Elderly Care Applications Research Articles

Motico: An attentional mechanism network model for smart aging disease risk prediction based on image data classification

The current disease risk prediction model with many parameters is complex to run smoothly on mobile terminals such as tablets and mobile phones in imaginative elderly care application scenarios. In order to further reduce the number of parameters in the model and enable the disease risk prediction model to run smoothly on mobile terminals, we designed a model called Motico (An Attention Mechanism Network Model for Image Data Classification). During the implementation of the Motico model, in order to protect image features, we designed an image data preprocessing method and an attention mechanism network model for image data classification. The Motico model parameter size is only 5.26 MB, and the memory only takes up 135.69 MB. In the experiment, the accuracy of disease risk prediction was 96 %, the precision rate was 97 %, the recall rate was 93 %, the specificity was 98 %, the F1 score was 95 %, and the AUC was 95 %. This experimental result shows that our Motico model can implement classification prediction based on the image data classification attention mechanism network on mobile terminals.

Effectiveness of the non-face-to-face comprehensive elderly care application "smart silver care" for community-dwelling elderly: A randomized controlled trial.

Quality of life for the elderly has become an important issue, and services aimed at improving it have typically been provided face-to-face. However, coronavirus disease 2019 has limited the use of face-to-face services, and the need to convert such systems to online interfaces has emerged. This study evaluates the effectiveness of a non-face-to-face comprehensive elderly care application called "Smart Silver Care." This study was designed as a randomized controlled trial. Sixty community-dwelling elderly individuals were randomly assigned to experimental and control groups in a 1:1 ratio. The participants participated in the "Smart Silver Care" intervention using a tablet and smartwatch based on the programs we provided. The participants performed five tasks, five days a week, consisting of physical, emotional, and cognitive programs. Participants could communicate with the researchers in real-time from their homes, and the researchers could remotely supervise their performance. We found positive effects on the relevant scales testing fall risk (Activities-Specific Balance Confidence [ABC] Scale, p = 0.028; Timed Up and Go [TUG] test, p = 0.001). However, there was no time × group interaction between the experimental and control groups on the relevant scales for depression and quality of life (Short Form-Geriatric Depression Scale [SGDS]-K: p = 0.225; EuroQol five-dimension five-level [EQ-5D-5L], p = 0.172). While the SGDS-K and EQ-5D-5L did not show statistical significance, we found improvement trends in the experimental group. The findings of this study show that Smart Silver Care significantly improved the participants' TUG and ABC scores in community-dwelling elderly, and a qualitative evaluation confirmed that it could be conveniently used by the elderly. Thus, Smart Silver Care offers a feasible intervention to improve the quality of life of the elderly, including physical aspects.

Device Attitude and Real-Time 3D Visualization: An Interface for Elderly Care

Objectivethis paper presents an innovative graphical user interface to visualize the attitude of a sensing device in a three-dimensional space, serving a wide-range of medical applications. Material and methodsbased on inertial measurement units (IMU) or on magnetic, angular rate and gravity (MARG) sensors, a processing unit provides Euler angles using a sensor fusion technique to display the orientation of the device relative to the Earth frame in real-time. The device is schematized by linking six polygonal regions, and is subject to sequential rotations by updating the graph each 350 ms. We conduct comparative studies between the two sensing devices, i.e. IMUs and MARGs, as well as two orientation filters, namely Madgwick's algorithm and Mahony's algorithm. Resultsthe accuracy of the system is reported as a function of (i) the sampling frequency, (ii) the sensing unit, and (iii) the orientation filter, following two elderly care applications, namely fall risk assessment and body posture monitoring. The experiments are conducted using public datasets. The corresponding results show that Madgwick's algorithm is best suited for low sampling rates, whereas MARG sensors are best suited for the detection of postural transitions. Conclusionthis paper addresses the different aspects and discusses the limitations of attitude estimation systems, which are important tools to help clinicians in their diagnosis.

