimental analysis using a publicly available UR Fall Detection dataset and also using a Synthetic dataset. The experimental results clearly depict the superior performance of our model.

Personal Assistance Device for Independent Senior Citizens/ Patients

Tracking the health of a person and proper medication improves their life time. Studies suggest the most of the deaths of the elderly people have occurred during the night when the person is asleep. A Caretaker cannot assist a person all the time. This work proposes a personal assistant for an elderly people or a patient. The Personal assistants can provide in-home respite care, allowing family members or other caretakers to take a temporary break. The main objective of this work is to help seniors maintain their quality of life at home and to keep them living their lives their way, as well as to lighten the load of full-time or family caretaker. This paper proposes an affordable personal assistance device for health monitoring of elderly people using different sensors which can measure pulse rate, position of elderly. Therefore the doctor can identify the abnormal values easily and can attend the patient if the device is used in the hospital. Proper intake of medicine at correct time is indicated by the display on OLED screen and an alert is produced by buzzer.

A Vision-Based System for Monitoring Elderly People at Home

Assisted living technologies can be of great importance for taking care of elderly people and helping them to live independently. In this work, we propose a monitoring system designed to be as unobtrusive as possible, by exploiting computer vision techniques and visual sensors such as RGB cameras. We perform a thorough analysis of existing video datasets for action recognition, and show that no single dataset can be considered adequate in terms of classes or cardinality. We subsequently curate a taxonomy of human actions, derived from different sources in the literature, and provide the scientific community with considerations about the mutual exclusivity and commonalities of said actions. This leads us to collecting and publishing an aggregated dataset, called ALMOND (Assisted Living MONitoring Dataset), which we use as the training set for a vision-based monitoring approach.We rigorously evaluate our solution in terms of recognition accuracy using different state-of-the-art architectures, eventually reaching 97% on inference of basic poses, 83% on alerting situations, and 71% on daily life actions. We also provide a general methodology to estimate the maximum allowed distance between camera and monitored subject. Finally, we integrate the defined actions and the trained model into a computer-vision-based application, specifically designed for the objective of monitoring elderly people at their homes.

Software architecture optimization of mobile biomedical sensor-based tools providing analytical services for disease diagnostics and assistance

The development of highly specialized mobile applications and systems has risen for several years, as we observe rapid deployment of innovative biomedical sensors and wearable technologies. The experiences gathered over 10 years of mobile medical software development, provide practical recommendations for architectural concepts utilized in analytical health-based services. Constructed systems and mobile applications in majority of cases utilize biomedical signals to identify health state of a patient, as well as to evaluate or estimate the intensity of disease symptoms. Based on these experiences, this paper proposes architectural concepts, for both mobile and web-based components aimed at acquisition and processing of biomedical data in large scale medical systems dedicated for monitoring of patients. The major assumptions for this research included utilization of wearable and mobile technologies, supporting maximum number of wearable diagnostic technologies including inertial and biomedical data streams. Although medical diagnostics and decision algorithms have not been the main aim of the research, this preliminary phase was crucial to test capabilities of existing off-the-shelf technologies and functional responsibilities of system’s logic components. Optimized architecture variants contain several schemes for data processing, moving the responsibility for signal feature extraction, data classification and pattern recognition from wearable to mobile application and further to server services. The analysis of delays during data transmission and processing, provided architecture variants’ pros and cons but most of captured recommendations for applicability of such mechanisms in the domains of medical, military and fitness. To evaluate and construct architecture, a set of alternative technology stacks and quantitative measures has been defined. The major architecture characteristics (high availability, scalability, reliability) have been defined imposing asynchronous processing of sensor data, efficient data representation, iterative reporting, event-driven processing, restricting pulling operations. Analytical processing of inertial or biomedical sensor data requires filtering, time-window processing, aggregation, application of specific algorithmic transformations etc. Combination of several data sources and correlation of biomedical parameters between each other and specific patient’s health state, extend predefined ordinarily used event triggering rules. This work provides construction details of wearable-based mobile systems with specialized aimed at health state identification and monitoring. Undertaken construction decisions have been confirmed and justified based on functional and stress tests of system components. The first deployment attempt of designed architecture and its implementation has been aimed at remote, mobile monitoring of elderly people, and preconfigured for crucial health event recognition – fainting, stroke, cardiac arrest, seizures, some classes of neurological disorders and derivatives of such conditions.

