Indoor abnormality detection method, system for same, and program

Abstract

Provided is an indoor abnormality detection method capable of remotely determining the physical condition of a resident or the like while protecting the privacy of the resident. The method includes: a first step for receiving a carbon dioxide concentration detected by a concentration sensor in a room; a second step for detecting a change in the concentration of carbon dioxide in the room, received in the first step; a third step for receiving a sensor detection result from a human sensor disposed at a prescribed location in the room; and a fourth step for determining the condition of a person in the room on the basis of the change in concentration detected in the second step and the sensor detection result received in the third step.

Description

Indoor abnormality detection method, system and program

[0001] The present invention relates to an indoor abnormality detection method, a system and a program therefor.

[0002] There is a system in place that installs cameras in the living spaces of elderly people to check whether they are alive or not.

[0003] Japanese Patent Application Laid-Open No. 2021-131832

[0004] However, as mentioned above, the use of cameras raises concerns about the infringement of residents' privacy. Residents have the right to live independently, and constant surveillance could violate that right. Furthermore, if the camera malfunctions or is hacked, there is a risk that personal information and information about their daily lives could be leaked to third parties. Furthermore, because elderly people should be able to move freely in their own spaces, the installation of cameras could restrict their activities. Given these factors, a method is needed to ensure the safety of elderly people while protecting their individual privacy and dignity.

[0005] The present invention has been made in consideration of the above circumstances, and its purpose is to provide an indoor abnormality detection method, system, and program that can remotely determine the physical condition of occupants while protecting the privacy of the occupants, etc.

[0006] The present invention is an indoor anomaly detection method in which a computer executes the following steps: a first step of receiving a carbon dioxide concentration detected by a concentration sensor in the room; a second step of detecting a change in the carbon dioxide concentration in the room received in the first step; a third step of receiving sensor detection results from a human presence sensor placed at a predetermined location in the room; a fourth step of generating behavioral pattern information indicating people entering and exiting the room and their movement patterns within the room based on the change in concentration over a predetermined period and the sensor detection results for the predetermined period; and a fifth step of determining the status of the person in the room based on the change in concentration detected in the second step and the sensor detection results received in the third step.The fifth step determines that an abnormality has occurred with the person if both a first condition is met: the detected carbon dioxide concentration has continuously been detected for a predetermined period of time to have fallen to a certain standard and the sensor detection results have not detected a person in the room for a certain period of time; and a second condition is met: the person is estimated to be at home during a time period in which a person is estimated to be in the room based on the behavioral pattern information.

[0007] Preferably, the fourth step generates the behavior pattern information based on the concentration change, the timing of the concentration change, a sensor detection result, and the timing of the change in the sensor detection result.

[0008] Preferably, the fourth step generates the behavioral pattern information based on the change in concentration of the carbon dioxide detected by a plurality of concentration sensors installed in a plurality of rooms within the same living space or company, the timing of the change in concentration, and the change in the detection results of the sensor detection results detected by a plurality of human presence sensors installed in a plurality of rooms within the same living space or company, and the timing of the change in detection results.

[0009] Preferably, the fifth step determines whether there is one or more people in the room based on the behavior pattern information and the carbon dioxide concentration detected by the concentration sensor, and determines that the abnormality has occurred if there is only one person in the room.

[0010] Preferably, a sixth step includes receiving a carbon monoxide concentration detected by a concentration sensor in the room, and determining whether a fire has occurred in the room based on the carbon monoxide concentration received in the sixth step.

[0011] The present invention is an indoor abnormality detection system in which a computer executes the following steps: a first step of receiving a carbon dioxide concentration detected by a concentration sensor in the room; a second step of detecting a change in the carbon dioxide concentration in the room received in the first step; a third step of receiving sensor detection results from a human presence sensor placed at a predetermined location in the room; a fourth step of generating behavioral pattern information indicating people entering and exiting the room and their movement patterns within the room based on the change in concentration over a predetermined period and the sensor detection results for the predetermined period; and a fifth step of determining the status of the person in the room based on the change in concentration detected in the second step and the sensor detection results received in the third step, wherein the fifth step determines that an abnormality has occurred with the person if both a first condition is detected for a predetermined period of time that the detected carbon dioxide concentration has fallen to a certain standard and the sensor detection results do not detect a person in the room for a certain period of time, and a second condition is that the fifth step is during an estimated time period in which a person is estimated to be at home based on the behavioral pattern information.

