Investigation of an Indoor Air Quality Sensor for Asthma Management in Children

Jaimini, Utkarshani; Banerjee, Tanvi; Romine, William L.; Thirunarayan, Krishnaprasad; Sheth, Amit P.; Kalra, Maninder

doi:10.1109/lsens.2017.2691677

Cited by 38 publications

(25 citation statements)

References 14 publications

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“…This brings with it challenges of ensuring the reliability and quality of the data [5] [6]. For instance, there are a number of sensors for activity tracking (Garmin, Fitbit, etc.)…”

Section: Challengesmentioning

confidence: 99%

Augmented personalized health: How smart data with IoTs and AI is about to change healthcare

Sheth

Jaimini

Thirunarayan

et al. 2017

2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI)

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Healthcare as we know it is in the process of going through a massive change - from episodic to continuous, from disease focused to wellness and quality of life focused, from clinic centric to anywhere a patient is, from clinician controlled to patient empowered, and from being driven by limited data to 360-degree, multimodal personal-public-population physical-cyber-social big data driven. While ability to create and capture data is already here, the upcoming innovations will be in converting this big data into smart data through contextual and personalized processing such that patients and clinicians can make better decisions and take timely actions for augmented personalized health. This paper outlines current opportunities and challenges, with a focus on key AI approaches to make this a reality. The broader vision is exemplified using three ongoing applications (asthma in children, bariatric surgery, and pain management) as part of the Kno.e.sis kHealth personalized digital health initiative.

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“…This brings with it challenges of ensuring the reliability and quality of the data [5] [6]. For instance, there are a number of sensors for activity tracking (Garmin, Fitbit, etc.)…”

Section: Challengesmentioning

confidence: 99%

Augmented personalized health: How smart data with IoTs and AI is about to change healthcare

Sheth

Jaimini

Thirunarayan

et al. 2017

2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI)

Self Cite

View full text Add to dashboard Cite

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“…W ith the increasing growth worldwide of active population working inside a building, the management of indoor air quality is becoming crucially important for human health and work efficiency [1]. In this regard, the development of smart buildings is aimed to provide comfort and improved indoor air quality (IAQ) for occupants.…”

Section: Introductionmentioning

confidence: 99%

Sensing Data Fusion for Enhanced Indoor Air Quality Monitoring

Metia

Phung

2020

IEEE Sensors J.

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Multisensor fusion of air pollutant data in smart buildings remains an important input to address the well-being and comfort perceived by their inhabitants. An integrated sensing system is part of a smart building where real-time indoor air quality data are monitored round the clock using sensors and operating in the Internet-of-Things (IoT) environment. In this work, we propose an air quality management system merging indoor air quality index (IAQI) and humidex into an enhanced indoor air quality index (EIAQI) by using sensor data on a realtime basis. Here, indoor air pollutant levels are measured by a network of waspmote sensors while IAQI and humidex data are fused together using an extended fractional-order Kalman filter (EFKF). According to the obtained EIAQI, overall air quality alerts are provided in a timely fashion for accurate prediction with enhanced performance against measurement noise and nonlinearity. The estimation scheme is implemented by using the fractional-order modeling and control (FOMCON) toolbox. A case study is analysed to prove the effectiveness and validity of the proposed approach.

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“…Therefore, this study focuses on the multivariate data which has high correlation. Data reduction models with multidimensional sensors are presented in [8][9][10]. In [8] and [9][10], the authors applied MLR and PCA based models respectively.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Real-Time Multivariate Data Reduction Models for Adaptive-Threshold in Wireless Sensor Networks

et al. 2017

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This paper presents a new metric to assess the performance of different multivariate data reduction models in wireless sensor networks (WSNs). The proposed metric is called Updating Frequency Metric (UFM) which is defined as the frequency of updating the model reference parameters during data collection. A method for estimating the error threshold value during the training phase is also suggested. The proposed threshold of error is used to update the model reference parameters when it is necessary. Numerical analysis and simulation results show that the proposed metric validates its effectiveness in the performance of multivariate data reduction models in terms of the sensor node energy consumption. The adaptive threshold improves the frequency of updating the parameters by 80% and 52% in comparison to the non-adaptive threshold for multivariate data reduction models of MLR-B and PCA-B respectively.

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Investigation of an Indoor Air Quality Sensor for Asthma Management in Children

Cited by 38 publications

References 14 publications

Augmented personalized health: How smart data with IoTs and AI is about to change healthcare

Augmented personalized health: How smart data with IoTs and AI is about to change healthcare

Sensing Data Fusion for Enhanced Indoor Air Quality Monitoring

Performance Evaluation of Real-Time Multivariate Data Reduction Models for Adaptive-Threshold in Wireless Sensor Networks

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