Computer Vision-based Human Activity Recognition for Elderly Home Care

Video-based monitoring of elderly people at home receives more attention in recent days. In this paper, we propose a novel approach to develop smart monitoring system for elderly people using computer vision techniques. Gaussian Mixture Model (GMM) based algorithm is used for background and foreground separation inorder to track the activities of human object. The minimum bounding box of the human object is traced and features like major axis length, minor axis length and orientation angle are extracted. The proposed approach is evaluated on the video sequences of fall dataset.

A novel solution for a Wireless Body Sensor Network: Telehealth elderly people monitoring

Event-based data transfer through Wireless Body Sensor Networks (WBSN) for monitoring the health of the elderly has so far not been successfully implemented due to limitations arising forms unreliable data, end-to-end delay during data transmission and the high energy consumption by sensors. This paper aims to improve reliability and latency and to reduce energy consumption by sensors during data transmission in WSBN. The proposed system consists of an Enhanced Reliability, Energy-Efficient and Latency (EREEAL) algorithm to reduce data losses and end-to-end delay as well as improve the transmission reliability in WBSN by sending the sensor data during different time slots using Time Division Multiple Access (TDMA) analysis and by minimizing redundant sensitive data. The result shows that the new algorithm improves reliability to 98% over the data bits generated within 8 ∼ 12 min and reduces latency to 0.635 compared to 0.875 ms in the ‘state of the art’ system. Furthermore, the reduction in latency leads to lower power consumption by sensors, reduced to 315.638*10 3JS/bits during patient data transmission using a tele-monitoring process. The proposed system concentrates on reducing interference with data between sensors and focuses on minimizing data loss during transmission. Thus, this study provides an acceptable range of reliability with reduced delay and lower power consumption due to which doctors at a remote site can obtain reliable data value for smooth monitoring.

RETRACTED ARTICLE: Evaluation of artificial intelligence techniques for the classification of different activities of daily living and falls

Automatic detection of falls is extremely important, especially in the remote monitoring of elderly people, and will become more and more critical in the future, given the constant increase in the number of older adults. Within this framework, this paper deals with the task of evaluating several artificial intelligence techniques to automatically distinguish between different activities of daily living (ADLs) and different types of falls. To do this, UniMiB SHAR, a publicly available data set containing instances of nine different ADLs and of eight kinds of falls, is considered. We take into account five different classes of classification algorithms, namely tree-based, discriminant-based, support vector machines, K-nearest neighbors, and ensemble mechanisms, and we consider several representatives for each of these classes. These are all the classes contained in the Classification Learner app contained in MATLAB, which serves as the computational basis for our experiments. As a result, we apply 22 different classification algorithms coming from artificial intelligence under a fivefold cross-validation learning strategy, with the aim to individuate which the most suitable is for this data set. The numerical results show that the algorithm with the highest classification accuracy is the ensemble based on subspace as the ensemble method and on KNN as learner type. This shows an accuracy equal to 86.0%. Its results are better than those in the other papers in the literature that face this specific 17-class problem.

