Analysis of Smart Home use based on the Degree of Health-related Risk Variation: A Cross - Sectional National Survey in China

BackgroundDigital health has become a heated topic today and smart homes have received much attention as an important area of digital health. However, most of the existing studies have focused on discussing the impact of smart homes on people or the attitudes of older people towards smart homes. Only few studies have focused on relationship between health-related risks and use of smart homes.AimsTo investigate the association between health-related risks and the use of smart homes, provide new recommendations to promote the implementation of digital health strategies and achieve health for all.MethodsWe used data from 11,031 participants aged 18 and above. The population was clustered based on five health-related risk factors: perceived social support, family health, health literacy, media use, and chronic diseases self-behavioral management. A total of 23 smart homes were categorized into three sub-categories: entertainment smart home, functional smart home, and health smart home. We analyzed demographic characteristics and utilization rate of smart home across different cluster.ResultsThe participants were clustered into three groups: low risk, meddle risk, and high risk. The utilization rate of smart home was the most popular in the low risk group (total smart home: 86.97%; entertainment smart home: 61.07%, functional smart home: 77.42%, and health smart home: 75.33%; p < 0.001). For entertainment smart home, smart TV had the highest utilization rate (low risk: 45.73%; middle risk: 43.52%, high risk: 33.38%, p<0.001). For functional smart home, smart washing machine (low risk: 37.66%, middle risk: 35.11%, high risk: 26.49%; p<0.001) and smart air conditioner (low risk: 35.95%, middle risk: 29.13%, high risk: 24.61%) were higher than other of this category. For health smart home, sports bracelet has the highest utilization rate (low risk: 37.29%, middle risk: 24.49%, high risk: 22.83%).ConclusionHealth-related risks are an important factor affecting the use of smart homes. Joint efforts of government and product manufacturers are needed to broaden the smart home market and promote the implementation of digital health strategies.

Genetic algorithm–optimized support vector machine for real-time activity recognition in health smart home

Health smart home, as a typical application of Internet of things, provides a new solution for remote medical treatment. It can effectively relieve pressure from shortage of medical resources caused by aging population and help elderly people live at home more independently and safely. Activity recognition is the core of health smart home. This technology aims to recognize the activity patterns of users from a series of observations on the user’ actions and the environmental conditions, so as to avoid distress situations as much as possible. However, most of the existing researches focus on offline activity recognition, but not good at online real-time activity recognition. Besides, the feature representation techniques used for offline activity recognition are generally not suitable for online scenarios. In this article, the authors propose a real-time online activity recognition approach based on the genetic algorithm–optimized support vector machine classifier. In order to support online real-time activity recognition, a new sliding window-based feature representation technique enhanced by mutual information between sensors is devised. In addition, the genetic algorithm is used to automatically select optimal hyperparameters for the support vector machine model, thereby reducing the recognition inaccuracy caused by manual tuning of hyperparameters. Finally, a series of comprehensive experiments are conducted on freely available data sets to validate the effectiveness of the proposed approach.

Explorando tecnologias de IoT no contexto de Health Smart Home: uma abordagem para detecção de quedas em pessoas idosas

Atualmente, é crescente o número de pacientes que são tratados em casa, principalmente em países como o Japão, Estados Unidos e da Europa. Além disso, o número de idosos tem aumentado significativamente nos últimos quinze anos, e essas pessoas, muitas vezes, preferem receber tratamento médico em suas residências. No entanto, podem acontecer situações críticas durante esse período de recuperação, como por exemplo, o paciente idoso sofrer uma queda e agravar o seu quadro clínico. Neste cenário, avanços em Computação Ubíqua e Internet das Coisas (IoT) têm contribuído para evitar essas situações. Em particular, dispositivos embarcados, juntamente com a captura de movimentos por meio de sensores, podem ser aplicados para desenvolver soluções que ofereçam mais segurança para essas pessoas. Todavia, observa-se uma dificuldade de classificar os movimentos de um indivíduo e identificar, de fato, um movimento considerado anormal. Assim, o principal objetivo deste trabalho é a detecção e classificação dos movimentos utilizando dados conjuntos de sensores distintos e dispositivo embarcado. Paradigmas de Inteligência Artificial (AI) foram aplicados para a classificação dos movimentos e os testes realizados na arquitetura SAHHc. Os resultados apontaram uma precisão de 96.62\% na identificação das atividades executadas por um paciente/idoso.