Health Monitoring in Smart Homes Utilizing Internet of Things

Linkous, Lauren; Zohrabi, Nasibeh; Abdelwahed, Sherif

doi:10.1109/chase48038.2019.00020

Cited by 8 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This study identified a limitation related to the distance threshold (20 m) between the body-attached sensors and the server receivers, and the greater distance, the more data loss. Alnaggar et al (2023) (Linkous et al, 2019). Adib et al (2015) utilized ubiquitous sensing technologies to remotely monitor vital signs using a wireless sensing technology called Vital-Radio, achieving a high accuracy of 99% for breathing and heart rate measurements.…”

Section: Related Workmentioning

confidence: 99%

Heart rate prediction with contactless active assisted living technology: a smart home approach for older adults

Wang,

Cao,

Kaur

et al. 2024

Front. Artif. Intell.

View full text Add to dashboard Cite

BackgroundAs global demographics shift toward an aging population, monitoring their heart rate becomes essential, a key physiological metric for cardiovascular health. Traditional methods of heart rate monitoring are often invasive, while recent advancements in Active Assisted Living provide non-invasive alternatives. This study aims to evaluate a novel heart rate prediction method that utilizes contactless smart home technology coupled with machine learning techniques for older adults.MethodsThe study was conducted in a residential environment equipped with various contactless smart home sensors. We recruited 40 participants, each of whom was instructed to perform 23 types of predefined daily living activities across five phases. Concurrently, heart rate data were collected through Empatica E4 wristband as the benchmark. Analysis of data involved five prominent machine learning models: Support Vector Regression, K-nearest neighbor, Random Forest, Decision Tree, and Multilayer Perceptron.ResultsAll machine learning models achieved commendable prediction performance, with an average Mean Absolute Error of 7.329. Particularly, Random Forest model outperformed the other models, achieving a Mean Absolute Error of 6.023 and a Scatter Index value of 9.72%. The Random Forest model also showed robust capabilities in capturing the relationship between individuals' daily living activities and their corresponding heart rate responses, with the highest R2 value of 0.782 observed during morning exercise activities. Environmental factors contribute the most to model prediction performance.ConclusionsThe utilization of the proposed non-intrusive approach enabled an innovative method to observe heart rate fluctuations during different activities. The findings of this research have significant implications for public health. By predicting heart rate based on contactless smart home technologies for individuals' daily living activities, healthcare providers and public health agencies can gain a comprehensive understanding of an individual's cardiovascular health profile. This valuable information can inform the implementation of personalized interventions, preventive measures, and lifestyle modifications to mitigate the risk of cardiovascular diseases and improve overall health outcomes.

show abstract

Section: Related Workmentioning

confidence: 99%

Heart rate prediction with contactless active assisted living technology: a smart home approach for older adults

Wang,

Cao,

Kaur

et al. 2024

Front. Artif. Intell.

View full text Add to dashboard Cite

show abstract

“…Nearables are also able to form ad hoc networks to allow them to collaborate, extending the range of in-home localization services for patients. This was discussed in the context of the AAL (20%) [54,58,59] and dementia care studies (6%) [60][61][62].…”

Section: The Application Of Sensors and Actuators Within Environmentsmentioning

confidence: 99%

“…6% Indoor localization. [16,57,58,[60][61][62]64] AAL for elderly patients living independently at home was often discussed in conjunction with fall detection [53,54,82]. General physiological and psychological well-being was a wider category that featured sensor and actuator combinations that applied to less common medical conditions.…”

Section: %mentioning

confidence: 99%

Healthcare in Asymmetrically Smart Future Environments: Applications, Challenges and Open Problems

Dowdeswell,

Sinha,

Kuo

et al. 2023

Electronics

View full text Add to dashboard Cite

The Internet of Medical Things (IoMT) offers promising ways to meet healthcare needs of patients recovering in their own homes and other environments. Interconnected and resilient smart systems offer innovative and cost-effective ways of supporting patients by capitalizing on available devices and networking infrastructure. However, future environments will not be uniformly smart, and there will be asymmetries where our environments’ (home, work, etc.) resources and capabilities differ. Technological solutions will need to adapt to such asymmetries and provide high-quality service and equitable healthcare. This article presents a systematic mapping study that explores opportunities and challenges in building next-generation IoMT smart systems for future environments. The study spans academic literature published in the decade from 2011 to 2021, profiling it from three distinct perspectives: Smart Systems, Future Environments, and Tech-Assisted Health. Each perspective was explored via a Domain Expert-Driven Systematic Mapping protocol to establish where the research is focused and to identify research gaps. From an initial search of 495 studies, 113 were mapped to a set of predefined ontology classes, spanning 6 strategic focus categories. The mapping identified sensing technologies for medical vitals and sensor fusion technologies to combine measurements for more complex analysis, cloud platforms, and connectivity challenges; health conditions that have received the most attention in healthcare smart systems; issues and opportunities in handling large data volumes in integrated smart systems; as well as security and privacy challenges. We find that future middleware frameworks will require a greater degree of interoperability and maturity to fully deliver value. Promising middleware and integration frameworks will require significant adaption and refinement to coexist effectively with current healthcare technologies. Privacy and security are critical factors in healthcare but are currently poorly supported by IoT infrastructures, especially across multiple environments.

show abstract