205 research outputs found

    Review of technology‐supported multimodal solutions for people with dementia

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    Funding Information: This research was partially funded by FAITH project (H2020?SC1?DTH?2019?875358), CARELINK project (AAL?CALL?2016?049), and Funda??o para a Ci?ncia e Tecnologia through the program UIDB/00066/2020 (CTS?Center of Technology and Systems).Acknowledgments: The authors acknowledge the European Commission for its support and partial funding; the partners of the research project FAITH project (H2020?SC1?DTH?2019?875358); and CARELINK, AAL?CALL?2016?049 funded by AAL JP and co?funded by the European Commission and National Funding Authorities of Ireland, Belgium, Portugal, and Switzerland. Partial support also comes from Funda??o para a Ci?ncia e Tecnologia through the program UIDB/00066/2020 (CTS?Center of Technology and Systems). Funding Information: Acknowledgments: The authors acknowledge the European Commission for its support and partial funding; the partners of the research project FAITH project (H2020‐SC1‐DTH‐2019‐875358); and CARELINK, AAL‐CALL‐2016‐049 funded by AAL JP and co‐funded by the European Commission and National Funding Authorities of Ireland, Belgium, Portugal, and Switzerland. Partial support also comes from Fundação para a Ciência e Tecnologia through the program UIDB/00066/2020 (CTS—Center of Technology and Systems). Funding Information: Funding: This research was partially funded by FAITH project (H2020‐SC1‐DTH‐2019‐875358), CARELINK project (AAL‐CALL‐2016‐049), and Fundação para a Ciência e Tecnologia through the program UIDB/00066/2020 (CTS—Center of Technology and Systems). Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.The number of people living with dementia in the world is rising at an unprecedented rate, and no country will be spared. Furthermore, neither decisive treatment nor effective medicines have yet become effective. One potential alternative to this emerging challenge is utilizing supportive technologies and services that not only assist people with dementia to do their daily activities safely and independently, but also reduce the overwhelming pressure on their caregivers. Thus, for this study, a systematic literature review is conducted in an attempt to gain an overview of the latest findings in this field of study and to address some commercially available supportive technologies and services that have potential application for people living with dementia. To this end, 30 potential supportive technologies and 15 active supportive services are identified from the literature and related websites. The technologies and services are classified into different classes and subclasses (according to their functionalities, capabilities, and features) aiming to facilitate their understanding and evaluation. The results of this work are aimed as a base for designing, integrating, developing, adapting, and customizing potential multimodal solutions for the specific needs of vulnerable people of our societies, such as those who suffer from different degrees of dementia.publishersversionpublishe

    Wearables measuring electrodermal activity to assess perceived stress in care:A scoping review

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    Background:Chronic stress responses can lead to physical and behavioural health problems, often experienced and observed in the care of people with intellectual disabilities or people with dementia. Electrodermal activity (EDA) is a bio-signal for stress, which can be measured by wearables and thereby support stress management. However, the how, when and to what extent patients and healthcare providers can benefit is unclear. This study aims to create an overview of available wearables enabling the detection of perceived stress by using EDA.Methods:Following the PRISMA-SCR protocol for scoping reviews, four databases were included in the search of peer-reviewed studies published between 2012 and 2022, reporting detection of EDA in relation to self-reported stress or stress-related behaviours. Type of wearable, bodily location, research population, context, stressor type and the reported relationship between EDA and perceived stress were extracted.Results:Of the 74 included studies, the majority included healthy subjects in laboratory situations. Field studies and studies using machine learning (ML) to predict stress have increased in the last years. EDA is most often measured on the wrist, with offline data processing. Studies predicting perceived stress or stress-related behaviour using EDA features, reported accuracies between 42% and 100% with an average of 82.6%. Of these studies, the majority used ML.Conclusion:Wearable EDA sensors are promising in detecting perceived stress. Field studies with relevant populations in a health or care context are lacking. Future studies should focus on the application of EDA-measuring wearables in real-life situations to support stress management

    Sensing Technology to Monitor Behavioral and Psychological Symptoms and to Assess Treatment Response in People With Dementia. A Systematic Review