Adaptation of a robotic dialog system for medication reminder in elderly care

Social robots can assist older adults in their daily life. Verbal conversation is a natural and convenient way for older adults to interact with social robots. However, most of the existing conversation-based robot services, such as medication reminders, are rule-based systems. These systems require many hand-crafted rules and a significant amount of expert knowledge, therefore they cannot adapt to older adults’ characteristics and dialog history. There are many reinforcement learning (RL) based methods for task-oriented dialogues, but they mainly focus on completing the tasks through text-based conversations. Those methods cannot be directly used for elderly care applications involving human–robot interactions (HRI). Considering the above shortcomings, we proposed a dialog system adaptation method (DSAM) for social robots. The DSAM is based on reinforcement learning which considers the characteristics of older adults, the dialog history and user preference to adapt the dialog policy and improve the dialog module. We implemented DSAM in our custom-made ASCCBot social robot. To evaluate DSAM, we firstly tested the dialog agent which was trained by a user simulator with different settings. The results show that the obtained agent achieves a good result with the desired dialog flow compared to the baseline agent. Based on the obtained dialog policy, the adaptation process is evaluated. The results show that with a good success rate, the number of dialog turns is decreased and the NLU module performance is improved by the adaptation process, which proves the effectiveness of DSAM. We also tested DSAM with human subjects. The results show that the average adaptation success rate is 94.7% and the preference distance reaches 0 after 6 rounds of adaptation while creating reminders successfully with a limited amount of user feedback. • A robotic dialog adaptation system for medication reminder in elderly care. • Reinforcement learning based methods for task-oriented dialogues. • Characteristics of older adults, dialog history and user preference considered in adaptation. • User simulator and user modeling combined to adapt the dialog agent.

Elderly and Patient Care Education; Social Support Program (SODES) Project of Turkish Republic Ministry of Development

The aim of the study is to evaluate the theoretical and practical training given to the unemployed women who are disadvantaged group for the health care of the elder people who are also disadvantaged group, within the scope of the project. Hundred participants registered in the Social Support Program (SODES) Project of Turkısh Republic Ministry of Development. Proje name is “My elderly and my patient are in safe hands with SODES” in cooperation with Social Assistance Foundation of Sahinbey Gaziantep Governorship's Office in 2013-2014. Surveys related to demographic features, elderly care applications and first aid were applied to participants before and after trainings. Wilcoxon, Mann Whitney U, Kruskal Wallis tests and Spearman Correlation Analysis were used. p<0,05 was accepted as statistical significance. The 100,0% of the elderly care providers were unemployed women, the mean age of participants was 33,29±10,66, 38,0% of them were graduated from primary school, and 8,0% of them had graduate degree. Besides, 57% of them were married, 60% of them had child/children, 44,0% of them had elderly relatives, and 19,0% of them had an eldercare experience. Mean knowledge scores after trainings (72,82±7,98) were higher compared to scores before trainings (35,30±17,08) (p<0,05). There was a positive significant relationship between the eldercare pre-test and post-test scores (r=0,790, p=0,001). Conclusively, it was detected that knowledge of all elderly care providers, who were included in the ‘“My elderly and my patient are in safe hands with SODES” project, increased in the end of elderly care and first aid trainings.

ElderSim: A Synthetic Data Generation Platform for Human Action Recognition in Eldercare Applications

To train deep learning models for vision-based action recognition of elders’ daily activities, we need large-scale activity datasets acquired under various daily living environments and conditions. However, most public datasets used in human action recognition either differ from or have limited coverage of elders’ activities in many aspects, making it challenging to recognize elders’ daily activities well by only utilizing existing datasets. Recently, such limitations of available datasets have actively been compensated by generating synthetic data from realistic simulation environments and using those data to train deep learning models. In this paper, based on these ideas we develop ElderSim, an action simulation platform that can generate synthetic data on elders’ daily activities. For 55 kinds of frequent daily activities of the elders, ElderSim generates realistic motions of synthetic characters with various adjustable data-generating options and provides different output modalities including RGB videos, two- and three-dimensional skeleton trajectories. We then generate KIST SynADL, a large-scale synthetic dataset of elders’ activities of daily living, from ElderSim and use the data in addition to real datasets to train three state-of-the-art human action recognition models. From the experiments following several newly proposed scenarios that assume different real and synthetic dataset configurations for training, we observe a noticeable performance improvement by augmenting our synthetic data. We also offer guidance with insights for the effective utilization of synthetic data to help recognize elders’ daily activities.

