IoT in medical diagnosis: detecting excretory functional disorders for Older adults via bathroom activity change using unobtrusive IoT technology

The Internet of Things (IoT) and Artificial Intelligence (AI) are promising technologies that can help make the health system more efficient, which concurrently can be particularly useful to help maintain a high quality of life for older adults, especially in light of healthcare staff shortage. Many health issues are challenging to manage both by healthcare staff and policymakers. They have a negative impact on older adults and their families and are an economic burden to societies around the world. This situation is particularly critical for older adults, a population highly vulnerable to diseases that needs more consideration and care. It is, therefore, crucial to improve diagnostic and management as well as proposed prevention strategies to enhance the health and quality of life of older adults. In this study, we focus on detecting symptoms in early stages of diseases to prevent the deterioration of older adults' health and avoid complications. We focus on digestive and urinary system disorders [mainly the Urinary Tract Infection (UTI) and the Irritable Bowel Syndrome (IBS)] that are known to affect older adult populations and that are detrimental to their health and quality of life. Our proposed approach relies on unobtrusive IoT and change point detections algorithms to help follow older adults' health status daily. The approach monitors long-term behavior changes and detects possible changes in older adults' behavior suggesting early onsets or symptoms of IBS and UTI. We validated our approach with medical staff reports and IoT data collected in the residence of 16 different older adults during periods ranging from several months to a few years. Results are showing that our proposed approach can detect changes associated to symptoms of UTI and IBS, which were confirmed with observations and testimonies from the medical staff.

GATEKEEPER's Strategy for the Multinational Large-Scale Piloting of an eHealth Platform: Tutorial on How to Identify Relevant Settings and Use Cases

BACKGROUND

The World Health Organization's strategy toward healthy aging fosters person-centered integrated care sustained by eHealth systems. However, there is a need for standardized frameworks or platforms accommodating and interconnecting multiple of these systems while ensuring secure, relevant, fair, trust-based data sharing and use. The H2020 project GATEKEEPER aims to implement and test an open-source, European, standard-based, interoperable, and secure framework serving broad populations of aging citizens with heterogeneous health needs.

OBJECTIVE

We aim to describe the rationale for the selection of an optimal group of settings for the multinational large-scale piloting of the GATEKEEPER platform.

METHODS

The selection of implementation sites and reference use cases (RUCs) was based on the adoption of a double stratification pyramid reflecting the overall health of target populations and the intensity of proposed interventions; the identification of a principles guiding implementation site selection; and the elaboration of guidelines for RUC selection, ensuring clinical relevance and scientific excellence while covering the whole spectrum of citizen complexities and intervention intensities.

RESULTS

Seven European countries were selected, covering Europe's geographical and socioeconomic heterogeneity: Cyprus, Germany, Greece, Italy, Poland, Spain, and the United Kingdom. These were complemented by the following 3 Asian pilots: Hong Kong, Singapore, and Taiwan. Implementation sites consisted of local ecosystems, including health care organizations and partners from industry, civil society, academia, and government, prioritizing the highly rated European Innovation Partnership on Active and Healthy Aging reference sites. RUCs covered the whole spectrum of chronic diseases, citizen complexities, and intervention intensities while privileging clinical relevance and scientific rigor. These included lifestyle-related early detection and interventions, using artificial intelligence-based digital coaches to promote healthy lifestyle and delay the onset or worsening of chronic diseases in healthy citizens; chronic obstructive pulmonary disease and heart failure decompensations management, proposing integrated care management based on advanced wearable monitoring and machine learning (ML) to predict decompensations; management of glycemic status in diabetes mellitus, based on beat to beat monitoring and short-term ML-based prediction of glycemic dynamics; treatment decision support systems for Parkinson disease, continuously monitoring motor and nonmotor complications to trigger enhanced treatment strategies; primary and secondary stroke prevention, using a coaching app and educational simulations with virtual and augmented reality; management of multimorbid older patients or patients with cancer, exploring novel chronic care models based on digital coaching, and advanced monitoring and ML; high blood pressure management, with ML-based predictions based on different intensities of monitoring through self-managed apps; and COVID-19 management, with integrated management tools limiting physical contact among actors.