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    Background: The prevalence of dementia is expected to rapidly increase in the next decades, warranting innovative solutions improving diagnostics, monitoring and resource utilization to facilitate smart housing and living in the nursing home. This systematic review presents a synthesis of research on sensing technology to assess behavioral and psychological symptoms and to monitor treatment response in people with dementia. Methods: The literature search included medical peer-reviewed English language publications indexed in Embase, Medline, Cochrane library and Web of Sciences, published up to the 5th of April 2019. Keywords included MESH terms and phrases synonymous with “dementia”, “sensor”, “patient”, “monitoring”, “behavior”, and “therapy”. Studies applying both cross sectional and prospective designs, either as randomized controlled trials, cohort studies, and case-control studies were included. The study was registered in PROSPERO 3rd of May 2019. Results: A total of 1,337 potential publications were identified in the search, of which 34 were included in this review after the systematic exclusion process. Studies were classified according to the type of technology used, as (1) wearable sensors, (2) non-wearable motion sensor technologies, and (3) assistive technologies/smart home technologies. Half of the studies investigated how temporarily dense data on motion can be utilized as a proxy for behavior, indicating high validity of using motion data to monitor behavior such as sleep disturbances, agitation and wandering. Further, up to half of the studies represented proof of concept, acceptability and/or feasibility testing. Overall, the technology was regarded as non-intrusive and well accepted. Conclusions: Targeted clinical application of specific technologies is poised to revolutionize precision care in dementia as these technologies may be used both by patients and caregivers, and at a systems level to provide safe and effective care. To highlight awareness of legal regulations, data risk assessment, and patient and public involvement, we propose a necessary framework for sustainable ethical innovation in healthcare technology. The success of this field will depend on interdisciplinary cooperation and the advance in sustainable ethic innovation.publishedVersio

    Predicting Subjective Sleep Quality Using Objective Measurements in Older Adults

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    Humans spend almost a third of their lives asleep. Sleep has a pivotal effect on job performance, memory, fatigue recovery, and both mental and physical health. Sleep quality (SQ) is a subjective experience and reported via patients’ self-reports. Predicting subjective SQ based on objective measurements can enhance diagnosis and treatment of SQ defects, especially in older adults who are subject to poor SQ. In this dissertation, we assessed enhancement of subjective SQ prediction using an easy-to-use E4 wearable device, machine learning techniques and identifying disease-specific risk factors of abnormal SQ in older adults. First, we designed a clinical decision support system to estimate SQ and feeling refreshed after sleep using data extracted from an E4 wearable device. Specifically, we processed four raw physiological signals of heart rate variability (HRV), electrodermal activity, body movement, and skin temperature using distinct signal processing methodologies. Following this, we extracted signal-specific features and selected a subset of the features using recursive feature elimination cross validation strategy to maximize the accuracy of SQ classifiers in predicting the SQ of older caregivers. Second, we investigated discovering more effective features in SQ prediction using HRV features which are not only effortlessly measurable but also can reflect sleep stage transitions and some sleep disorders. Evaluation of two interpretable machine learning methodologies and a convolutional neural network (CNN) methodology demonstrated the CNN outperforms by an accuracy of 0.6 in predicting light, medium, and deep SQ. This outcome verified the capability of using HRV features measurable by easy-to-use wearable devices, in predicting SQ. Finally, we scrutinized daytime sleepiness risk factors as a sign of abnormal SQ from four perspectives: sleep fragmented, sleep propensity, sleep resilience, and non-restorative sleep. The analysis demonstrates distinguishability of the main risk factors of excessive daytime sleepiness (EDS) between patients suffering from fragmented sleep (e.g. apnea) and sleep propensity. We identified the average area under oxygen desaturation curve corresponds to apnea/hypopnea event as a disease-specific risk factor of abnormal SQ. Our further daytime sleepiness prediction demonstrated the significant role of the founded disease-specific risk factor as well

    AEVUM: Personalized Health Monitoring System

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    Advancement in the field of sensors and other portable technologies have resulted in a bevy of health monitoring devices such as blue-tooth and Wi-Fi enabled weighing scales and wearables which help individuals monitor their personal health. This collected information provides a plethora of data points over intervals of time that a primary care physician can utilize to gain a holistic understanding of an individual’s health and provide a more effective and personalized treatment. A drawback of the existing health monitoring devices is that they are not integrated with the professional medical infrastructure. With the wealth of information collected, it is also not feasible for a physician to look through all the data to obtain relevant information or patterns from multiple health monitoring systems. Therefore, it would be beneficial to have a single platform of hardware devices to monitor and collect data and a software application to securely store the collected information, identify patterns for analysis, and summarize the data for the physician and the patient. The aim of this study was to design and develop an unobtrusive, user friendly system, Aevum, which would integrate technology, adapt itself to changes in consumer behavior and integrate with the existing healthcare infrastructure to help an individual monitor their health in a customized manner. Aevum is a multi-device system consisting of a smart, puck-shaped hardware product, a wristband and a software application available to the patient as well as the physician. In addition to monitoring vitals such as heart rate, blood pressure, body temperature and weight, Aevum can monitor environmental factors that affect an individual’s health and uses personalized metrics such as precise calorie intake and medication management to monitor health. This allows the user to personalize Aevum based on their health condition. Finally, Aevum identifies patterns of anomalies in the collected data and compiles the information which can be accessed by the physician to assist in their treatment

