Technology and dementia: the future is now

Background: Technology has multiple potential applications to dementia from diagnosis and assessment to care delivery and supporting ageing in place. Objectives: To summarise key areas of technology development in dementia and identify future directions and implications. Method: Members of the US Alzheimer’s Association Technology Professional Interest Area involved in delivering the annual pre-conference summarised existing knowledge on current and future technology developments in dementia. Results: The main domains of technology development are as follows: (i) diagnosis, assessment and monitoring, (ii) maintenance of functioning, (iii) leisure and activity, (iv) caregiving and management. Conclusions: The pace of technology development requires urgent policy, funding and practice change, away from a narrow medical approach, to a holistic model that facilitates future risk reduction and pre- vention strategies, enables earlier detection and supports implementation at scale for a meaningful and fulfilling life with dementia

Interpreting health events in big data using qualitative traditions

© The Author(s) 2020. The training of artificial intelligence requires integrating real-world context and mathematical computations. To achieve efficacious smart health artificial intelligence, contextual clinical knowledge serving as ground truth is required. Qualitative methods are well-suited to lend consistent and valid ground truth. In this methods article, we illustrate the use of qualitative descriptive methods for providing ground truth when training an intelligent agent to detect Restless Leg Syndrome. We show how one interdisciplinary, inter-methodological research team used both sensor-based data and the participant’s description of their experience with an episode of Restless Leg Syndrome for training the intelligent agent. We make the case for clinicians with qualitative research expertise to be included at the design table to ensure optimal efficacy of smart health artificial intelligence and a positive end-user experience

Automated smart home assessment to support pain management: Multiple methods analysis

©Roschelle L Fritz, Marian Wilson, Gordana Dermody, Maureen Schmitter-Edgecombe, Diane J Cook. Objective: This study aimed to determine if a smart home can detect pain-related behaviors to perform automated assessment and support intervention for persons with chronic pain.Background: Poorly managed pain can lead to substance use disorders, depression, suicide, worsening health, and increased use of health services. Most pain assessments occur in clinical settings away from patients’ natural environments. Advances in smart home technology may allow observation of pain in the home setting. Smart homes recognizing human behaviors may be useful for quantifying functional pain interference, thereby creating new ways of assessing pain and supporting people living with pain.Methods: A multiple methods, secondary data analysis was conducted using historic ambient sensor data and weekly nursing assessment data from 11 independent older adults reporting pain across 1-2 years of smart home monitoring. A qualitative approach was used to interpret sensor-based data of 27 unique pain events to support clinician-guided training of a machine learning model. A periodogram was used to calculate circadian rhythm strength, and a random forest containing 100 trees was employed to train a machine learning model to recognize pain-related behaviors. The model extracted 550 behavioral markers for each sensor-based data segment. These were treated as both a binary classification problem (event, control) and a regression problem.Results: We found 13 clinically relevant behaviors, revealing 6 pain-related behavioral qualitative themes. Quantitative results were classified using a clinician-guided random forest technique that yielded a classification accuracy of 0.70, sensitivity of 0.72, specificity of 0.69, area under the receiver operating characteristic curve of 0.756, and area under the precision-recall curve of 0.777 in comparison to using standard anomaly detection techniques without clinician guidance (0.16 accuracy achieved; P \u3c .001). The regression formulation achieved moderate correlation, with r=0.42.Conclusions: Findings of this secondary data analysis reveal that a pain-assessing smart home may recognize pain-related behaviors. Utilizing clinicians’ real-world knowledge when developing pain-assessing machine learning models improves the model’s performance. A larger study focusing on pain-related behaviors is warranted to improve and test model performance

A new device to track and identify people in a multi-residents context

In recent years, technologies for monitoring people inside a house lead to the development of smart home. However, the vast majority of works deals only in monitoring the activities of a single inhabitant. Nevertheless, most of the people in the current context of ageing population does not live alone. Recognizing the activities performed by each inhabitant in a house is an important challenge. A first step to achieve this is to be able to distinguish where each inhabitant is in the house. In this paper, we present a new device to track and identify people in a multi-residents context. Experiments have been conducted to validate the reliability and accuracy of the proposed device