Optimal deployment of face recognition solutions in a heterogeneous IoT platform for secure elderly care applications

Face recognition provides a desirable solution for authentication and surveillance in Internet of Things platforms for elderly care. However, its inclusion is challenging because of the possibly reduced interaction capabilities of users, the high variety of interaction devices, and the need of managing biometric data securely. Our approach relies on lightweight deep neural networks for secure recognition and to guide users during interaction. An automated procedure selects the appropriate inference engine, model configurations, and batch size, based on edge device characteristics. Biometric data is homomorphically encrypted to preserve privacy. An evaluation with respect to state-of-the-art alternatives shows its potential.

Jointly optimization for activity recognition in secure IoT-enabled elderly care applications

Elderly care is a significant livelihood project in the increasingly serious aging society. Nowadays, the wider application of the Internet of Things (IoT) technology on assistant means has making an importance contribution to elderly care in institutions and at home. On the broader data foundation collected by IoT devices, Human Activity Recognition (HAR) with its high demand in various elderly care applications also has grabbed considerable research attentions. However, the elderly’s sensitive data transmitted over the wireless communication channel need to address many security concerns, and the accuracy of activity recognition is susceptible to many influencing factors, especially, the employed feature selection method and classifier. In this paper, to ensure the confidentiality of the elderly’s sensitive data, a secure and efficient group-based key establishment and authentication framework is first proposed. Subsequently, activity recognition is investigated within the security data sensitive framework, where a feature reorganization based feature selection method is proposed with the demonstrated relationship between the recognition accuracy and strongly correlative features, and three classifiers are investigated based on classical ones. By contrast, an improved convolutional neural network (CNN) enabled classifier is singled out to conduct the activity recognition with the feature reorganization method. Finally, security proofs show that the proposed security framework can ensure data confidentiality and be resilient to possible attacks, and experimental analyses show investigations in activity recognition can achieve more cost-effective results.

A Smart Wireless Paging Sensor Network for Elderly Care Application Using LoRaWAN

Long range wide area network (LoRaWAN) is increasingly gained much attention due to its advantages in wireless sensor networks (WSNs) application. The message delivery between participated nodes and gateway in the WSNs affects the quality of service (QoS) such as energy consumption and the successful delivery rate (SDR). In this paper, we present a novel packet transmission model for a smart wireless paging sensor network (WPSN) based on LoRaWAN, which supports a real-time elderly care application. The model is used to investigate the performance of star topology communication on elderly care via Markov discrete-time M/M/1 queuing system. The proposed traffic model enables the performance evaluation of WPSN for elderly care. Moreover, an optimal cluster allocation policy is proposed by which maximizing SDR are obtained and QoS constraint requirements are met in terms of energy consumption. We conduct analytic analysis and simulation to exploit the QoS of LoRa-based WSNs for elderly care application. The proposed WPSN is experimental studied and validated in a real elder care scenario. The contributions presented in this paper may be referenced in the design of LoRa-based WPSN.

Ambient Sensors for Elderly Care and Independent Living: A Survey.

Elderly care at home is a matter of great concern if the elderly live alone, since unforeseen circumstances might occur that affect their well-being. Technologies that assist the elderly in independent living are essential for enhancing care in a cost-effective and reliable manner. Elderly care applications often demand real-time observation of the environment and the resident’s activities using an event-driven system. As an emerging area of research and development, it is necessary to explore the approaches of the elderly care system in the literature to identify current practices for future research directions. Therefore, this work is aimed at a comprehensive survey of non-wearable (i.e., ambient) sensors for various elderly care systems. This research work is an effort to obtain insight into different types of ambient-sensor-based elderly monitoring technologies in the home. With the aim of adopting these technologies, research works, and their outcomes are reported. Publications have been included in this survey if they reported mostly ambient sensor-based monitoring technologies that detect elderly events (e.g., activities of daily living and falls) with the aim of facilitating independent living. Mostly, different types of non-contact sensor technologies were identified, such as motion, pressure, video, object contact, and sound sensors. Besides, multicomponent technologies (i.e., combinations of ambient sensors with wearable sensors) and smart technologies were identified. In addition to room-mounted ambient sensors, sensors in robot-based elderly care works are also reported. Research that is related to the use of elderly behavior monitoring technologies is widespread, but it is still in its infancy and consists mostly of limited-scale studies. Elderly behavior monitoring technology is a promising field, especially for long-term elderly care. However, monitoring technologies should be taken to the next level with more detailed studies that evaluate and demonstrate their potential to contribute to prolonging the independent living of elderly people.