[0012] Preferably, the fourth step generates the behavior pattern information based on the concentration change, the timing of the concentration change, a sensor detection result, and the timing of the change in the sensor detection result.

[0013] The present invention is a program that causes a computer to execute the following steps: a first step of receiving a carbon dioxide concentration detected by a concentration sensor in a room; a second step of detecting a change in the carbon dioxide concentration in the room received in the first step; a third step of receiving sensor detection results from a human presence sensor placed at a predetermined location in the room; a fourth step of generating behavioral pattern information indicating people entering and exiting the room and their movement patterns within the room based on the change in concentration over a predetermined period of time and the sensor detection results for the predetermined period of time; and a fifth step of determining the status of a person in the room based on the change in concentration detected in the second step and the sensor detection results received in the third step.The fifth step is a program that determines that an abnormality has occurred in the person when both a first condition is detected for a predetermined period of time that the detected carbon dioxide concentration has fallen to a certain standard and the sensor detection results do not detect a person in the room for a certain period of time, and a second condition is that the fifth step is during an estimated time period when a person is estimated to be at home based on the behavioral pattern information.

[0014] Preferably, the fourth step generates the behavior pattern information based on the concentration change, the timing of the concentration change, a sensor detection result, and the timing of the change in the sensor detection result.

[0015] According to the present invention, it is possible to provide an indoor abnormality detection method, system and program that can remotely determine the physical condition of a resident while protecting the privacy of the resident or the like.

[0016] FIG. 1 is a system configuration diagram illustrating indoor abnormality detection according to a first embodiment of the present invention. FIG. 2 is a diagram illustrating a concentration sensor 13 and a human presence sensor 15 arranged in a room 12 of a monitored house. FIG. 3 is a functional block diagram of the indoor abnormality detection device 11 shown in FIG. 1. FIG. 4 is a graph illustrating the difference in carbon dioxide levels when a person is active. FIG. 5 is a graph illustrating the difference in carbon dioxide levels when a person is active. FIG. 6 is a graph illustrating the time progression of indoor carbon dioxide concentration and sensor detection results. FIG. 7 is a flowchart illustrating an example of operation of the indoor abnormality detection device 11 according to the first embodiment of the present invention. FIG. 8 is a flowchart illustrating the generation of behavior pattern data according to a second embodiment of the present invention. FIG. 9 is a flowchart illustrating an example of operation of the indoor abnormality detection device 11 according to the second embodiment of the present invention.

[0017] An indoor abnormality detection method according to an embodiment of the present invention will now be described. Fig. 1 is a system configuration diagram for explaining indoor abnormality detection according to an embodiment of the present invention. As shown in Fig. 1, in this embodiment, concentration sensors 13 (13_1 to 13_4, etc.) are arranged in rooms 12 (12_1 to 12_4, etc.) in a residential space or a business.

[0018] Human presence sensors 15 (15_1 to 15_3, etc.) are arranged in the room 12. The human presence sensors 15 have, for example, a detection angle of 110 degrees and a detection distance of approximately 5 m. The human presence sensors 15 are arranged along the path of movement of people in the room.

[0019] The indoor abnormality detection device 11 receives the carbon dioxide concentration detected by the concentration sensor 13, receives the sensor detection results from the human presence sensor 15, determines the abnormal condition of people in the room (described later), and sends a message to the relevant person terminal device 21 if necessary.

[0020] 2 is a diagram illustrating a concentration sensor 13 arranged in a room 12 of a house to be monitored. The concentration sensor 13 and the human presence sensor 15 are installed in a predetermined room 12 among a plurality of rooms 12 of the house or the like.

[0021] Fig. 3 is a functional block diagram of the indoor abnormality detection device 11 shown in Fig. 1. As shown in Fig. 2, the indoor abnormality detection device 11 includes, for example, an operation unit 53, a communication unit 55, a memory 59, and a processing unit 61.

[0022] The operation unit 53 is an operation means such as a touch panel, keyboard, or mouse. The communication unit 55 communicates with the concentration sensor 13 and the related party terminal device 21. The input unit 57 is a terminal or the like for inputting data from outside. The memory 59 stores a program to be executed by the processing unit 61. The processing unit 61 executes the program PRG stored in the memory 59 to perform the processing of the indoor abnormality detection device 11 defined in this embodiment.

