Identifying the activity level is pivotal to take metabolic rate into account when assessing comfort in indoor environments. This work addresses two-fold aims. Firstly, the feasibility of employing multi-ultrasonic sensors on a multidomain monitoring platform for personalized comfort is investigated. Secondly, a data calibration and filtering methodology aimed at extracting features to detect office activities is proposed using temporal markers. A living environment for office use was considered and volunteer healthy subjects were monitored during 5 typical office activities. Results confirm the feasibility of integrating ultrasonic sensors in a monitoring platform to capture meaningful movement patterns to discern various office activities. In addition, results show that activity discrimination has an impact of 76% on the estimated Predictive Mean Vote (PMV) values. This information can be integrated in personal comfort models (PCMs) to optimize the occupants' well-being as well as thermoregulation of the built environment and, hence, the building energy consumption.

A Non-Intrusive Ultrasound-Based Sensing Technique for Activity Detection: Proof of Concept Towards Optimized Personalized Comfort

Ciuffreda, Ilaria
;
Cosoli, Gloria;Arnesano, Marco;
2024-01-01

Abstract

Identifying the activity level is pivotal to take metabolic rate into account when assessing comfort in indoor environments. This work addresses two-fold aims. Firstly, the feasibility of employing multi-ultrasonic sensors on a multidomain monitoring platform for personalized comfort is investigated. Secondly, a data calibration and filtering methodology aimed at extracting features to detect office activities is proposed using temporal markers. A living environment for office use was considered and volunteer healthy subjects were monitored during 5 typical office activities. Results confirm the feasibility of integrating ultrasonic sensors in a monitoring platform to capture meaningful movement patterns to discern various office activities. In addition, results show that activity discrimination has an impact of 76% on the estimated Predictive Mean Vote (PMV) values. This information can be integrated in personal comfort models (PCMs) to optimize the occupants' well-being as well as thermoregulation of the built environment and, hence, the building energy consumption.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11389/56875
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