CONCLUSIONS

This paper provides a methodology for selecting adequate settings for the large-scale piloting of eHealth frameworks and exemplifies with the decisions taken in GATEKEEPER the current views of the WHO and European Commission while moving forward toward a European Data Space.

COVID-19-associated spontaneous subacute subdural haematoma: report of two cases

Since March 2020, the pandemic of coronavirus disease 2019 (COVID-19) has become a threat to global health. Several kinds of coronavirus-associated disorders, including vascular involvements with neurological symptoms, have been reported worldwide. Here, we describe two individuals with COVID-19 with no history of traumatic brain injury nor of vascular injuries, who developed spontaneous subdural haematoma in a subacute process. Both individuals became lethargic and unresponsive during admission in the intensive care unit. Both have undergone emergent craniotomy with acceptable outcome. The first patient improved significantly and was discharged a week after surgery. However, the second individual had no improvement on her consciousness and died 3 days after surgery. Haemorrhagic events, including subdural haematoma, can happen during COVID-19 infection with several possible mechanisms. Brain imaging and further neurological evaluation must be performed in any individuals with COVID-19 who show signs of alteration in their state of consciousness.

Microelimination or Not? The Changing Epidemiology of Human Immunodeficiency Virus-Hepatitis C Virus Coinfection in France 2012–2018

Background

The arrival of highly effective, well-tolerated, direct-acting antiviral agents (DAA) led to a dramatic decrease in hepatitis C virus (HCV) prevalence. Human immunodeficiency virus (HIV)-HCV–coinfected patients are deemed a priority population for HCV elimination, while a rise in recently acquired HCV infections in men who have sex with men (MSM) has been described. We describe the variations in HIV-HCV epidemiology in the French Dat’AIDS cohort.

Methods

This was a retrospective analysis of a prospective cohort of persons living with HIV (PLWH) from 2012 to 2018. We determined HCV prevalence, HCV incidence, proportion of viremic patients, treatment uptake, and mortality rate in the full cohort and by HIV risk factors.

Results

From 2012 to 2018, 50 861 PLWH with a known HCV status were followed up. During the period, HCV prevalence decreased from 15.4% to 13.5%. HCV prevalence among new HIV cases increased from 1.9% to 3.5% in MSM but remained stable in other groups. Recently acquired HCV incidence increased from 0.36/100 person-years to 1.25/100 person-years in MSM. The proportion of viremic patients decreased from 67.0% to 8.9%. MSM became the first group of viremic patients in 2018 (37.9%). Recently acquired hepatitis represented 59.2% of viremic MSM in 2018. DAA treatment uptake increased from 11.4% to 61.5%. More treatments were initiated in MSM in 2018 (41.2%) than in intravenous drug users (35.6%). In MSM, treatment at the acute phase represented 30.0% of treatments in 2018.

Conclusions

A major shift in HCV epidemiology was observed in PLWH in France from 2012 to 2018, leading to a unique situation in which the major group of HCV transmission in 2018 was MSM.

Clinical Trials Registration. NCT02898987.

The apoptotic effects of progesterone on breast cancer (MCF-7) and human osteosarcoma (MG-636) cells

Purpose

Progesterone has been reported to inhibit the proliferation of breast cancer and osteosarcoma cells; however, its inhibitory mechanism has not yet been clarified. The aim of the present study was to clarify the effects of progesterone on apoptosis in breast cancer (MCF-7) and human osteosarcoma (MG-63) cells.

Materials and methods

In this experimental study the cytotoxic effect of progesterone was measured in MCF-7 and MG-63 cells exposed to different concentrations of progesterone using MTT assay, and effective concentrations were identified. The expression levels of the Bax, P53 and Bcl-2 genes were evaluated by real-time PCR, and caspase-3, 8 and 9 activity levels were determined using a colorimetric method. Hoechst staining and flow cytometry were used to confirm apoptosis. The data were statistically analyzed using one-way analysis of variance (ANOVA) and independent-samples t-test.

