Vesta: A digital health analytics platform for a smart home in a box

McConville, Ryan; Archer, Gareth; Craddock, Ian J; Kozłowski, Michał; Piechocki, Robert J.; Pope, James; Santos-Rodríguez, Raúl

doi:10.1016/j.future.2020.07.046

Cited by 19 publications

(23 citation statements)

References 32 publications

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“…However, in RSSI values we usually see the application of Kalman filters to eliminate noise. Since the goal in this work is to fuse accelerometers and RSSI values, we extracted the features proposed in [ 8 ]. First, we filter the RSSI and accelerometer values by applying a moving average for every 3 rows.…”

Section: Resultsmentioning

confidence: 99%

“…Usually, Kalman filtering techniques are applied to raw RSSI signals to eliminate noise. In the current application, instead of applying the usual Kalman or particle filtering, we adopted the feature extraction from RSSI values that was proposed in [ 8 ]. Feature extraction is usually found in the wearable sensors’ analysis.…”

Section: Introductionmentioning

confidence: 99%

“…We could summarize the contributions of this work in the following: Testing the efficiency of accelerometer measurements in room-level localization task, which is actually a classification problem. Examining the performance of feature extraction from RSSI readings, based on the features suggested in [ 8 ]. Assessing the performance of RSSI and accelerometer data together, in room-level localization, by combining them in an early level or in the results level.…”

Section: Introductionmentioning

confidence: 99%

“…Examining the performance of feature extraction from RSSI readings, based on the features suggested in [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

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Combining RSSI and Accelerometer Features for Room-Level Localization

Tsanousa

Xefteris

Meditskos

et al. 2021

Sensors

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The continuing advancements in technology have resulted in an explosion in the use of interconnected devices and sensors. Internet-of-Things (IoT) systems are used to provide remote solutions in different domains, like healthcare and security. A common service offered by IoT systems is the estimation of a person’s position in indoor spaces, which is quite often achieved with the exploitation of the Received Signal Strength Indication (RSSI). Localization tasks with the goal to locate the room are actually classification problems. Motivated by a current project, where there is the need to locate a missing child in crowded spaces, we intend to test the added value of using an accelerometer along with RSSI for room-level localization and assess the performance of ensemble learning methods. We present here the results of this preliminary approach of the early and late fusion of RSSI and accelerometer features in room-level localization. We further test the performance of the feature extraction from RSSI values. The classification algorithms and the fusion methods used to predict the room were evaluated using different protocols applied to a public dataset. The experimental results revealed better performance of the RSSI extracted features, while the accelerometer’s individual performance was poor and subsequently affected the fusion results.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Examining the performance of feature extraction from RSSI readings, based on the features suggested in [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Combining RSSI and Accelerometer Features for Room-Level Localization

Tsanousa

Xefteris

Meditskos

et al. 2021

Sensors

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“…• Technology facilitating healthy and supportive social networks to provide peer support should be further developed 44 • Monitoring of dietary intake could be improved by technology, for example by analysing supermarket purchasing data. 45 • The use of activity sensors and similar devices in the home 46 may identify patterns of behaviour which could increase the risk of type 2 diabetes or impair self management. This information could be incorporated to design more effective environments or ways to help.…”

Section: Using Technology To Understand People's Environments and Make Them Healthiermentioning

confidence: 99%

Optimizing the use of technology to support people with diabetes: research recommendations from Diabetes UK’s 2019 diabetes and technology workshop

et al. 2021

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The need to optimize the use of, access to and effectiveness of technology to help improve the lives of people with diabetes and make managing the condition easier is a key issue for diabetes care and for people with diabetes. 1 While a significant volume of research focuses on the issues around technology in diabetes, the speed at which new technologies have been emerging and developing can ensure that best practice is based on solid

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