Body Temperature—Indoor Condition Monitor and Activity Recognition by MEMS Accelerometer Based on IoT-Alert System for People in Quarantine Due to COVID-19

Hoang, Minh Long; Carratù, Marco; Paciello, Vincenzo; Pietrosanto, Antonio

doi:10.3390/s21072313

Cited by 52 publications

(23 citation statements)

References 29 publications

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“…In terms of the observation operation, sensors exist that enable real knowledge of regular occupant actions, as well as others that use the collected parameters to infer occupant behaviors [14]. Sensors that detect occupant activities directly can be employed alone [15] or in conjunction with modeling and simulation [16]. In-situ monitoring campaigns, on the other hand, are difficult tasks that necessitate a high level of expertise to optimize researcher decisions at various levels [17], including sensor types [16], sensor technologies [18], ideal placements [19], observing promotional duration and data collection time step [20].…”

Section: Related Workmentioning

confidence: 99%

Detecting occupancy behaviour of buildings through environment monitoring sensing

Yahaya¹,

Usman²,

Uwa³

2022

World J. Adv. Res. Rev.

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The goal of this research is to produce a realistic occupancy forecast as well as an ideal occupancy-based controller for increasing the performance of Heating Ventilation and Air-Conditioning [HVAC] systems. Reliable occupancy prediction is a critical enabler for demand-based HVAC control since it ensures that the HVAC system is not running unnecessarily while a room or zone is empty. We present simple yet successful occupancy prediction methods, as well as an approach for automatically setting temperature set-points, in this work. We present three alternative occupancy prediction algorithms based on historical occupancy measurements. The proposed approach uses historical occupancy profile and environmental room data to establish an occupancy identity strategy that can provide a stochastic model based on uncertain basis functions as an alternative. According to the findings, the proposed occupancy prediction algorithms could reach an accuracy of approximately 85% reliable prediction with few negative predictions.

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Section: Related Workmentioning

confidence: 99%

Detecting occupancy behaviour of buildings through environment monitoring sensing

Yahaya¹,

Usman²,

Uwa³

2022

World J. Adv. Res. Rev.

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“…The use of LPWAN devices coupled with machine learning would help in realizing such an IoT application. One of the critical vital signs is the body temperature [59], which needs regular monitoring for critically ill patients [60]. TelosB mote is used in conjunction with a temperature sensor in [48] for monitoring body temperature periodically.…”

Section: Temperature Monitoringmentioning

confidence: 99%

Machine Learning and LPWAN Based Internet of Things Applications in Healthcare Sector during COVID-19 Pandemic

2021

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Low power wide area networks (LPWAN) are comprised of small devices having restricted processing resources and limited energy budget. These devices are connected with each other using communication protocols. Considering their available resources, these devices can be used in a number of different Internet of Things (IoT) applications. Another interesting paradigm is machine learning, which can also be integrated with LPWAN technology to embed intelligence into these IoT applications. These machine learning-based applications combine intelligence with LPWAN and prove to be a useful tool. One such IoT application is in the medical field, where they can be used to provide multiple services. In the scenario of the COVID-19 pandemic, the importance of LPWAN-based medical services has gained particular attention. This article describes various COVID-19-related healthcare services, using the the applications of machine learning and LPWAN in improving the medical domain during the current COVID-19 pandemic. We validate our idea with the help of a case study that describes a way to reduce the spread of any pandemic using LPWAN technology and machine learning. The case study compares k-Nearest Neighbors (KNN) and trust-based algorithms for mitigating the flow of virus spread. The simulation results show the effectiveness of KNN for curtailing the COVID-19 spread.

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“…Wearables approaches are proposed in [10,24,46,51,52,53,54,55,56,57,58,59] to obtain occupancy information as a product of tasks completed by other systems which can be used to track the occupancy location. ML model can obtain signal intensity from statically positioned beacons in a target space to obtain a fine-grained occupant location and achieve the location accuracy of five meters.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The study adopted a similar occupancy prediction classification used in [3] to categorizes reviewed occupancy prediction approaches using various technologies as described in Table 1. The environmental variables sensing approach is proposed in [13,14,15,16,21,24,25,26,27,28,29,30,31,32,33] to predict room occupation by measuring the variation of indoor parameters. The sensor modality adopted in these studies can be extended in many indoor sensing applications, including multi-sensing technology to observe concentrations of volatile organic compounds in the air, wireless sensor network technology for monitoring indoor air quality, and smart climate technology for weather forecast.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative Analysis of Machine Learning Methods for Non-Intrusive Indoor Occupancy Detection and Estimation

Aliero¹,

Pasha²,

Toosi³

et al. 2021

Preprint

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Occupancy-driven application research has been active research for a decade that focuses on improving or replacing new building infrastructure to improve building energy efficiency. Existing approaches for HVAC energy saving are putting more emphasis on occupancy detection, estimation, and localization to trade-off between energy consumption and thermal comfort satisfaction. In a non-intrusive approach, various sensors, actuators, and analytic data methods are commonly used to process data from occupant surroundings and trigger appropriate action to achieve the task. However, the performance of the non-intrusive approach reported in the literature is relatively poor due to the lack of quality of dataset used in model training and expropriate choice of machine learning model. This study proposed a non-intrusive approach that to improve the collection and quality of dataset using data pre-processing. The study collected a training dataset using various sensors installed in the building and developed a model using five machine learning models to determine occupant’s presence and estimate their number in the building. The proposed solution is tested in the living room with a prototype system integrated with various sensors designed to obtain occupant surrounding environmental datasets. The model’s prediction results obtained indicate that it is possible for the proposed solution to obtain data, process, and predict the occupant number with high accuracy (73.6 -99.7% using random forest).

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Body Temperature—Indoor Condition Monitor and Activity Recognition by MEMS Accelerometer Based on IoT-Alert System for People in Quarantine Due to COVID-19

Cited by 52 publications

References 29 publications

Detecting occupancy behaviour of buildings through environment monitoring sensing

Detecting occupancy behaviour of buildings through environment monitoring sensing

Machine Learning and LPWAN Based Internet of Things Applications in Healthcare Sector during COVID-19 Pandemic

Comparative Analysis of Machine Learning Methods for Non-Intrusive Indoor Occupancy Detection and Estimation

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