Results

Compared to the control group, we observed a significant increase in the expression levels of the Bax and P53 genes and the activity levels of caspase-3 and 9, and a significant decrease in the expression level of the Bcl-2 gene in MCF-7 and MG-63 treated with effective concentration of progesterone. The caspase-8 activity level did not change significantly in treated MG-63 but increased in treated MCF-7 cells. Hoechst staining and flow cytometry results confirmed apoptosis in the cells exposed to effective concentration of progesterone.

Conclusions

The cytotoxic effect of progesterone on breast cancer and osteosarcoma cells was mediated by apoptotic pathways. In this context, progesterone triggers the extrinsic and intrinsic apoptotic pathways in MCF-7 cells and induces the intrinsic apoptotic pathway in MG-63 cells.

Study of Middleware for Internet of Healthcare Things and Their Applications

The rapid proliferation and miniaturization of the wireless and embedded devices has led to the invasion of the Internet of Things in many domains and has reached the healthcare sector to form what is called the Internet of Healthcare Things (IoHT) . The growing number of the applications in the Internet of Healthcare Things as well as the overwhelming number of heterogeneous medical devices that should interact in this network has put the researchers and developers in front of lot of challenges: How to facilitate the implementation of the various healthcare applications? And how to ease the integration of new devices and make their interoperation a transparent task for the developers? To fulfill these requirements, lot of middleware have been proposed. In this paper we provide a complete study on the existing middleware for IoHT and specify their applications, we propose a taxonomy for them and we present their main advantages and drawbacks.

Pilot Site Deployment of an IoT Solution for Older Adults' Early Behavior Change Detection

The world demography is continuously changing. During the last decade, we noticed a regular variation in the world demography leading to a nearly balanced society share between the young and aging population. This increasing older adult population is facing many problems. In fact, the transition to the aging period is associated with physical, psychological, cognitive, and societal changes. Negative behavior changes are considered as indicators of older adults’ frailty. This is why it is important to detect such behavior changes early in order to prevent isolation, sedentary lifestyle, and even diseases, and therefore delay the frailty period. This paper exhibits a proof-of-concept pilot site deployment of an Internet of Thing (IoT) solution for the continuous monitoring and detection of older adults’ behavior changes. The objective is to help geriatricians detect sedentary lifestyle and health-related problems at an early stage.

Machine Learning Classification Models with SPD/ED Dataset: Comparative Study of Abstract Versus Full Article Approach

In response to the researchers need in the bio-medical domain, we opted for automating the bibliographic research stage. In this context, several classification models of supervised machine learning are used. Namely the SVM, Random Forest, Decision Tree, KNN, and Gradient Boosting. In this paper, we conduct a comparative study between experimental results of full article classification and abstract classification approaches. Furthermore, we evaluate our results by using evaluation metrics such as accuracy, precision, recall and F1-score. We observe that the abstract approach outperforms the full article approach in terms of learning time and efficiency.

Ensuring the Correctness and Well Modeling of Intelligent Healthcare Management Systems

Recent research focus more and more on IoT systems and their applications in order to make people life easier and controllable. The main aim is to expand IoT applications and services into various domains while ensuring communication and automated exchange between them. Recent research handles many issues related to IoT especially implementation, modeling, and deployment. However, many challenges need more deep and thorough analysis especially in terms of flexible modeling, extensible implementation, with respect to the privacy issue. This work focuses principally on modeling IoT systems dedicated to smart healthcare case. We attempt to address the emergency service by initiating a modeling mechanism for Healthcare Management System (HMS) by using UML diagrams, and propose an appropriate access control in order to reinforce it. Then, we ensure the correctness of the developed HMS by relying on the verification and validation based on a formal analysis that showed significant results by using Alloy tool.