[0023] The indoor abnormality detection device 11 receives carbon dioxide concentration data indicating the carbon dioxide concentration detected by the concentration sensor 13 from the concentration sensor 13 in the living space. The indoor abnormality detection device 11 detects the amount of change per unit time in the carbon dioxide concentration indicated by the received carbon dioxide concentration data. For example, the indoor abnormality detection device 11 detects the amount of change as shown in FIGS. 4 and 5 . The amount of change may be expressed as a percentage change. By using the amount of change in this way, changes in the occupant's condition can be identified with high accuracy.

[0024] The indoor abnormality detection device 11 receives, from the human presence sensor 15 in the living space, a human presence detection result detected by the human presence sensor 15. Fig. 6 is a diagram for explaining the waveform of the human presence detection result.

[0025] The indoor abnormality detection device 11 determines whether or not there is a person in the living space based on the detected amount of change and the human presence detection result.

[0026] The indoor abnormality detection device 11 determines whether a first condition is met, that is, after the carbon dioxide concentration of one of the concentration sensors 13_1 to 13_4 in one of the rooms 12_1 to 12_4 changes from a survival concentration to a non-survival concentration, the sensor detection results of the human presence sensors 15_1 to 15_3 do not detect a person for a certain period of time. When the first condition is met, the indoor abnormality detection device 11 determines that an abnormality has occurred with a person in the room. Here, the survival concentration is a concentration determined based on the results of detecting the carbon dioxide concentration when a person is alive in the rooms 12_1 to 12_4 for a predetermined period of time. The non-survival concentration is a concentration determined based on the results of detecting the carbon dioxide concentration when no person is alive in the rooms 12_1 to 12_4 for a predetermined period of time.

[0027] Based on the behavioral patterns and carbon dioxide concentrations identified above, the indoor abnormality detection device 11 determines whether there is one or multiple people in the residential area, and determines that an abnormality has occurred if there is only one person in the residential space.

[0028] In Figures 4 and 5, the difference in carbon dioxide (only the amount of change) when people are active is shown in graphs. When the line on the graph moves vigorously, people are in a state of movement. This state can also be understood from the carbon dioxide concentration. Based on the carbon dioxide concentration and its changes, the behavior of residents and the indoor air environment can be understood.

[0029] 6, (A) shows the carbon dioxide concentration, and (B) shows the time transition of the sensor detection result. The horizontal axis shows time, and the vertical axis shows the level. As shown in FIG. 6(B), the sensor detection result fluctuates greatly when a person is within the detection range of the human presence sensor 15.

[0030] 7 is a flowchart illustrating an example of the operation of the indoor abnormality detection device 11 according to the first embodiment of the present invention. Each step will be described.

[0031] Step ST11: The indoor abnormality detection device 11 receives carbon dioxide concentration data indicating the carbon dioxide concentrations detected by the concentration sensors 13_1 to 13_4 in the living space from the concentration sensors 13_1 to 13_4, and stores the data in the memory 59.

[0032] Step ST12: The indoor abnormality detection device 11 detects the amount of change per unit time of the carbon dioxide concentration data received in step ST11, and stores the amount of change in the memory 59. For example, the indoor abnormality detection device 11 detects the amount of change as shown in FIGS.

[0033] Step ST13: The indoor abnormality detection device 11 receives the sensor detection result detected by the human presence sensor 15 from the human presence sensor 15 in the living space.

[0034] Step ST14: The indoor abnormality detection device 11 determines whether or not a person is present in the living space based on at least one of the carbon dioxide concentration or its change and the sensor detection result. This determination may be made for each of the rooms 12_1 to 12_4. If the indoor abnormality detection device 11 determines that a person is present, it proceeds to step ST15.

[0035] Step ST15: The indoor abnormality detection device 11 determines whether a first condition, which means that an abnormality has occurred with respect to a person in the room, is met based on the concentration or change in the carbon dioxide concentration received from the concentration sensors 13_1 to 13_4 and the sensor detection results received from the human presence sensors 15_1 to 15_3. Specifically, the indoor abnormality detection device 11 determines that the first condition is met if the sensor detection results do not detect a person in the room for a certain period of time after the detected carbon dioxide concentration has fallen to a certain standard. The above-mentioned "detected carbon dioxide concentration has fallen to a certain standard" is determined based on the criterion that the carbon dioxide concentration has changed from a predetermined human survival concentration to a non-survival concentration within a certain period of time.

