Monitoring Of Elderly People Research Articles

MORTALIDADE DE PESSOAS IDOSAS POR NEOPLASIAS MALIGNAS DOS ÓRGÃOS GENITAIS

Introduction: This article discusses malignant neoplasms of the genital organs in the elderly, in both sexes, in the last ten years, as the elderly population and the number of cancers in the reproductive systems are growing. Objective: To investigate the mortality of elderly people due to malignant neoplasms of the genital organs, according to sex, in the state of Minas Gerais, in the last 10 years. Methodology: This is a descriptive study, with a quantitative, longitudinal approach, carried out using secondary data collected through DATASUS (Informatics Department of the Brazilian Unified Health System). Results and Discussion: From 2013 to 2022, 15,036 deaths of elderly men due to malignant neoplasms of the genital organs were recorded, and 7,965 deaths of elderly women, which corresponded to 65.37% and 34.62% respectively. Men were the most affected by neoplasms of the genital organs in the period studied, and deaths increased with age; It is believed that this is due to the greater omission in seeking public health assistance. In men, the most evident cancer was prostate cancer; in women, the cervix. Conclusion: Health monitoring of elderly people is essential, as this is the population that has grown the most in recent years, in addition to being the most affected by neoplasms. The research highlighted an overview of the number of cases that can favor the planning of strategies to approach this population.

Usability Evaluation of a Web-Based Remote Patient Monitoring Application: An Ongoing Study.

This study aims to discover problems and user experiences in a new released version of Sleepiz web application using heuristic evaluation and eye-tracking retrospective think-aloud performed by domain experts and end users. The web application is designed to support healthcare professionals in decision-making and monitoring of elderly people diagnosed with chronic respiratory diseases. Identification of usability problems and user experiences might contribute to improve the platform and will be reported to the developers.

Human activity recognition using smart phone for physical activity analysis

Human Activity Recognition (HAR) is an active research area that is useful in many applications, such as elderly people monitoring, health parameter analysis, fall detection, video surveillance, etc. There are many challenges in HAR, such as generalized feature extraction, varying background and illumination, etc. The sensor based and image based are two major approaches in HAR. Image based is complex and can be used in the monitoring area only.The sensor based appraches are not user friendly as an individuals need to wear the sensors on the body which is not comfortable. The use of smart phones for HAR is increasing due to advancements in machine learning algorithms. We have proposed a convolutional neural network model for the classification of activities of daily leaving (ADLs) performed by 30 individuals carrying a smartphone. The model consists of two and three dense layers, respectively. We trained the model using 7352 unique samples from the database. We obtained the highest accuracy of 95.02% when testing the result on 2947 samples with 15 epochs. The accuracy and loss plotted is shown in fig 5 a to d which indicates the proper traing of the model. The model is neither overfitted nor underfitted. The hyper-parameter tuning helped in increasing the accuracy. The classification of the physical activities can be and useful inferences can be obtained by analyzing the activities and its time. This will useful in generating the health related alerts to an individual for the activities.

Recognition human walking and running actions using temporal foot-lift features

The recognition of human walking and running actions becomes essential part of many different practical applications such as smart video-surveillance, patient and elderly people monitoring, health care as well as human-robot interaction. However, the requirements of a large spatial information and a large number of frames for each recognition phase are still open challenges. Aiming at reducing the number frames and joint information required, temporal foot-lift features were introduced in this study. The temporal foot-lift features and weighted KNN classifier were used to recognize “Walkin and“Running”actions from four different human action datasets. Half of the datasets were trained and the other half of datasets were experimentally tested for performance evaluation. The experimental results were presented and explained with justifications. An overall recognition accuracy of 88.6% was achieved using 5 frames and it was 90.7% when using 7 frames. The performance of proposed method was compared with the performances of existing methods. Skeleton joint information and temporal foot-lift features are promising features for real-time human moving action recognition.

