Contribution to a Smart Home Design for Medical Surveillance

Eloutouate, Lamiae; Elouaai, Fatiha; Bouhorma, Mohammed; Hicham, Gibet Tani

doi:10.1007/978-3-030-37629-1_78

Cited by 1 publication

(1 citation statement)

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“…Researchers have attempted to build and analyze health indicators using innovative data sources ( 27 – 29 ). They are exploring the use of smartphones ( 30 ), online searches ( 31 ), social media ( 7 ), wearables ( 32 ), ambient sensors ( 33 ), electronic health records (EHRs) ( 27 , 34 ), medical-administrative records ( 27 ), and pharmacy sales ( 28 ) to broaden the scope of surveillance.…”

Section: Next-generation Data Sources For Public Health Surveillancementioning

confidence: 99%

NextGen Public Health Surveillance and the Internet of Things (IoT)

Sahu

Majowicz

Dubin

et al. 2021

Front. Public Health

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Recent advances in technology have led to the rise of new-age data sources (e.g., Internet of Things (IoT), wearables, social media, and mobile health). IoT is becoming ubiquitous, and data generation is accelerating globally. Other health research domains have used IoT as a data source, but its potential has not been thoroughly explored and utilized systematically in public health surveillance. This article summarizes the existing literature on the use of IoT as a data source for surveillance. It presents the shortcomings of current data sources and how NextGen data sources, including the large-scale applications of IoT, can meet the needs of surveillance. The opportunities and challenges of using these modern data sources in public health surveillance are also explored. These IoT data ecosystems are being generated with minimal effort by the device users and benefit from high granularity, objectivity, and validity. Advances in computing are now bringing IoT-based surveillance into the realm of possibility. The potential advantages of IoT data include high-frequency, high volume, zero effort data collection methods, with a potential to have syndromic surveillance. In contrast, the critical challenges to mainstream this data source within surveillance systems are the huge volume and variety of data, fusing data from multiple devices to produce a unified result, and the lack of multidisciplinary professionals to understand the domain and analyze the domain data accordingly.

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