The Challenges of Connecting Smart Home Health Sensors to Cloud Analytics

Wallace, R. Bruce; Horsfall, Frank; Goubran, Rafik; El-Haraki, Ali; Knoefel, Frank

doi:10.1109/sas.2019.8706128

Cited by 9 publications

(5 citation statements)

References 19 publications

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“…In recent years, many scholars at home and abroad have carried out in-depth research in this field and achieved rich results. Wallace et al [3] realized automatic a-trial for fibrillation and coronary heart disease by analyzing the pulse signal. e authors of [4], based on the Internet of ings technology, builds a health management system, uses a variety of machine learning techniques to analyze a variety of disease data sets, and finds that the random forest has a good prediction effect on a variety of diseases, with reasonable accuracy, but the algorithm has poor robustness.…”

Section: Introductionmentioning

confidence: 99%

Data Fusion Approach for Managing Clinical Data in an Industrial Environment using IoT

Kulkarni¹,

Kulkarni²,

Babu

et al. 2022

Scientific Programming

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As health issues continue to become more prevalent as the population grows, building a public health network is critical for enhancing the overall health quality of the community. This study offers an Internet of Things (IoT) based health care system that can be employed in the context of community medical care industrial areas. The main focus of this research is to develop a disease prediction strategy that could be applied to community health services using theoretical modelling. Using principal component analysis (PCA) and cluster analysis, an artificial bee colony (ABC) creates a nonlinear support vector machine (SVM) classifier pair. Feature-level fusion analysis was performed to detect probable abnormalities. The results of the experiments reveal that the SVM model offers significant benefits in disease prediction. In the SVM illness prediction model, the ABC algorithm has the best parameter optimization effect in terms of accuracy, time, and other factors. The suggested method outperformed the traditional SVM and BP neural network methods by 17.24 percent and 72.41 percent, respectively. It can lower the RMSE and improve assessment indicators like the precision recall rate and the F-measure, demonstrating the method’s validity and accuracy. As a result, it is frequently used in community health management, geriatric community monitoring, and clinical medical therapy in an industrial environment.

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Section: Introductionmentioning

confidence: 99%

Data Fusion Approach for Managing Clinical Data in an Industrial Environment using IoT

Kulkarni¹,

Kulkarni²,

Babu

et al. 2022

Scientific Programming

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“…Over the past few years, there has been a rapid growth of the Internet of Things (IoT), and data acquisition and storage techniques [18]. Within a smart home, each of the sensors are now constantly relaying data which are being collected over months.…”

Section: Introductionmentioning

confidence: 99%

Visualizing Effects of COVID-19 Social Isolation with Residential Activity Big Data Sensor Data

Rajkumar

Wallace

Ault

et al. 2020

2020 IEEE International Conference on Big Data (Big Data)

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The ability to understand and visualize big data sets is of increasing interest to caregivers and clinicians as ambient home sensing can provide massive amounts of data related to the activities of residents. However, this data is only useful if it can be effectively and simply visualized for review and analysis. This paper presents the visualization of longitudinal data sets from ambient well-being sensors deployed in 3 residences that have a spousal pair dyad where 1 resident has been diagnosed with Mild Cognitive Impairment or Dementia and the spousal partner is acting as a caregiver. The paper presents the differences in activity and behaviour that can be observed in the 3 residences by comparing two 30-day periods prior to and one 30-day period during COVID-19 social isolation precautions. The work shows the potential for this circle plot based visualization technique to summarize resident activity and also to convey external factors such as the variation in solar day that can itself influence behaviour.

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“…Research is required into the network services provided by telecommunications providers, so they are integrated with capabilities within the residence and cloud to support aging-in-place through supportive smart home systems. Early work included preliminary traffic modelling for these applications [16] and the security and privacy considerations for home monitoring [17], as well as models for scalable services through applications of cloud processing [18].…”

Section: Motivationmentioning

confidence: 99%

“…[6]- [15]. Research has also considered cloud processing in such systems that could provide support back to the individual or supply their family and caregivers with information related to well-being, thus providing more advanced functions and services to support aging-in-place [14], [16], [26]- [28]. Many research projects have explored such possibilities where some projects focused on (1) daily cognitive evaluations through homebased sensing [6], [11], [15], [29], (2) detection of overnight wandering in Dementia patients using a multi-sensor system [8]- [10], (3) the challenges accompanied by cloud processing and data analytics in smart home systems [16], [26], [27], (4) the security and reliability aspects of smart home systems [14], (5) real-time location tracking in indoor environments using Wi-Fi and a wearable sensor [30], (6) monitoring of user activity and rate of activity using RF-ID technology [31], (7) deployment of in-home robots to provide human-level social engagement and support for elderly with dementia [28], and (8) determining electrodermal activity as means for emotion-sensing using a wearable Printed Circuit Board (PCB) [32].…”

Section: Supportive Smart Home Systemsmentioning

confidence: 99%

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The Supportive Smart Home System: Implications and Solutions for Service Providers

Almhairat¹

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Models Predicting Smart Home Sensor Measurements" [2], and "Maintaining Synchrony of Dual Machine Learning: A Phase-Locked Loop Approach" [3]. The above-stated references are conference proceedings published by the Institute of Electrical and Electronics Engineers (IEEE). The IEEE allows the reuse of published articles in a thesis or dissertation without requesting permission (see Declaration message from IEEE below).

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The Challenges of Connecting Smart Home Health Sensors to Cloud Analytics

Cited by 9 publications

References 19 publications

Data Fusion Approach for Managing Clinical Data in an Industrial Environment using IoT

Data Fusion Approach for Managing Clinical Data in an Industrial Environment using IoT

Visualizing Effects of COVID-19 Social Isolation with Residential Activity Big Data Sensor Data

The Supportive Smart Home System: Implications and Solutions for Service Providers

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