Opportunities and Challenges in the Implementation of IoT Applications for Health Monitoring of Elderly People in the Indian Scenario

The Internet of Things (IoT)-based health monitoring has emerged as a transformative paradigm in healthcare, especially when it comes to tracking the health of senior citizens. The aging population in India is on the rise, necessitating the development of effective and scalable solutions to address the healthcare needs of the elderly. In this regard, IoT applications present promising prospects by facilitating early identification of medical problems, timely intervention, and continuous, real-time health monitoring. IoT applications can be customized to suit the varying needs of rural and urban population. However, the implementation of IoT applications in the healthcare sector in India comes with its own set of challenges. Issues related to infrastructure, data security, and the privacy concerns need to be carefully addressed to ensure the successful deployment and sustained effectiveness of these technologies. The majority of the Indian population resides in villages; therefore, cost effectiveness is also a major concern for rural population. This paper explores the opportunities and challenges associated with the implementation of IoT applications for health monitoring and aim to provide insights for policymakers, healthcare professionals, and technologists to design and implement IoT-based health monitoring systems tailored to the specific needs of the elderly population in India.

A computer vision-approach for activity recognition and residential monitoring of elderly people

In this study, we explore a human activity recognition (HAR) system using computer vision for assisted living systems (ALS). Most existing HAR systems are implemented using wired or wireless sensor networks. These systems have limitations such as cost, power issues, weight, and the inability of the elderly to wear and carry them comfortably. These issues could be overcome by a computer vision based HAR system. But such systems require a highly memory-consuming image dataset. Training such a dataset takes a long time. The proposed computer-vision-based system overcomes the shortcomings of existing systems. The authors have used key-joint angles, distances between the key joints, and slopes between the key joints to create a numerical dataset instead of an image dataset. All these parameters in the dataset are recorded via real-time event simulation. The data set has 780,000 calculated feature values from 20,000 images. This dataset is used to train and detect five different human postures. These are sitting, standing, walking, lying, and falling. The implementation encompasses four distinct algorithms: the decision tree (DT), random forest (RF), support vector machine (SVM), and an ensemble approach. Remarkably, the ensemble technique exhibited exceptional performance metrics with 99 ​% accuracy, 98 ​% precision, 97 ​% recall, and an F1 score of 99 ​%.

EatSense: Human centric, action recognition and localization dataset for understanding eating behaviors and quality of motion assessment

Current datasets for computer vision-based action recognition and localization cover a wide range of classes and challenging scenarios. However, these datasets don't cater to healthcare applications that involve long-term monitoring, tracking minor changes in movements over time for healthcare purposes, or completely modeling a specific human behavior that includes multiple sub-actions. Specifically, there are no existing datasets for research on either health monitoring on atomic-action-based eating behavior or for a full range of eating sub-actions that fully segment the main action. Addressing these gaps is valuable for extending research on the health monitoring of elderly people and is needed for creating richer and more complete descriptions of actions. This paper introduces a new benchmark dataset named EatSense that targets both the computer vision and healthcare communities and fills in the aforementioned gaps. EatSense is recorded while a person eats in an uncontrolled dining setting. The key features of EatSense are the introduction of challenging atomic actions for action recognition, the significantly diverse durations of actions that make it difficult for current temporal action localization frameworks to localize, the capability to model comprehensive eating behavior in terms of a sequence of action-based behaviors, and the simulation of minor variations in motion or performance. We conduct extensive experiments on EatSense with baseline deep learning-based approaches for benchmarking and hand-crafted feature-based approaches for explainable applications. We believe this dataset will benefit future researchers in building robust temporal action localization networks, behavior recognition, and performance assessment models for eating.