Monitoring changes in physical activity data during strength training of people with myotonic dystrophy type 1

Myotonic dytrophy type 1 (DM1) is an incurable neuromuscular disease and muscle weakness is a prominent symptom. Research has shown that strength training can be an interesting solution to help with this symptom. Therefore an assistive technology aiming at supervising strength training at home for people with DM1 has been developed and tested in the home of 10 patients for 10 weeks. As many change point detection (CPD) techniques have been used for monitoring change in activity data in the past, no one applied these techniques to physical activities of people with DM1 disease. Hence, physical activity data have been collected during the 10-week experiment and state-of-the-art CPD algorithm has been used to analyze changes in physical activity during the strength training program at home. The results prove that many challenges need to be addressed in this context and could act as a guideline for future works

Revisión sistemática sobre las diferentes aplicaciones tecnológicas para mejorar la autonomía del adulto mayor en el hogar

Introducción: Las personas mayores de 60 años son el grupo de población de mayor crecimiento en el mundo afectando a nuestras economías, servicios sociales y atención médica. Este cambio demográfico es un serio desafío no solo para el cuidado de la salud sino también para la sociedad, para mantener a las poblaciones que envejecen.Objetivo: El objetivo principal del presente trabajo fue el de analizar los diferentes dispositivos e innovaciones tecnológicas existentes que pueden mejorar la atención y ofrecer apoyo para la vida independiente de los adultos mayores en su hogar. Metodología: Se ha llevado a cabo un estudio de revisión sistemática según la guía de PRISMA. Se ha realizado una búsqueda en las bases de datos Pubmed, Scopus, Web of Science y Science Direct.Resultados: Los resultados pusieron de manifiesto que existen una gran cantidad de sensores portátiles, entornos inteligentes, tecnologías robóticas cuyo fin es el de ayudar en la localización, seguridad, protección, seguimiento de las actividades diarias y en la detección temprana de demencias del adulto mayor. Conclusiones: Es necesario proporcionar recursos que satisfagan la vida diaria de las personas, pero es más esencial determinar qué entornos se deben proporcionar para que las poblaciones que envejecen puedan vivir de una manera segura, independiente, cómoda y saludable. Además, analizar las diferentes soluciones tecnológicas existentes puede servir de ayuda como recopilación de los diferentes recursos y herramientas existentes, con resultados estudiados de sus efectos, para los diferentes profesionales, familiares o cuidadores que se dedican al cuidado de usuarios vulnerables de beneficiarse de ellas, ofreciéndoles más alternativas innovadoras y aprovechando todo lo que estás pueden ofrecernos. Palabras clave: "demencia", "sensores de movimiento", "sensores de tercera edad", "sensores vestibles", "hogar inteligente", "salud", "personas mayores", "actividades diarias", "calidad de vida".<br /

State-of-the-Art Sensors for Remote Care of People with Dementia during a Pandemic: A Systematic Review

In the last decade, there has been a significant increase in the number of people diagnosed with dementia. With diminishing public health and social care resources, there is substantial need for assistive technology-based devices that support independent living. However, existing devices may not fully meet these needs due to fears and uncertainties about their use, educational support, and finances. Further challenges have been created by COVID-19 and the need for improved safety and security. We have performed a systematic review by exploring several databases describing assistive technologies for dementia and identifying relevant publications for this review. We found there is significant need for appropriate user testing of such devices and have highlighted certifying bodies for this purpose. Given the safety measures imposed by the COVID-19 pandemic, this review identifies the benefits and challenges of existing assistive technologies for people living with dementia and their caregivers. It also provides suggestions for future research in these areas