Detecting Abnormal Human Behavior in Microgrid Based on Machine Learning

Development of electronic systems and intellectual controls and information exchange has led to the formation of a new informatics and energy concept SmartGrid, which addresses the local electrical engineering objects - MicroGrid. MicroGrid contains a certain set of sources and loads that are typically connected to a centralized power grid. As part of MicroGrid, there are a number of management and device-specific tasks that result in the need to handle large information flows. Compared to fully automated systems, Microgrid is characterized by the presence of a person, which leads to the need to take into account subjective factors - the presence of a person, its interference in the functioning of electrical equipment and the impact of his actions on the working modes of the whole system. The presence of a human factor leads to the need to operate large volumes of data. In turn, it forces researchers to turn to specialized methods of working with large data (Big Data). Big Data is a series of approaches, tools and methods for processing structured and unstructured data of large volumes to produce results that are suitable for human perception and effective in the conditions of their continuous increase. To solve the problem of processing large amounts of data, it is necessary to use methods of machine learning (Machine Learning). Machine Learning as a branch of informatics uses statistical techniques to give computer systems the ability to learn (that is, to gradually improve performance in a particular task) without explicit programming. In this paper, Anomaly Detection was considered as one of the Machine Learning tools for defining anomalies in MicroGrid. One of the regime in MicroGrid that was considered as anomaly is non-usual activity, non-typical behavior of the person – “human factor”. Research directed to revealing of non-typical (abnormal) behavioral models is topical for the systems of psycho-physiological monitoring of elderly people, persons who need constant observation and care, persons in post-stress condition or under the extraordinary conditions. Subjective factors, in turn, can cause the appearance of emissions as a result of false interpretation or unauthorized human intervention in the work of technical equipment. In the analysis of behavioral characteristics, the task of choosing from the whole set of parameters describing the behavior and movement of a person, the very decisive parameters on the basis of which it is possible to detect emissions or novelty of behavior. In the application to distributed generation MicroGrid, the problem of detecting user behavior abnormalities is considered. The problem is solved by the use of Machine Learning methods, in particular, the Anomaly Detection method. As the key parameters for the simplest case of detecting abnormal behavior, we consider the average power consumption at five-minute intervals, as well as the number of triggers of the motion sensor installed in the MicroGrid indoor space.Ref. 12, fig. 4, tabl. 2.

A Wearable Haptic System for the Health Monitoring of Elderly People in Smart Cities

A sensor-fusion wearable health-monitoring system with integrated haptic feedback was previously introduced by our research group. The system's components are the following: a chest-worn device with an embedded controller board, an electrocardiogram (ECG) sensor, a temperature sensor, an accelerometer, a vibration motor, a colour-changing light-emitting diode (LED) and a push-button. This multi-sensor device makes possible to collect biometric and medical monitoring data from its wearer. The data provide a real-time indication of the wearer's health state and can also be further analysed later for medical diagnosis. The embedded vibration motor can actuate distinctive haptic feedback patterns according to the wearer's health state. The embedded colour-changing LED provides the wearer with an additional intuitive visual feedback of the current health state, and the wearer can report a potential emergency condition by using the push-button. In this paper, a conceptual case study is presented concerning possible applications for the health monitoring of elderly people in smart cities. The proposed system aims at reducing risk by assessing individual and overall potentially-harmful situations. A data collection and analysis are also presented to demonstrate that the system can provide compelling vibrotactile feedback.

Pose-Invariant Face Detection by Replacing Deep Neurons with Capsules for Thermal Imagery in Telemedicine.

The aim of this work was to examine the potential of thermal imaging as a cost-effective tool for convenient, nonintrusive remote monitoring of elderly people in different possible head orientations, without imposing specific behavior on users, e.g., looking toward the camera. Illumination and pose invariant head tracking is important for many medical applications as it can provide information, e.g., about vital signs, sensory experiences, injuries, wellbeing. In the performed experiments, we investigated the influence of different modifications of images (rotation, displacement of facial features, and displacement of facial quarters) on the prediction accuracy. Specifically, two models were tested on the set of collected low-resolution thermal images: Inception V3 Convolutional Neural Network (CNN) and Hinton's Capsule Network. The preliminary results confirm that the prediction ability of the model based on capsules can deal with different head orientations much better than CNN (for the 45° head rotation Capsule Network achieved ~% accuracy while CNN only 9.5%).

A medication self-management system with real-time health monitoring

India is the world's second largest country with lots of human resource. According to a survey of United Nations Department of Economic and Social Affairs, Population Division, with a tremendous medication and monitoring facilities in the health care, the rise in the elderly population will be from 7.5% in 2010 to 11.1% in 2025.However half of the elderly population are dependent and experience greater burden of ailments like the cardiovascular and circulatory illness, arthritis etc. With most of the youngsters working, the issue in taking care of the elderly people needs to be automated in all dimensions. The proposed work aims at designing a wearable device for health monitoring of elderly people who are not in a position to move swiftly. The device monitors the user’s ECG and pulse rate. The device is compact and user friendly. Besides, the proposed system also includes an automated pill box which issues the right pill on command that is loaded al-ready in it.The sensed data is intimated through an android application to the concerned person. On reception, the data is analyzed and the person sends an appropriate code using a telegram application. This code activates the pill box to open up the right compartment and delivers the pill to the ailing elderly patient. Further monitoring can be done after the medication to ensure the patient's condition. The set up can as well be fitted in a wheel chair for the timely delivery of the pills to a person moving in it.