Video Based Mobility Monitoring of Elderly People Using Deep Learning Models

In recent years, the number of older people living alone has increased rapidly. Innovative vision systems to remotely assess people’s mobility can help healthy, active, and happy aging. In the related literature, the mobility assessment of older people is not yet widespread in clinical practice. In addition, the poor availability of data typically forces the analyses to binary classification, e.g. normal/anomalous behavior, instead of processing exhaustive medical protocols. In this paper, real videos of elderly people performing three mobility tests of a clinical protocol are automatically categorized, emulating the complex evaluation process of expert physiotherapists. Videos acquired using low-cost cameras are initially processed to obtain skeletal information. A proper data augmentation technique is then used to enlarge the dataset variability. Thus, significant features are extracted to generate a set of inputs in the form of time series. Four deep neural network architectures with feedback connections, even aided by a preliminary convolutional layer, are proposed to label the input features in discrete classes or to estimate a continuous mobility score as the result of a regression task. The best results are achieved by the proposed Conv-BiLSTM classifier, which achieves the best accuracy, ranging between 88.12% and 90%. Further comparisons with shallow learning classifiers still prove the superiority of the deep Conv-BiLSTM classifier in assessing people’s mobility, since deep networks can evaluate the quality of test executions.

2-D LIDAR-Based Approach for Activity Identification and Fall Detection

Activity detection is a key task in the monitoring of elderly people living alone. This is because it helps locate them and identify any accident that might occur to them. In this article, we propose a novel approach that uses 2-D light detection and ranging (LIDAR) and deep learning to perform activity detection. In a first step, our approach processes and interpolates the data collected using the 2-D LIDAR following an algorithm we propose to locate the person and identify the useful data points. In the next steps, the data are transformed into two types of representations: 1) a time-series type and 2) an image type. The time-series data are used to train different long short-term memory (LSTM) networks to identify the person and to recognize his/her activity, while the image type is used to fine-tune a convolutional neural network (CNN) for fall detection. Throughout our experiments, we show that our approach allows for the identification of people from their gait, and the detection of unsteady gait or unstable walk (i.e., when the person is about to fall or feeling dizzy) as well as the detection of up to four activities: 1) walking; 2) standing; 3) sitting; and 4) falling. The results obtained from our experiment show that the proposed method reaches an accuracy equal to 94.1% for multiclass activity detection, 98.6% for fall detection, 93.2% for person identification (for three different people), and 92.5% for unsteady walk detection.

Automatic detection of cognitive impairment in elderly people using an entertainment chatbot with Natural Language Processing capabilities

Previous researchers have proposed intelligent systems for therapeutic monitoring of cognitive impairments. However, most existing practical approaches for this purpose are based on manual tests. This raises issues such as excessive caretaking effort and the white-coat effect. To avoid these issues, we present an intelligent conversational system for entertaining elderly people with news of their interest that monitors cognitive impairment transparently. Automatic chatbot dialogue stages allow assessing content description skills and detecting cognitive impairment with Machine Learning algorithms. We create these dialogue flows automatically from updated news items using Natural Language Generation techniques. The system also infers the gold standard of the answers to the questions, so it can assess cognitive capabilities automatically by comparing these answers with the user responses. It employs a similarity metric with values in [0, 1], in increasing level of similarity. To evaluate the performance and usability of our approach, we have conducted field tests with a test group of 30 elderly people in the earliest stages of dementia, under the supervision of gerontologists. In the experiments, we have analysed the effect of stress and concentration in these users. Those without cognitive impairment performed up to five times better. In particular, the similarity metric varied between 0.03, for stressed and unfocused participants, and 0.36, for relaxed and focused users. Finally, we developed a Machine Learning algorithm based on textual analysis features for automatic cognitive impairment detection, which attained accuracy, F-measure and recall levels above 80%. We have thus validated the automatic approach to detect cognitive impairment in elderly people based on entertainment content. The results suggest that the solution has strong potential for long-term user-friendly therapeutic monitoring of elderly people.