A Multidimensional Time-Series Similarity Measure With Applications to Eldercare Monitoring.

In the last decade, data mining techniques have been applied to sensor data in a wide range of application domains, such as healthcare monitoring systems, manufacturing processes, intrusion detection, database management, and others. Many data mining techniques are based on computing the similarity between two sensor data patterns. A variety of representations and similarity measures for multiattribute time series have been proposed in the literature. In this paper, we describe a novel method for computing the similarity of two multiattribute time series based on a temporal version of Smith-Waterman (SW), a well-known bioinformatics algorithm. We then apply our method to sensor data from an eldercare application for early illness detection. Our method mitigates difficulties related to data uncertainty and aggregation that often arise when processing sensor data. The experiments take place at an aging-in-place facility, TigerPlace, located in Columbia, MO, USA. To validate our method, we used data from nonwearable sensor networks placed in TigerPlace apartments, combined with information from an electronic health record. We provide a set of experiments that investigate temporal version of SW properties, together with experiments on TigerPlace datasets. On a pilot sensor dataset from nine residents, with a total of 1902days and around 2.1 million sensor hits of collected data, we obtained an average abnormal events prediction F-measure of 0.75.

Doppler radar fall activity detection using the wavelet transform.

We propose in this paper the use of Wavelet transform (WT) to detect human falls using a ceiling mounted Doppler range control radar. The radar senses any motions from falls as well as nonfalls due to the Doppler effect. The WT is very effective in distinguishing the falls from other activities, making it a promising technique for radar fall detection in nonobtrusive inhome elder care applications. The proposed radar fall detector consists of two stages. The prescreen stage uses the coefficients of wavelet decomposition at a given scale to identify the time locations in which fall activities may have occurred. The classification stage extracts the time-frequency content from the wavelet coefficients at many scales to form a feature vector for fall versus nonfall classification. The selection of different wavelet functions is examined to achieve better performance. Experimental results using the data from the laboratory and real inhome environments validate the promising and robust performance of the proposed detector.

TV-Based Caring Videophone System for the Elderly in the Smart Home Environment

A novel videophone system for the elderly-care application is proposed. Based on the detailed analysis of the elderly’s physical and psychological characteristics, a TV-based caring videophone system for the elderly is developed: an embedded multimedia device is designed to implement the interactive video and audio processing and IP-based communication, in which TV is adopted as the display terminal to achieve a low-cost but high-quality service. Considering the user’s convenience, many personalized designs, such as photo-based address book, photo-click-dialing, and touch pad based remote controller, are developed to make the proposed videophone system more intuitive and easy to use for the elderly. Based on Support Vector Machine (SVM) algorithms, an evaluation model is also developed with the data collected from the embedded multimedia device. It is useful to evaluate the physical and psychological health of the elderly.

Hierarchical and incremental event learning approach based on concept formation models

We propose an event learning approach for video, based on concept formation models. This approach incrementally learns on-line a hierarchy of states and event by aggregating the attribute values of tracked objects in the scene. The model can aggregate both numerical and symbolic values.The utilisation of symbolic attributes gives high flexibility to the approach. The approach also proposes the integration of attributes as a doublet value-reliability, for considering the effect in the event learning process of the uncertainty inherited from previous phases of the video analysis process.Simultaneously, the approach recognises the states and events of the tracked objects, giving a multi-level description of the object situation.The approach has been evaluated for an elderly care application and a rat behaviour analysis application. The results show that the approach is capable of learning and recognising meaningful events occurring in the scene, and to build a rich model of the objects behaviour. The results also show that the approach can give a description of the activities of a person (e.g. approaching to a table, crouching), and to detect abnormal events based on the frequency of occurrence.

