This paper proposes an activity monitoring system for occupants at home, using a CO₂ sensor, which is a low privacy violation. The proposed system has two features.

1) The proposed system estimates the activity of occupants indoors based on changes in CO₂ concentration, which increases or decreases with occupants’ exhalation, the opening and closing of doors and windows, and the operation state of mechanical ventilation in the room. The system estimates the date and time a person stays indoors and detects the occupant’s safety daily.

2) The IoT sensor node, including CO₂ sensors, can be implemented at a low cost of about $20 U.S. The calibration of the CO₂ sensor is set to simply 400 ppm based on outdoor ambient air; the relative value of CO₂ concentration after calibration is sufficient for this system to estimate the occupants’ daily activities.

Figure 1 shows an overview of the proposed CO₂ sensor-based activity monitoring system. The IoT sensor node (Figure 1(b) and Figure 2) is mounted at a height of 1.5 meters on a wall of the indoor room to be measured at least 1 meter away from the door or window. The IoT sensor node is powered from a commercial outlet in its vicinity. Data to the cloud storage (Figure 1(d)) is transmitted via an indoor Wi-Fi router (Figure 1(c)). This system used Ambient, a cloud storage service offered in Japan. The interval of transmission to the cloud storage was set to 30 seconds. The stored CO₂ concentration can be monitored in real-time by a remote observer (Figure 1(e)). In this study, the CO₂ concentration stored in the cloud storage is used to determine the presence or absence of indoor residents, to confirm their safety, and to estimate their daily activities.

A block diagram of the IoT sensor node with a built-in CO₂ sensor is shown in Figure 2. The CO₂ sensor is a Non-Dispersive InfraRed (NDIR) type MH-Z19C

manufactured by Zhengzhou Winsen Electronics Technology Co., Ltd. The DC-DC converter stabilizes the power supply of the IoT sensor node. The embedded microcontroller and development environment are ESP32 with a built-in Wi-Fi module manufactured by Espressif Systems and Arduino IDE 1.8.x, respectively.

Table 1 summarizes the three indoor rooms (Room A, B, and C) in which the CO₂ concentration is observed, showing the room’s purpose, room size, number of occupants, and main activities of daily living. Figure 3 shows the layout of Room A (laboratory) and the location of the CO₂ sensor, and Figure 4 presents a photograph of Room B (office) showing the location of the CO₂ sensor, portable stove, and window. Figure 5 shows examples of CO₂ measurements in three rooms (Room A, B, and C) over 48 hours.

Figure 5(a) shows an example of a 46-square-meter Room A (laboratory) with seven adults in a meeting from 1 p.m. to 4 p.m. on the first day and eight adults in a meeting from 9 a.m. to 12 p.m. on the second day. The CO₂ concentration in Room A increased after the start of the meeting and decreased slowly after the end of the meeting, depending on the exhalations of the residents, the

opening and closing of doors and windows, and the operation of the mechanical ventilation system.

Figure 5(b) shows an observation example in Room B (office), a 45-square-meter room facing Room A. One resident stayed in this room from 10 a.m. to 6 p.m. on weekdays. The mechanical ventilation is in good operating condition, and there is no significant increase or decrease of CO₂ concentration due to the exhalation of the occupant; when the portable heater was used to heat water in Room B, the CO₂ concentration increased or decreased in a short period time (about 5 to 15 minutes).

Figure 5(c) shows an example observation of one occupant in an 8 square meter in Room C [bedroom] of his house. During sleep, the bedroom window and door were closed, and the CO₂ concentration increased 30 minutes after bed. Upon waking the next morning, the CO₂ concentration in the closed bedroom was approximately 1000 ppm. Five minutes after opening the bedroom door, the CO₂ concentration decreased to 400 ppm.

Although the measurement results in Figure 5 are only three examples, it is possible to infer whether people are staying or not and their daily activities from the measurement of CO₂ concentrations in indoor residences. In this paper, indoor safety confirmation should be able to measure at least one detection of daily living activities associated with changes in CO₂ concentration throughout the day. The safety confirmation by the proposed system is assumed to be home if the daily activities can be detected and absent or unknown if they cannot be detected.

