Bianchi, V., Bassoli, M., Lombardo, G., Fornacciari, P. and Mordonini, M. (2019) IoT Wearable Sensor and Deep Learning: An Integrated Approach for Personalized Human Activity Recognition in a Smart Home Environment. IEEE Internet of Things Journal, 6, 8553-8562.
https://doi.org/10.1109/JIOT.2019.2920283

has been cited by the following article:

• TITLE: An Activity Recognition System at Home Based on CO2 Sensors

  AUTHORS: Hiroyuki Matsubara

  KEYWORDS: Activity Recognition, CO2 Sensor, Internet of Things (IoT), Low Privacy-Intrusion, Heat Map

  JOURNAL NAME: Journal of Computer and Communications, Vol.11 No.11, November 24, 2023

  ABSTRACT: Activity recognition of indoor occupants using indirect sensing with less privacy violation is one of the hot research topics. This paper proposes a CO2 sensor-based indoor occupant activity monitoring system. Using the IoT sensor node that contains CO2 sensors, the measured CO2 concentrations in three locations (laboratory, office, and bedroom) were stored in a cloud server for up to 35 days starting July 1, 2023. The CO2 measurements stored at 30-second intervals were statistically processed to produce a heat-mapped display of the hourly average or maximum CO2 concentration. From the heatmap visualizations of CO2 concentration, the proposed system estimated meeting, heating water using a portable stove, and sleep for the occupants’ activity recognition.