Internet of Things based Ambient assisted living for Elderly People Health Monitoring

Ambient Assisted Living (AAL) is a recent communication technology, which makes an intelligent object in the environment to support the elderly people in living independently. An Ambient Intelligence is a technology and it Aims to build a safe environment around the assisted people. Nowadays, the population of elderly people in living alone keeps on increasing. Hence, it becomes a major impact in our society. The AAL based Elderly people monitoring system requires the efficient and cost effective solution. The proposed system performs three operations such as Elderly people monitoring, activity recognition and health status prediction using Support Vector Machine (SVM) Algorithm. The sensor's (Accelerometer, Temperature sensor, Pulse rate sensor) are connected to the Arduino micro controller and the generated data is stored in cloud storage (Thing Speak). The health status prediction part introduces SVM algorithm and it classifies the data from Thing Speak. Finally, the proposed system improves the prediction accuracy and also provides the cost effective solution of this problem.

An Energy Efficient, Minimally Intrusive Multisensor Intelligent System For Health Monitoring Of Elderly People

abstract Most of the existing systems for elderly health monitoring deploy a large number of cognitive sensors including wearable sensors for physiological parameter measurement. Increasing number of sensors not only make the system power consuming and expensive but also intrusive in nature. However, there exists very limited research on power saving algorithms in such systems incorporating customer friendly features. In this paper, we report a modified health monitoring system which addresses both these issues. The central controller unit has an in-built algorithm based on two level adaptive branch prediction techniques to detect the period of inactivity of sensor nodes. Further, only one wearable heart rate sensor node is included in the system which measures the heart rate and detects abnormality. The central controller signals an alarm to the user to wear this predicting the sleeping time. This makes the system minimally intrusive and user friendly. Thus the multi-sensor network consists of motion sensor, current sensor and a wearable heart rate sensor along with a central controller unit. The prototype of the whole system has been installed in the house of elderly person and it has been observed that the time of prediction was close to the actual time for more than 90% of the days for a test period of one month. An average of 68% power saving has been achieved in the modified system.

Feature based fall detection system for elders using compressed sensing in WVSN

In general there is a steep increase in the number of cases related to elderly people falling down and getting hospitalized since they are living alone. This increases the need for an efficient and low cost surveillance based fall detection system. Wireless video sensor network (WVSN) can be used for such surveillance applications like monitoring elderly people at home, old age homes or hospitals. But there are some limitations in WVSN like memory constraint, low bandwidth and limited battery life. A light weight fall detection algorithm with efficient encoding technique is needed to make WVSN suitable for health care applications. In this paper a simple feature based fall detection system using compressed sensing algorithm is proposed and it is compared with the existing method. This proposed framework shows 82.5% reduction in time and 83.75% reduction in energy compared to raw frame transmission. The average percentage of space saving achieved by this proposed work is 83.81% which shows 30% increase when compared to the existing method.

The motion influence on respiration rate estimation from low-resolution thermal sequences during attention focusing tasks.

Global aging has led to a growing expectancy for creating home-based platforms for indoor monitoring of elderly people. A motivation is to provide a non-intrusive technique, which does not require special activities of a patient but allows for remote monitoring of elderly people while assisting them with their daily activities. The goal of our study was to evaluate motion performed by a person focused on a specific task and check if this motion disrupts estimation of respiration rate. The preliminary results show that it is possible to reliable estimate respiration rate by focusing attention of a patient on a certain activity. The respiratory rate analyzed for silent reading task was estimated with mean error 0.27 breaths per minute (bpm), while for reading aloud task with 1.18 bpm. The observed head motion during the reading aloud task was 1.5 higher that for silent reading and about two times smaller for a case in which subjects were not focused on any task.