To evaluate the usefulness of the proposed system, the CO₂ concentrations in the three rooms shown in Table 1 are measured. The observation period was 35 days, from July 1 to August 4, 2023. The CO₂ concentrations in the three rooms were graphed from logs stored every 30 seconds for up to 35 days in the cloud storage. The hourly CO₂ concentration visualized by the heat maps was used to estimate the date and time of the resident’s stay, detect safety daily, and estimate daily activities.

Figures 6(a)-(c) present the results of CO₂ measurements in Room A, Room B, and Room C, respectively. There were no missing periods during the observation period. The CO₂ concentration was approximately 400 ppm when the residents were not present or when the rooms were well-ventilated, and sometimes exceeded 1000 ppm when there were stays in the rooms.

Figure 7 shows the CO₂ concentrations averaged hourly and displayed as heat maps; Figure 7(a) is a heat map of Room A. The CO₂ concentration was 800 ppm on Tuesday afternoon and Wednesday morning when a meeting was being held, and the CO₂ concentration gradually decreased to 400 ppm after the meeting. Figure 7(b) shows the heat map of Room B. Since the residence was well-ventilated and only one resident was staying in the 45-square-meter room,

the CO₂ concentration was 400 ppm, the same as the outside air, except for some periods. Figure 7(c) shows the heat map of Room C. There were many periods when the concentration exceeded 800 ppm during sleep.

Figure 8 shows a maximum hourly CO₂ concentration heat map. Figure 8(b) shows the behavior observed in Room B from 9:00 to 14:00, where a portable stove was used to boil water. Figure 9 and Figure 10 show the mean and maximum CO₂ concentrations for each day of the week for a maximum measurement period of 35 days in hourly and heatmap units, respectively.

In Room A, the presence or absence of undergraduate students can be inferred from the heat maps in Figure 9(a) and Figure 10(a). From the heatmap display, it can be assumed that occupants stayed during the weekday daytime and were absent during the weekday nighttime and holidays. The living behavior can be read as having regular Tuesday afternoon and Wednesday morning meetings.

In Room B, the heat maps in Figure 9(b) and Figure 10(b) show that one resident stayed during the daytime on weekdays. In Room C, the heat maps in Figure 9(c) and Figure10(c) show that an occupant was absent during the daytime on weekdays, stayed at nighttime for sleeping, and stayed on weekends. On the other hand, it can be inferred that the occupant do not remain during weekday afternoons.

From the heat map display of CO₂ concentrations in the three rooms shown in Figures 7-10, the presence or absence of residents, ventilation conditions in each residence, and living behaviors were inferred.

The CO₂ concentration increased with the exhalation of residents and the use of portable gas stoves and decreased rapidly with the release of doors and windows. When there are many residents in a living room, it is easy to infer living behavior because the CO₂ concentration tends to increase when the residents are not adequately ventilated. For example, suppose the doors and windows of a residence are closed, and air conditioning is used in summer and winter. In that case, behaviors such as using gas heaters in winter and periodically opening windows for ventilation are easy to infer from the change in CO₂ concentration.

In the report [5] , machine learning was used to estimate ten scenes of daily activities, including TV watching, eating, bathing, and sleeping, by combining measurements from three types of sensors. In this study, we believe that if machine learning is applied to the proposed system, it will be possible to automate the estimation of three lifestyle behaviors, such as whether residents stay in their rooms based on CO₂ concentration measurements, ventilation such as opening and closing doors and windows, and the use of heaters and portable gas stoves.

The proposed system estimated the presence or absence of each resident, the ventilation status of the residence, and the living behavior from a heatmap display of CO₂ concentration using an hourly statistical process as a simple method

that does not require machine learning. The results of the CO₂ concentration measurements were used to estimate the living behavior of the residents. In addition, using a portable stove to boil water was shown as an estimation of daily activities for less than one hour. Gas appliances are widely used for cooking in ordinary households. For cooking behavior estimation, the temporary increase in CO₂ concentration due to using gas appliances can be visualized as a heat map of the maximum value by statistical processing. The proposed system can estimate more lifestyle behaviors.