A Novel On-Wrist Fall Detection System Using Supervised Dictionary Learning Technique

Wrist-based fall detection system provides a very comfortable and multi-modal healthcare solution, especially for elderly risking falls. However, the wrist location presents a very challenging and unstable spot to distinguish falls among other daily activities. In this paper, we propose a Supervised Dictionary Learning approach for wrist-based fall detection. Three Dictionary learning algorithms for classification are invoked in this study, namely SRC, FDDL, and LRSDL. To extract the best descriptive representation of the signal data we followed different preprocessing scenarios based on accelerometer, gyroscope, and magnetometer. A considerable overall performance was obtained by the SRC algorithms reaching respectively 99.8%, 100%, and 96.6% of accuracy, sensitivity, and specificity using raw data provided by a triaxial accelerometer, accordingly overthrowing previously proposed methods for wrist placement.

Context-Aware Healthcare Adaptation Model for COPD Diseases

Nowadays, ubiquitous computing and mobile applications are controlling all our life’s aspects, from social media and entertainment to the very basic needs like commerce, learning, government, and health. These systems have the ability to self-adapt to meet changes in their execution environment and the user’s context. In the healthcare domain, information systems have proven their efficiency, not only by organizing and managing patients’ data and information but also by helping doctors and medical experts in diagnosing disease and taking precluding procedure to avoid serious conditions. In chronic diseases, telemonitoring systems provide a way to monitor the patient’s state and biomarkers within their usual life’s routine. In this article, we are combining the healthcare telemonitoring systems with the context awareness and self-adaptation paradigm to provide a self-adaptive framework architecture for COPD patients.

Multirate ECG Processing and k-Nearest Neighbor Classifier Based Efficient Arrhythmia Diagnosis

The goal of this work is to make a contribution to the development of computationally efficient multirate Electrocardiogram (ECG) automated detectors of arrhythmia. It utilizes an intelligent combination of multirate denoising plus wavelet decomposition for an effective realization of the ECG wireless implants. The decomposed signal subband features are mined and in next step these are utilized by the mature k-Nearest Neighbor (KNN) classifier for arrhythmia diagnosis. The multirate nature substantially reduces the processing activity of the system and thus allows a dramatic decrease in energy consumption compared to traditional counterparts. The performance of the system is estimated also in terms of the classification performance. Obtained results reveal an overall 22.5-fold compression gain and 4-folds processing outperformance over the traditional equals while securing 93.2% highest classification accuracy and specificity of 0.956. Findings confirm that the proposed solution could potentially be embedded in contemporary automatic and mobile cardiac diseases diagnosis systems.

EEG-Based Hypo-vigilance Detection Using Convolutional Neural Network

Hypo-vigilance detection is becoming an important active research areas in the biomedical signal processing field. For this purpose, electroencephalogram (EEG) is one of the most common modalities in drowsiness and awakeness detection. In this context, we propose a new EEG classification method for detecting fatigue state. Our method makes use of a and awakeness detection. In this context, we propose a new EEG classification method for detecting fatigue state. Our method makes use of a Convolutional Neural Network (CNN) architecture. We define an experimental protocol using the Emotiv EPOC+ headset. After that, we evaluate our proposed method on a recorded and annotated dataset. The reported results demonstrate high detection accuracy (93%) and indicate that the proposed method is an efficient alternative for hypo-vigilance detection as compared with other methods.

Comparative Study of Relevant Methods for MRI/X Brain Image Registration

Several methods of brain image registration have been proposed in order to overcome the requirement of clinicians. In this paper, we assess the performance of a hybrid method for brain image registration against the most used standard registration tools. Most traditional registration tools use different methods for mono- and multi-modal registration, whereas the hybrid registration method is providing both mono and multi-modal brain registration of PET, MRI and CT images. To determine the appropriate registration method, we used two challenging brain image datasets as well as two evaluation metrics. Results show that the hybrid method outperforms all other standard registration tools and has achieved promising accuracy for MRI/X brain image registration.

The PULSE Project: A Case of Use of Big Data Uses Toward a Cohomprensive Health Vision of City Well Being

Despite the silent effects sometimes hidden to the major audience, air pollution is becoming one of the most impactful threat to global health. Cities are the places where deaths due to air pollution are concentrated most.