[0036] In this case, when an abnormal situation occurs with a person, the indoor carbon dioxide concentration is monitored to determine whether it has changed by a predetermined amount over time. The indoor abnormality detection device 11 may determine that the first condition is met when the detected carbon dioxide concentration continues to fall below a certain standard for a predetermined period of time and the sensor detection result does not detect a person in the room for a certain period of time. The predetermined period and the certain period of time are determined in advance based on past actual measured values. Alternatively, the indoor abnormality detection device 11 may update the indoor carbon dioxide concentration based on the actual measured values ​​for a certain period of time.

[0037] Step ST16: The indoor abnormality detection device 11 notifies the related person terminal device 21 of the related person of the resident that an abnormality has occurred in the resident.

[0038] As described above, the indoor abnormality detection device 11 does not require the installation of a camera in the room 12, but instead installs the concentration sensor 13 and the human presence sensor 15, and can determine the state of the occupant in the room 12 using the carbon dioxide concentration detected by the concentration sensor 13 and the sensor detection result detected by the human presence sensor 15. This makes it possible to protect the privacy of the occupant.

[0039] Second Embodiment

[0040] In this embodiment, in addition to the first condition in the first embodiment described above, if the second condition is met, that is, if it is an estimated time period during which people are estimated to be at home based on information about the residents' behavior patterns, it is determined that an abnormality has occurred. This improves the accuracy of the determination.

[0041] 8 is a flowchart for explaining how behavior pattern data is generated. Each step will be explained below. Step ST21: The indoor abnormality detection device 11 reads out the amount of change in carbon dioxide for a certain period (e.g., one week to one month) stored in the memory 59 in step ST12 shown in FIG.

[0042] Step ST22: The indoor abnormality detection device 11 reads out the sensor detection results for the above-mentioned fixed period stored in the memory 59 in step ST13.

[0043] Step ST23: The communication terminal device 11 generates behavior pattern information of the resident based on the concentration changes read out in steps ST21 and ST22, the timing of the concentration changes, the sensor detection results, and the timing of the changes in the sensor detection results. If concentration sensors 13 and human presence sensors 15 are installed in multiple rooms, a pattern is identified for each room, and overall behavior pattern information is generated based on the patterns. The behavior pattern information identifies the time periods when the resident (person) is present in the rooms 12_1 to 12_4, the time periods when the resident is not present, the movement patterns between rooms, etc. This identifies an estimated at-home time period during which the resident is estimated to be in the living space. The estimated at-home time period is, for example, a time period during which the resident is present in the living space or a specific room 12_1 to 12_4 with a predetermined probability (e.g., 90%).

[0044] 9 is a flowchart illustrating an example of the operation of the indoor abnormality detection device 11 according to the second embodiment of the present invention. Each step will be described.

[0045] Steps ST11, ST12, ST15, and ST16 are the same as the steps in the first embodiment described with reference to FIG.

[0046] Step ST33 will now be described. Step ST33 is performed as follows: If the indoor abnormality detection device 11 satisfies the second condition that the estimated time period is one in which someone is estimated to be at home based on the behavior pattern information generated in step ST23 described above, the indoor abnormality detection device 11 proceeds to step ST15.

[0047] According to the second embodiment, resident behavior pattern information is generated in advance and is further used to notify an abnormality occurrence message, so that notification can be made based on a more accurate situation assessment.

[0048] The present invention is not limited to the above-described embodiments, and those skilled in the art may make various modifications, combinations, subcombinations, and substitutions of the components of the above-described embodiments within the technical scope of the present invention or equivalents thereof.

[0049] In addition to the functions of the first and second embodiments described above, if the carbon dioxide concentration indicates that there is someone in the room, but the sensor detection results do not detect a person during the time period when the person should have started to behave based on the behavioral pattern information, a low-level warning may be sent to relevant parties.

[0050] In the above-described embodiment, a residential space is exemplified, but the present invention may also be applied to other spaces such as a workplace in a company, a hospital ward, or a facility.

[0051] The temperature and humidity of the room 12 are also affected by whether or not there is someone in the room 12. It can also determine whether the occupant has intentionally turned on the air conditioning, allowing for vital signs to be monitored. As the number of people increases, humidity also rises, and there are also changes due to seasonal factors, and by detecting these, the state of the room 12 can be grasped. Ultimately, the indoor abnormality detection device 11 comprehensively utilizes the above information to individually compile data, making it possible to detect whether people are at home or not, and to read behavioral patterns.