Development of an anomaly alert system triggered by unusual behaviors at home

In many countries, the number of elderly people has grown due to the increase in the life expectancy of the population, many of whom currently live alone and are prone to having accidents that they cannot report, especially if they are immobilized. For this reason, we have developed a non-intrusive IoT device, which, through multiple integrated sensors, collects information on habitual user behavior patterns and uses it to generate unusual behavior rules. These rules are used by our SecurHome system to send alert messages to the dependent person's family members or caregivers if their behavior changes abruptly over the course of their daily life. This document describes in detail the design and development of the SecurHome system.SecurHome is a multidisciplinary research project on ageing in the framework of the International Centre on Ageing (CENIE). It is a project evaluated by the Spanish State Agency for Research and co-financed by the European Regional Development Fund in the framework of the Interreg V-A Spain–Portugal Cooperation Programme (POCTEP) 2014–2020

The SHAPES Smart Mirror Approach for Independent Living, Healthy and Active Ageing

The benefits that technology can provide in terms of health and support for independent living are in many cases not enough to break the barriers that prevent older adults from accepting and embracing technology. This work proposes a hardware and software platform based on a smart mirror, which is equipped with a set of digital solutions whose main focus is to overcome older adults’ reluctance to use technology at home and wearable devices on the move. The system has been developed in the context of two use cases: the support of independent living for older individuals with neurodegenerative diseases and the promotion of physical rehabilitation activities at home. Aspects such as reliability, usability, consumption of computational resources, performance and accuracy of the proposed platform and digital solutions have been evaluated in the initial stages of the pilots within the SHAPES project, an EU-funded innovation action. It can be concluded that the SHAPES smart mirror has the potential to contribute as a technological breakthrough to overcome the barriers that prevent older adults from engaging in the use of assistive technologies.Los beneficios que la tecnología puede brindar en términos de salud y apoyo para la vida independiente en muchos casos no son suficientes para romper las barreras que impiden que los adultos mayores acepten y adopten la tecnología. Este trabajo propone una plataforma de hardware y software basada en un espejo inteligente, la cual está equipada con un conjunto de soluciones digitales cuyo enfoque principal es superar la reticencia de los adultos mayores a usar tecnología en el hogar y dispositivos portátiles en movimiento. El sistema se ha desarrollado en el contexto de dos casos de uso: el apoyo a la vida independiente de personas mayores con enfermedades neurodegenerativas y la promoción de actividades de rehabilitación física en el hogar. Aspectos como la fiabilidad, la usabilidad, el consumo de recursos informáticos, el rendimiento y la precisión de la plataforma y las soluciones digitales propuestas se han evaluado en las etapas iniciales de los pilotos del proyecto SHAPES, una acción de innovación financiada con fondos europeos. Se puede concluir que el espejo inteligente SHAPES tiene el potencial de contribuir como un avance tecnológico para superar las barreras que impiden que los adultos mayores se involucren en el uso de tecnologías de asistencia

A smart home environment to support safety and risk monitoring for the elderly living independently

The elderly prefer to live independently despite vulnerability to age-related challenges. Constant monitoring is required in cases where the elderly are living alone. The home environment can be a dangerous environment for the elderly living independently due to adverse events that can occur at any time. The potential risks for the elderly living independently can be categorised as injury in the home, home environmental risks and inactivity due to unconsciousness. The main research objective was to develop a Smart Home Environment (SHE) that can support risk and safety monitoring for the elderly living independently. An unobtrusive and low cost SHE solution that uses a Raspberry Pi 3 model B, a Microsoft Kinect Sensor and an Aeotec 4-in-1 Multisensor was implemented. The Aeotec Multisensor was used to measure temperature, motion, lighting, and humidity in the home. Data from the multisensor was collected using OpenHAB as the Smart Home Operating System. The information was processed using the Raspberry Pi 3 and push notifications were sent when risk situations were detected. An experimental evaluation was conducted to determine the accuracy with which the prototype SHE detected abnormal events. Evaluation scripts were each evaluated five times. The results show that the prototype has an average accuracy, sensitivity and specificity of 94%, 96.92% and 88.93% respectively. The sensitivity shows that the chance of the prototype missing a risk situation is 3.08%, and the specificity shows that the chance of incorrectly classifying a non-risk situation is 11.07%. The prototype does not require any interaction on the part of the elderly. Relatives and caregivers can remotely monitor the elderly person living independently via the mobile application or a web portal. The total cost of the equipment used was below R3000