In order to correctly address intervention and prevention thus is essential to assest the risk and the impacts of air pollution spatially and temporally inside the urban spaces. PULSE aims to design and build a large-scale data management system enabling real time analytics of health, behaviour and environmental data on air quality. The objective is to reduce the environmental and behavioral risk of chronic disease incidence to allow timely and evidence-driven management of epidemiological episodes linked in particular to two pathologies; asthma and type 2 diabetes in adult populations. developing a policy-making across the domains of health, environment, transport, planning in the PULSE test bed cities.

Baseline Modelling and Composite Representation of Unobtrusively (IoT) Sensed Behaviour Changes Related to Urban Physical Well-Being

We present the grounding approach, deployment and preliminary validation of the elementary devised model of physical well-being in urban environments, summarizing the heterogeneous personal Big Data (on physical activity/exercise, walking, cardio-respiratory fitness, quality of sleep and related lifestyle and health habits and status, continuously collected for over a year mainly through wearable IoT devices and survey instruments in 7 global testbed cities) into 5 composite domain indicators/indexes convenient for interpretation and use in predictive public health and preventive interventions. The approach is based on systematized comprehensive domain knowledge implemented through range/threshold-based rules from institutional and study recommendations, combined with statistical methods, and will serve as a representative and performance benchmark for evolution and evaluation of more complex and advanced well-being models for the aimed predictive analytics (incorporating machine learning methods) in subsequent development underway.

Mobile Assistive Application for Blind People in Indoor Navigation

Navigation is an important human task that needs the human sense of vision. In this context, recent technologies developments provide technical assistance to support the visually impaired in their daily tasks and improve their quality of life. In this paper, we present a mobile assistive application called “GuiderMoi” that retrieves information about directions using color targets and identifies the next orientation for the visually impaired. In order to avoid the failure in detection and the inaccurate tracking caused by the mobile camera, the proposed method based on the CamShift algorithm aims to introduce better location and identification of color targets. Tests were conduct in natural indoor scene. The results depending on the distance and the angle of view, defined the accurate values to have a highest rate of target recognition. This work has perspectives for this such as implicating the augmented reality and the intelligent navigation based on machine learning and real-time processing.

Modeling and Specification of Bootstrapping and Registration Design Patterns for IoT Applications

The architectures of software systems are becoming more complex, large, and dynamic. The design of these architectures allows architects to master building complex software systems. But, their informal description, may give rise to ambiguity, their understanding becomes more and more difficult and leads to the incorrect implementation of these software systems. There are many solutions allowing software architecture design. In this paper, we use software design patterns as a solution. This is due to their reusable software elements. Our principal objective is to propose other alternatives to the informal visual description of software architectures. In past work, we have studied Service Oriented Architectures. We used SOA design patterns with standard formal notations. This work is a continuation to the past one. We apply our approach on design patterns for the Internet of Things. We introduce a refinement-based approach for modeling IoT design patterns. It takes advantage of graphical modeling and formal method. It is organized around two main axes. The first axis is to provide modeling solutions in conformance with the UML standard language. The second axis covers the general specification of design pattern models with the Event-B method. As a result, we propose a design support tool for IoT architectures based on IoT design patterns. It allows modeling of correct-by-design software systems.

A Convolutional Neural Network for Lentigo Diagnosis

Using Reflectance Confocal Microscopy (RCM) for lentigo diagnosis is today considered essential. Indeed, RCM allows fast data acquisition with a high spatial resolution of the skin. In this paper, we use a deep convolutional neural network (CNN) to perform RCM image classification in order to detect lentigo. The proposed method relies on an InceptionV3 architecture combined with data augmentation and transfer learning. The method is validated on RCM data and shows very efficient detection performance with more than 98% of accuracy.

Secure E-Health Platform

Currently, the Internet has become a service hosting infrastructure through its interconnection of a very large number of heterogeneous objects, thus offering users several types of services implemented by different sectors. Although these services make people’s lives easier and provide them with a means of communication between their real and virtual worlds, they risk being a path of intrusion into their private lives, or in some cases an easy target for malicious individuals aiming to endanger human life. To avoid this, we have designed a secure e-health platform based on IoT that serves to monitor patients’ medical profiles remotely by collecting their medical records while ensuring their confidentiality and integrity.