Elder Care Based on Cognitive Sensor Network

The advancement of semiconductor fabrication technology and smart algorithms has allowed us to design and develop intelligent sensors with cognitive ability. Statistics reveal that in recent times the number of elderly people is increasing rapidly around the world and the situation is not going to ease in the foreseeable future. In this paper, we report a novel home monitoring system which is based on cognitive sensor network and is intended for elder-care application. The smart system consists of an optimum number of cognitive wireless sensors which are used to detect usage of electrical devices, bed usage pattern, flow of water, etc., and also incorporates a panic button. The cognitive sensors provide information that can be used for monitoring the elderly by detecting any abnormality pattern in their daily activities around the house. The system will generate and send an early warning message to the care giver, when an unforeseen abnormal condition occurs.

Model and algorithmic framework for detection and correction of cognitive errors

This paper outlines an approach that we are taking for elder-care applications in the smart home, involving cognitive errors and their compensation. Our approach involves high level modeling of daily activities of the elderly by breaking down these activities into smaller units, which can then be automatically recognized at a low level by collections of sensors placed in the homes of the elderly. This separation allows us to employ plan recognition algorithms and systems at a high level, while developing stand-alone activity recognition algorithms and systems at a low level. It also allows the mixing and matching of multi-modality sensors of various kinds that go to support the same high level requirement. Currently our plan recognition algorithms are still at a conceptual stage, whereas a number of low level activity recognition algorithms and systems have been developed. Herein we present our model for plan recognition, providing a brief survey of the background literature. We also present some concrete results that we have achieved for activity recognition, emphasizing how these results are incorporated into the overall plan recognition system.

Older adults' privacy considerations for vision based recognition methods of eldercare applications

This study aims to explore older adults' privacy considerations for technology based monitoring applications in eldercare that use video systems. It specifically aims to introduce alternative vision based tools and identify whether distorting or "anonymizing" captured images affect older adults' privacy concerns and willingness to allow such an application to be installed in their residence. Ten residents of an independent retirement community were recruited to participate in a series of scenarios. Each scenario involved a daily activity such as sitting in the living room and having a visitor, or preparing a snack. These sessions were video-recorded using different image processing and extraction approaches. Follow-up in-depth interviews with participants were conducted after a demonstration of the captured images. Findings indicate that shape extraction can alleviate privacy concerns associated with the use of cameras. Participants expressed no privacy concerns with silhouette images and emphasized the importance of anonymity in the video sequences. They furthermore expressed the desire to control system operation by being able to turn a vision-based system off and on, and also determine who has access to the collected information.

Wireless Sensors for Home Monitoring - A Review

A review of wireless sensors and sensor networks, especially for in-home monitoring of elderly people, has been presented in this paper. The characteristics of various sensors for monitoring applications have been studied. The requirements of the sensor for making a smart sensor network have been investigated. A typical in-house developed system for home monitoring and elder-care application has been presented. A few patents on the sensors for home monitoring have been reviewed. Keywords: current sensing, non-intrusive monitoring system, radio frequency communication, Wireless sensor network, hardware interfacing, elderly care

Mobile manipulators for assisted living in residential settings

We describe a methodology for creating new technologies for assisted living in residential environments. The number of eldercare clients is expected to grow dramatically over the next decade as the baby boom generation approaches 65 years of age. The UMass/Smith ASSIST framework aims to alleviate the strain on centralized medical providers and community services as their clientele grow, reduce the delays in service, support independent living, and therefore, improve the quality of life for the up-coming elder population. We propose a closed loop methodology wherein innovative technical systems are field tested in assisted care facilities and analyzed by social scientists to create and refine residential systems for independent living. Our goal is to create technology that is embraced by clients, supports efficient delivery of support services, and facilitates social interactions with family and friends. We introduce a series of technologies that are currently under evaluation based on a distributed sensor network and a unique mobile manipulator (MM) concept. The mobile manipulator provides client services and serves as an embodied interface for remote service providers. As a result, a wide range of cost-effective eldercare applications can be devised, several of which are introduced in this paper. We illustrate tools for social interfaces, interfaces for community service and medical providers, and the capacity for autonomous assistance in the activities of daily living. These projects and others are being considered for field testing in the next cycle of ASSIST technology development.