A Study of Wearable Bio-Sensor Technologies and Applications in Healthcare

In today’s world the rapid advancements in Micro-Electromechanical Systems (MEMS) and Nano technology have improved almost all the aspects of daily life routine with the help of different smart devices such as smart phones, compact electronic devices etc. The prime example of these emerging developments is the development of wireless sensors for healthcare procedures. One kind of these sensors is wearable bio-sensors. In this paper, the technologies of two types of bio-sensors (ECG, EMG) are investigated and also compared with traditional ECG, EMG equipment. We have taken SHIMMERTM wireless sensor platform as an example of wearable biosensors technology. We have investigated the systems developed for analysis techniques with SHIMMERTM ECG and EMG wearable bio-sensors and these biosensors are used in continuous remote monitoring. For example, applications in continuous health monitoring of elderly people, critical chronic patients and Fitness & Fatigue observations. Nevertheless, early fall detection in older adults and weak patients, treatment efficacy assessment. This study not only provides the basic concepts of wearable wireless bio-sensors networks (WBSN), but also provides basic knowledge of different sensor platforms available for patient’s remote monitoring. Also various healthcare applications by using bio-sensors are discussed and in last comparison with traditional ECG and EMG is presented.

RSN Editorial 2017

It is a pleasure to report that the Premium Award for papers published in the journal in 2015 is made to Branka Jokanovic, Moeness Amin, Yiming Zhang and Fauzia Ahmad of Villanova University, USA, for their paper Multi-Window Time and Frequency Signature Reconstruction from Undersampled Continuous-Wave Radar Measurements for Fall Detection’, which appeared in the February 2015 Special Issue on Application of Radar to Remote Patient Monitoring and Eldercare (vol.9. no.2, pp 173-183). This is an example of an exciting new application of passive radar. It is attractive to provide some form of indoor monitoring of elderly people living in sheltered accommodation and particularly to detect falls, which are a significant issue for the elderly. Radar-based techniques have the advantages over video monitoring that they are not invasive of privacy and do not depend on particular lighting conditions. This might be particularly important in monitoring an individual in the bathroom, where the likelihood of slippage or falling might be high. Furthermore, since elderly people tend to well-defined patterns of behaviour, a monitoring system of this kind could potentially ‘learn’ such patterns and detect and flag any anomalies. The paper analyses the radar time-frequency signature of fall events, as well as bending over, sitting and standing, using an approach based on compressive sensing and multi-window analysis. This demonstrates that falls can reliably be detected and classified. We warmly congratulate the authors on this award. The names of the members of the Editorial Board are listed inside the front cover of this journal; the members are distinguished researchers who represent the full scope of the journal, and they come from all over the world. They help with reaching decisions on submitted papers, encouraging authors to submit their work to the journal, and proposing topics for Special Issues and Special Sections. Finally, we express our gratitude to the referees for their time, expertise and hard work in reviewing papers. This is an essential part of the publication process, but necessarily the referees must remain anonymous. This Editorial is one place where we can acknowledge their contribution and record our thanks to them.

New Immunoassays of Bioactive TSH Improve Detection of Thyroid Disorders

Background: TSH assays have long been showing discordances, especially in the upper limit of the reference range. There is thus a need to improve TSH measurement and detection of thyroid dysfunction. This study aimed at evaluating the ability of new TSH assays to detect early thyroid deficiency in a healthy population with no suspicion of thyroid disorders. Methods: We selected 797 subjects without declared hyperthyroidism, hypothyroidism, goiter, personal or familial thyroid dysfunction, thyroid autoimmunity or medication among 1,623 individuals recruited in a multicenter prospective study (2012-2014). Hypothyroidism-related clinical signs (hCS) were collected, TSH and thyroid hormones levels measured by routine tests and 4 new assays based on calibration with a biologically active recombinant TSH. Analysis was performed as a function of gender and age (<60 y and ≥ 60 y). Results: Expression of hCS appeared to vary as a function of gender and age in healthy individuals. The cohort displayed a higher percentage of males with no hCS in both 97.5th centile), FT4 levels were found to be slightly reduced and the expression of hCS increased (median, 2.0 vs. 1.0) when TSH was measured by the new assays compared to the routine assay. Detection of subclinical TSH was particularly improved in elderly women. Conclusion: New assays measuring bioactive TSH exhibited a good correlation with expression of hCS and FT4 levels, especially in ≥ 60 y women. Such assays may improve the monitoring of elderly people as well as the whole healthy population, especially in the context of environmental hazards.