Alzheimer’s Disease Early Detection Using a Low Cost Three-Dimensional Densenet-121 Architecture

The objective of this work is to detect Alzheimer’s disease using Magnetic Resonance Imaging. For this, we use a three-dimensional densenet-121 architecture. With the use of only freely available tools, we obtain good results: a deep neural network showing metrics of 87% accuracy, 87% sensitivity (micro-average), 88% specificity (micro-average), and 92% AUROC (micro-average) for the task of classifying five different classes (disease stages). The use of tools available for free means that this work can be replicated in developing countries.

Evaluation of removal efficiency of residual diclofenac in aqueous solution by nanocomposite tungsten-carbon using design of experiment

Wastewater containing pharmaceutical residual components must be treated before being discharged to the environment. This study was conducted to investigate the efficiency of tungsten-carbon nanocomposite in diclofenac removal using design of experiment (DOE). The 27 batch adsorption experiments were done by choosing three effective parameters (pH, adsorbent dose, and initial concentration) at three levels. The nanocomposite was prepared by tungsten oxide and activated carbon powder in a ratio of 1 to 4 mass. The remaining concentration of diclofenac was measured by a spectrometer with adding reagents of 2, 2'-bipyridine, and ferric chloride. Analysis of variance (ANOVA) was applied to determine the main and interaction effects. The equilibrium time for removal process was determined as 30 min. It was observed that the pH had the lowest influence on the removal efficiency of diclofenac. Nanocomposite gave a high removal at low concentration of 5.0 mg/L. The maximum removal for an initial concentration of 5.0 mg/L was 88.0% at contact time of 30 min. The results of ANOVA showed that adsorbent mass was among the most effective variables. Using DOE as an efficient method revealed that tungsten-carbon nanocomposite has high efficiency in the removal of residual diclofenac from the aqueous solution.

Sensor data quality processing for vital signs with opportunistic ambient sensing

Opportunistic ambient sensing involves placement of sensors appropriately so that intermittent contact can be made unobtrusively for gathering physiological signals for vital signs. In this paper, we discuss the results of our quality processing system used to extract heart rate from ballisto-cardiogram signals obtained from a micro-bending fiber optic sensor pressure mat. Visual inspection is used to label data into informative and non-informative classes based on their heart rate information. Five classifiers are employed for the classification process, i.e., random forest, support vector machine, multilayer, feedforward neural network, linear discriminant analysis, and decision tree. To compute the overall effectiveness of quality processing, the informative signals are processed to estimate interbeat intervals. The system was used to process, data collected from 50 human subjects sitting in a massage chair while performing different activities. Opportunistically collected data was obtained from the fiber optic sensor mat placed on the headrest of the massage chair. Using our classification approach, 57.37% of the dataset was able to provide informative signals. On the informative signals, random forest classifier achieves the best classification accuracy with a mean accuracy of 98.99%. The average of the mean absolute error between the estimated heart rate and the reference ECG is reduced from 13.2 to 8.47. Therefore, the proposed system shows a good robustness for opportunistic ambient sensing.

Solitary dural plasmacytoma with parenchymal invasion

Summary: Primary solitary dural plasmacytoma is an uncommon neoplasm of plasma cell. The clinical presentation and neuroradiological findings of these tumors may be deceptive and resemble to the other most prevalent brain tumors. Solitary plasmacytoma mostly shows a benign behavior and malignant features like local invasion is so rare. We describe the case of parenchymal brain involvement by solitary dural plasmacytoma.