    Exploring the Landscape of Ubiquitous In-home Health Monitoring: A Comprehensive Survey

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    Ubiquitous in-home health monitoring systems have become popular in recent years due to the rise of digital health technologies and the growing demand for remote health monitoring. These systems enable individuals to increase their independence by allowing them to monitor their health from the home and by allowing more control over their well-being. In this study, we perform a comprehensive survey on this topic by reviewing a large number of literature in the area. We investigate these systems from various aspects, namely sensing technologies, communication technologies, intelligent and computing systems, and application areas. Specifically, we provide an overview of in-home health monitoring systems and identify their main components. We then present each component and discuss its role within in-home health monitoring systems. In addition, we provide an overview of the practical use of ubiquitous technologies in the home for health monitoring. Finally, we identify the main challenges and limitations based on the existing literature and provide eight recommendations for potential future research directions toward the development of in-home health monitoring systems. We conclude that despite extensive research on various components needed for the development of effective in-home health monitoring systems, the development of effective in-home health monitoring systems still requires further investigation.Comment: 35 pages, 5 figure

    高齢者の健康管理におけるウェアラブルデバイスと中国伝統医学の役割

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    早大学位記番号:新8258早稲田大

    Novel Machine Learning and Wearable Sensor Based Solutions for Smart Healthcare Monitoring

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    The advent of IoT has enabled the design of connected and integrated smart health monitoring systems. These health monitoring systems can be utilized for monitoring the mental and physical wellbeing of a person. Stress, anxiety, and hypertension are the major elements responsible for the plethora of physical and mental illnesses. In this context, the older population demands special attention because of the several age-related complications that exacerbate the effects of stress, anxiety, and hypertension. Monitoring stress, anxiety, and blood pressure regularly can prevent long-term damage by initiating necessary intervention or clinical treatment beforehand. This will improve the quality of life and reduce the burden on caregivers and the cost of healthcare. Therefore, this thesis explores novel technological solutions for real-time monitoring of stress, anxiety, and blood pressure using unobtrusive wearable sensors and machine learning techniques. The first contribution of this thesis is the experimental data collection of 50 healthy older adults, based on which, the works on stress detection and anxiety detection have been developed. The data collection procedure lasted for more than a year. We have collected physiological signals, salivary cortisol, and self-reported questionnaire feedback during the study. Salivary cortisol is an established clinical biomarker for physiological stress. Hence, a stress detection model that is trained to distinguish between the stressed and not-stressed states as indicated by the increase in cortisol level has the potential to facilitate clinical level diagnosis of stress from the comfort of their own home. The second contribution of the thesis is the development of a stress detection model based on fingertip sensors. We have extracted features from Electrodermal Activity (EDA) and Blood Volume Pulse (BVP) signals obtained from fingertip EDA and Photoplethysmogram (PPG) sensors to train machine learning algorithms for distinguishing between stressed and not-stressed states. We have evaluated the performance of four traditional machine learning algorithms and one deep-learning-based Long Short-Term Memory (LSTM) classifier. Results and analysis showed that the proposed LSTM classifier performed equally well as the traditional machine learning models. The third contribution of the thesis is to evaluate an integrated system of wrist-worn sensors for stress detection. We have evaluated four signal streams, EDA, BVP, Inter-Beat Interval (IBI), and Skin Temperature (ST) signals from EDA, PPG, and ST sensors. A random forest classifier was used for distinguishing between the stressed and not-stressed states. Results and analysis showed that incorporating features from different signals was able to reduce the misclassification rate of the classifier. Further, we have also prototyped the integration of the proposed wristband-based stress detection system in a consumer end device with voice capabilities. The fourth contribution of the thesis is the design of an anxiety detection model that uses features from a single wearable sensor and a context feature to improve the performance of the classification model. Using a context feature instead of integrating other physiological features for improving the performance of the model can reduce the complexity and cost of the anxiety detection model. In our proposed work, we have used a simple experimental context feature to highlight the importance of context in the accurate detection of anxious states. Our results and analysis have shown that with the addition of the context-based feature, the classifier was able to reduce misclassification by increasing the confidence of the decision. The final and the fifth contribution of the thesis is the validation of a proposed computational framework for the blood pressure estimation model. The proposed framework uses features from the PPG signal to estimate the systolic and diastolic blood pressure values using advanced regression techniques
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