A deep learning approach for remote heart rate estimation

Remote monitoring of elderly people or patients in home isolation is an essential part of modern telemedicine. Videoplethysmography (VPG) is a method of noncontact assessment of heart rate and other cardiovascular parameters. Many algorithms have been developed to extract and improve the quality of the VPG signal. The main objective of this study is to design a method that replaces existing multistage algorithms and provides continuous monitoring of the user’s pulse. The article presents a method of heart rate measurement based on the Long Short Term Memory (LSTM) Deep Neural Network. The proposed method outperforms the algorithm based on the analysis of the green component (G) and provides comparable results to the state-of-the-art methods such as Independent Component Analysis (ICA) and Plane Orthogonal to the Skin (POS). The best result for G was 6.49 bpm (beats per minute), ICA = 3.02 bpm, POS = 2.61 bpm, and for the proposed method was 3.26 bpm. While maintaining the accuracy comparable to ICA and POS algorithms, the LSTM network works well also beyond the visible spectrum, e.g., with infrared lighting when the color signal is not available and is easily adaptable to telemedicine applications.

Quantification of the Link between Timed Up-and-Go Test Subtasks and Contractile Muscle Properties.

Frailty and falls are a major public health problem in older adults. Muscle weakness of the lower and upper extremities are risk factors for any, as well as recurrent falls including injuries and fractures. While the Timed Up-and-Go (TUG) test is often used to identify frail members and fallers, tensiomyography (TMG) can be used as a non-invasive tool to assess the function of skeletal muscles. In a clinical study, we evaluated the correlation between the TMG parameters of the skeletal muscle contraction of 23 elderly participants (22 f, age 86.74 ± 7.88) and distance-based TUG test subtask times. TUG tests were recorded with an ultrasonic-based device. The sit-up and walking phases were significantly correlated to the contraction and delay time of the muscle vastus medialis (ρ = 0.55–0.80, p < 0.01). In addition, the delay time of the muscles vastus medialis (ρ = 0.45, p = 0.03) and gastrocnemius medialis (ρ = −0.44, p = 0.04) correlated to the sit-down phase. The maximal radial displacements of the biceps femoris showed significant correlations with the walk-forward times (ρ = −0.47, p = 0.021) and back (ρ = −0.43, p = 0.04). The association of TUG subtasks to muscle contractile parameters, therefore, could be utilized as a measure to improve the monitoring of elderly people’s physical ability in general and during rehabilitation after a fall in particular. TUG test subtask measurements may be used as a proxy to monitor muscle properties in rehabilitation after long hospital stays and injuries or for fall prevention.

Using Direct Acyclic Graphs to Enhance Skeleton-Based Action Recognition with a Linear-Map Convolution Neural Network.

Research on the human activity recognition could be utilized for the monitoring of elderly people living alone to reduce the cost of home care. Video sensors can be easily deployed in the different zones of houses to achieve monitoring. The goal of this study is to employ a linear-map convolutional neural network (CNN) to perform action recognition with RGB videos. To reduce the amount of the training data, the posture information is represented by skeleton data extracted from the 300 frames of one film. The two-stream method was applied to increase the accuracy of recognition by using the spatial and motion features of skeleton sequences. The relations of adjacent skeletal joints were employed to build the direct acyclic graph (DAG) matrices, source matrix, and target matrix. Two features were transferred by DAG matrices and expanded as color texture images. The linear-map CNN had a two-dimensional linear map at the beginning of each layer to adjust the number of channels. A two-dimensional CNN was used to recognize the actions. We applied the RGB videos from the action recognition datasets of the NTU RGB+D database, which was established by the Rapid-Rich Object Search Lab, to execute model training and performance evaluation. The experimental results show that the obtained precision, recall, specificity, F1-score, and accuracy were 86.9%, 86.1%, 99.9%, 86.3%, and 99.5%, respectively, in the cross-subject source, and 94.8%, 94.7%, 99.9%, 94.7%, and 99.9%, respectively, in the cross-view source. An important contribution of this work is that by using the skeleton sequences to produce the spatial and motion features and the DAG matrix to enhance the relation of adjacent skeletal joints, the computation speed was faster than the traditional schemes that utilize single frame image convolution. Therefore, this work exhibits the practical potential of real-life action recognition.