An adaptable and flexible framework for assistive living of cognitively impaired people

On account of chronic neurocognitive disorders, many people progressively lose their autonomy and become more dependent on others, finally reaching the stage when they need round-the-clock care from caregivers. Over time, as patients' needs increase with the evolution of their diseases, caregivers experience increasing levels of stress and burden. Therefore, an assistive solution that is able to adapt to the changing needs of the end-users is needed. This need was considered as a major requirement that emerged from our field work and deployment experience in Singapore. In this paper, we focus on the technical aspects of our deployment, where we were interested in solving the technical requirement of adaptability and extendibility of the framework that has emerged from our predeployment analysis and discussions with professional caregivers. We expose our approach for dynamic integration of assistive services with their related sensing technologies and interaction devices and provide the technical results of the deployment of this solution. We also provide guidelines for real-world deployment of assistive solutions.

A study to demonstrate the use of FNA cytology rather than biopsy in the diagnosis of neonatal biliary atresia

OBJECTIVE

To study the use of fine needle aspiration (FNA) cytology in neonatal biliary atresia (BA).

METHODS

Twelve female and nine male patients (age range, 3-7 months; mean age, 4.5 months) with a pre-operative diagnosis of BA, who were scheduled for Kasia portoenterostomy and selected for intraoperative FNA, were studied.

RESULTS

Cholestasis, bile deposits, bile infarcts, hepatitic rosettes enclosing bile plugs, feathery degeneration of hepatocytes and inflammatory cells were seen in the cytological slides. Bile infarcts, rosette formation and inflammatory cells were mainly noticed in type 3 BA. Bile infarcts and hepatitic rosettes are surrogate findings for the diagnosis of the obstructive type of cholestasis.

CONCLUSION

With the help of imaging studies, FNA in a multidisciplinary setting can be diagnostic of neonatal BA when cytological features suggest the obstructive nature of cholestasis, but this procedure cannot replace completely liver biopsy for this diagnosis.

Deployment of assistive living technology in a nursing home environment: methods and lessons learned

BACKGROUND

With an ever-growing ageing population, dementia is fast becoming the chronic disease of the 21st century. Elderly people affected with dementia progressively lose their autonomy as they encounter problems in their Activities of Daily Living (ADLs). Hence, they need supervision and assistance from their family members or professional caregivers, which can often lead to underestimated psychological and financial stress for all parties. The use of Ambient Assistive Living (AAL) technologies aims to empower people with dementia and relieve the burden of their caregivers.The aim of this paper is to present the approach we have adopted to develop and deploy a system for ambient assistive living in an operating nursing home, and evaluate its performance and usability in real conditions. Based on this approach, we emphasise on the importance of deployments in real world settings as opposed to prototype testing in laboratories.

METHODS

We chose to conduct this work in close partnership with end-users (dementia patients) and specialists in dementia care (professional caregivers). Our trial was conducted during a period of 14 months within three rooms in a nursing home in Singapore, and with the participation of eight dementia patients and two caregivers. A technical ambient assistive living solution, consisting of a set of sensors and devices controlled by a software platform, was deployed in the collaborating nursing home. The trial was preceded by a pre-deployment period to organise several observation sessions with dementia patients and focus group discussions with professional caregivers. A process of ground truth and system's log data gathering was also planned prior to the trial and a system performance evaluation was realised during the deployment period with the help of caregivers. An ethical approval was obtained prior to real life deployment of our solution.

RESULTS

Patients' observations and discussions allowed us to gather a set of requirements that a system for elders with mild-dementia should fulfil. In fact, our deployment has exposed more concrete requirements and problems that need to be addressed, and which cannot be identified in laboratory testing. Issues that were neither forecasted during the design phase nor during the laboratory testing surfaced during deployment, thus affecting the effectiveness of the proposed solution. Results of the system performance evaluation show the evolution of system precision and uptime over the deployment phases, while data analysis demonstrates the ability to provide early detection of the degradation of patients' conditions. A qualitative feedback was collected from caregivers and doctors and a set of lessons learned emerged from this deployment experience.

CONCLUSION

Lessons learned from this study were very useful for our research work and can serve as inspiration for developers and providers of assistive living services. They confirmed the importance of real deployment to evaluate assistive solutions especially with the involvement of professional caregivers. They also asserted the need for larger deployments. Larger deployments will allow to conduct surveys on assistive solutions social and health impact, even though they are time and manpower consuming during their first phases.