Smart Homes for Elderly Healthcare—Recent Advances and Research Challenges

By Sumit Majumder, Emad Aghayi, M. Noferesti, Hamidreza Memarzadeh-Tehran, Tapas K. Mondal, Zhibo Pang, M. Deen. Italian National Conference on Sensors, 2017

537 citations recorded by the source corpus.

Advancements in medical science and technology, medicine and public health coupled with increased consciousness about nutrition and environmental and personal hygiene have paved the way for the dramatic increase in life expectancy globally in the past several decades. However, increased life expectancy has given rise to an increasing aging population, thus jeopardizing the socio-economic structure of many countries in terms of costs associated with elderly healthcare and wellbeing. In order to cope with the growing need for elderly healthcare services, it is essential to develop affordable, unobtrusive and easy-to-use healthcare solutions. Smart homes, which incorporate environmental and wearable medical sensors, actuators, and modern communication and information technologies, can enable continuous and remote monitoring of elderly health and wellbeing at a low cost. Smart homes may allow the elderly to stay in their comfortable home environments instead of expensive and limited healthcare facilities. Healthcare personnel can also keep track of the overall health condition of the elderly in real-time and provide feedback and support from distant facilities. In this paper, we have presented a comprehensive review on the state-of-the-art research and development in smart home based remote healthcare technologies.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, General Health Professions

View Paper: https://www.semanticscholar.org/paper/aae849da305bd62acc6829360081d0751fb82189

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. M. Jamal Deen
2. Andrea Cavallaro
3. Hamed Haddadi
4. Sumit Majumder

In Computer Vision and Pattern Recognition

1. Hamed Haddadi
2. Andrea Cavallaro
3. Richard G. Clegg
4. Mohammad Malekzadeh
5. M. Jamal Deen
6. Sumit Majumder
7. Ali Shahin Shamsabadi

In General Health Professions

1. M. Jamal Deen
2. Sumit Majumder

Citations in the corpus

• Smartphone Sensors for Health Monitoring and Diagnosis (2019, background): “In fact, heart disease and cancer together account for 48% of all deaths, thus becoming the leading cause of mortality [7,8].”
• Mobile sensor data anonymization (2018, methodology): “using a binary cross-entropy for fall detection [18].”