System Architecture of a Proactive Intelligent System to Monitor Health of Older Adults Living Alone

Worldwide improvements in the quality of life highlight immense need to have a remote health monitoring system that can provide critical biomedical data. This paper presents a low-cost health monitoring system, forming part of the Internet of Things (IoT), which aims at continuous, 24/7 monitoring of elderly people and disabled people. The system is implemented with a variety of sensors, for example, temperature, heart rate, and movement measurements, to observe a person’s status. Doctors may also prescribe this system with a specific number and type of sensor, depending on a patient’s condition. In a case study, three sensors measured the status of a person during the day. The measurements reflected the actions of the person as he/she relaxed or was active, in addition to monitoring his/her state of health. The observed data were recorded in a database that can be displayed by authorized caregivers. Results witnessed the efficacy of the proposed system. The proposed system finds enormous potential in giving remote healthcare facilities, especially to unaccompanied older adults.

Radar system with dedicated planar traveling wave antennas for elderly people monitoring

Abstract. Radar systems for contactless vital sign monitoring are well known and an actual object of research. These radar-based sensors could be used for monitoring of elderly people in their homes but also for detecting the activity of prisoners and to control electrical devices (light, audio, etc.) in smart living environments. Mostly these sensors are foreseen to be mounted on the ceiling in the middle of a room. In retirement homes the rooms are mostly rectangular and of standardized size. Furniture like beds and seating are found at the borders or the corners of the room. As the propagation path from the center of the room ceiling to the borders and corners of a room is 1.4 and 1.7 time longer the power reflected by people located there is 6 or even 10 dB lower than if located in the center of the room. Furthermore classical antennas in microstrip technology are strengthening radiation in broadside direction. Radar systems with only one single planar antenna must be mounted horizontally aligned when measuring in all directions. Thus an antenna pattern which is increasing radiation in the room corners and borders for compensation of free space loss is needed. In this contribution a specification of classical room sizes in retirement homes are given. A method for shaping the antenna gain in the E-plane by an one-dimensional series-fed traveling wave patch array and in the H-plane by an antenna feeding network for improvement of people detection in the room borders and corners is presented for a 24 GHz digital beamforming (DBF) radar system. The feeding network is a parallel-fed power divider for microstrip patch antennas at 24 GHz. Both approaches are explained in theory. The design parameters and the layout of the antennas are given. The simulation of the antenna arrays are executed with CST MWS. Simulations and measurements of the proposed antennas are compared to each other. Both antennas are used for the transmit and the receive channel either. The sensor topology of the radar system is explained. Furthermore the measurement results of the protoype are presented and discussed.

A Robust Feature Extraction Model for Human Activity Characterization Using 3-Axis Accelerometer and Gyroscope Data.

Human Activity Recognition (HAR) using embedded sensors in smartphones and smartwatch has gained popularity in extensive applications in health care monitoring of elderly people, security purpose, robotics, monitoring employees in the industry, and others. However, human behavior analysis using the accelerometer and gyroscope data are typically grounded on supervised classification techniques, where models are showing sub-optimal performance for qualitative and quantitative features. Considering this factor, this paper proposes an efficient and reduce dimension feature extraction model for human activity recognition. In this feature extraction technique, the Enveloped Power Spectrum (EPS) is used for extracting impulse components of the signal using frequency domain analysis which is more robust and noise insensitive. The Linear Discriminant Analysis (LDA) is used as dimensionality reduction procedure to extract the minimum number of discriminant features from envelop spectrum for human activity recognition (HAR). The extracted features are used for human activity recognition using Multi-class Support Vector Machine (MCSVM). The proposed model was evaluated by using two benchmark datasets, i.e., the UCI-HAR and DU-MD datasets. This model is compared with other state-of-the-art methods and the model is outperformed.

Improving Human Activity Monitoring by Imputation of Missing Sensory Data: Experimental Study

The automatic recognition of human activities with sensors available in off-the-shelf mobile devices has been the subject of different research studies in recent years. It may be useful for the monitoring of elderly people to present warning situations, monitoring the activity of sports people, and other possibilities. However, the acquisition of the data from different sensors may fail for different reasons, and the human activities are recognized with better accuracy if the different datasets are fulfilled. This paper focused on two stages of a system for the recognition of human activities: data imputation and data classification. Regarding the data imputation, a methodology for extrapolating the missing samples of a dataset to better recognize the human activities was proposed. The K-Nearest Neighbors (KNN) imputation technique was used to extrapolate the missing samples in dataset captures. Regarding the data classification, the accuracy of the previously implemented method, i.e., Deep Neural Networks (DNN) with normalized and non-normalized data, was improved in relation to the previous results without data imputation.

SpeedNet: Indoor Speed Estimation With Radio Signals

Indoor human speed estimation is critical to in-home health monitoring of elderly people since it can provide the moving status of the human. Contactless indoor speed estimation with radio signals is challenging due to the complicated relationship between the speed of moving human and radio signals. In this article, we propose an indoor speed estimation framework, SpeedNet, to estimate the speed from the radio signals. Specifically, SpeedNet first extracts the dominant path signal reflected from the human through the beamforming technique. Then, SpeedNet obtains the doppler frequency shift (DFS) corresponding to the moving human by analyzing the short-time Fourier transform (STFT) spectrogram of the dominant path signal. Finally, SpeedNet trains a deep neural network composed of convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) to utilize the spatial and temporal features of DFS to estimate the speed of moving human. The experimental results show that SpeedNet can estimate the human moving speed with an average accuracy of 96.33% in a typical indoor environment, which is better than the state-of-the-art approaches.

IoT based Context-Aware Smart Home Caregivers System for Monitoring Elderly People

In pervasive computing, the area of contextawareness is considered as an important technology and also plays a important role in the area of healthcare environments. In this paper we propose a system for elderly people signs monitoring and critical health conditions which is sends to the caregivers to identify the daily activities by using internet of things (IoT) this can be done in real-time. For example the elderly people who are not well in daily activities like current health status is “normal” or “critical” and the caregivers can give the early suggestions to the people who are in nearby. We have build a fuzzy logic from the beginning of data acquisition, processing of the data namely filtering , aggregating it into contextual information and reasoning to identify the elderly people health condition. Variety of connected devices can modify the remote characteristic, real-time monitoring, measuring and transmitting numerous body health parameters for making the decision and medication. The goal of this paper is to introduce the approach Context Aware Smart Home Caregivers System (CASHCS) to identify the normal and abnormal conditions of the people health status so that doctor can cure the problems in early stage without any complication. So this can be used to build a framework for context-aware healthcare applications.

IoT based Context-Aware Smart Home Caregivers System for Monitoring Elderly People

In pervasive computing, the area of contextawareness is considered as an important technology and also plays a important role in the area of healthcare environments. In this paper we propose a system for elderly people signs monitoring and critical health conditions which is sends to the caregivers to identify the daily activities by using internet of things (IoT) this can be done in real-time. For example the elderly people who are not well in daily activities like current health status is “normal” or “critical” and the caregivers can give the early suggestions to the people who are in nearby. We have build a fuzzy logic from the beginning of data acquisition, processing of the data namely filtering , aggregating it into contextual information and reasoning to identify the elderly people health condition. Variety of connected devices can modify the remote characteristic, real-time monitoring, measuring and transmitting numerous body health parameters for making the decision and medication. The goal of this paper is to introduce the approach Context Aware Smart Home Caregivers System (CASHCS) to identify the normal and abnormal conditions of the people health status so that doctor can cure the problems in early stage without any complication. So this can be used to build a framework for context-aware healthcare applications.