[0052] For example, the behavioral patterns of an elderly person living alone in an apartment are the bedroom, living room, and going out (excluding the toilet and bathroom). By installing the concentration sensors 13 in the living room and bedroom, it is possible to check the safety and lifestyle patterns of the elderly person without the camera observing their private life.

[0053] The indoor abnormality detection device 11 may also receive the carbon monoxide concentration detected by the indoor concentration sensor and determine whether a fire has occurred in the room based on the carbon monoxide concentration. It also functions as a safety function against carbon monoxide poisoning caused by forgetting to turn off the stove or by using a space heater in cold regions. If a fire occurs, significant changes in CO, CO2, and temperature will occur immediately.

[0054] The present invention is applicable to an indoor anomaly detection system.

[0055] 11...Specification verification system 12...Indoor 13...Carbon dioxide concentration sensor 15...Human presence sensor 21...Stakeholder terminal device

Claims

a third step of receiving sensor detection results from a human presence sensor placed at a predetermined location in the room; a fourth step of generating behavioral pattern information indicating people entering and exiting the room and their movement patterns within the room based on the concentration change over a predetermined period and the sensor detection results over a predetermined period; and a fifth step of determining the status of the person in the room based on the concentration change detected in the second step and the sensor detection results received in the third step, wherein the fifth step determines that an abnormality has occurred with the person if both of the following conditions are met: a first condition that the detected carbon dioxide concentration has continuously been detected for a predetermined period of time to have fallen to a certain standard and the sensor detection results have not detected anyone in the room for a certain period of time; and a second condition that the person is within an estimated time period when it is estimated that someone is at home based on the behavioral pattern information.

2. The indoor abnormality detection method described in claim 1, wherein the fourth step generates the behavior pattern information based on the concentration change, the timing of the concentration change, the sensor detection result, and the timing of the change in the sensor detection result.

3. The indoor abnormality detection method described in claim 2, wherein the fourth step generates the behavior pattern information based on the change in concentration of the carbon dioxide detected by a plurality of concentration sensors installed in a plurality of rooms in the same living space or company, and the timing of the change in concentration, and the change in the sensor detection results detected by a plurality of human presence sensors installed in a plurality of rooms in the same living space or company, and the timing of the change in detection results.

4. The indoor abnormality detection method according to claim 3, wherein the fifth step determines whether there is one person or multiple people in the room based on the behavior pattern information and the carbon dioxide concentration detected by the concentration sensor, and determines that the abnormality has occurred if there is only one person in the room.

5. The indoor abnormality detection method according to claim 4, further comprising a sixth step of receiving a carbon monoxide concentration detected by a concentration sensor in the room, and determining whether a fire has occurred in the room based on the carbon monoxide concentration received in the sixth step.

6. An indoor abnormality detection system in which a computer executes the following steps: a first step of receiving a carbon dioxide concentration detected by a concentration sensor in the room; a second step of detecting a change in the carbon dioxide concentration in the room received in the first step; a third step of receiving sensor detection results from a human presence sensor placed at a predetermined location in the room; a fourth step of generating behavior pattern information indicating people entering and exiting the room and their movement patterns within the room based on the change in concentration over a predetermined period and the sensor detection results for the predetermined period; and a fifth step of determining the status of the person in the room based on the change in concentration detected in the second step and the sensor detection results received in the third step, wherein the fifth step determines that an abnormality has occurred with the person when both a first condition is met that the detected carbon dioxide concentration has continuously detected a state where it has fallen to a certain standard for a predetermined period of time and the sensor detection results do not detect a person in the room for a certain period of time, and a second condition that the person is estimated to be at home during a time period in which a person is estimated to be in the room based on the behavior pattern information.

7. The indoor abnormality detection method described in claim 6, wherein the fourth step generates the behavior pattern information based on the concentration change, the timing of the concentration change, the sensor detection result, and the timing of the change in the sensor detection result.

8. A program that causes a computer to execute the following steps: a first step of receiving a carbon dioxide concentration detected by a concentration sensor in a room; a second step of detecting a change in the carbon dioxide concentration in the room received in the first step; a third step of receiving sensor detection results from a human presence sensor placed at a predetermined location in the room; a fourth step of generating behavioral pattern information indicating people entering and exiting the room and their movement patterns within the room based on the change in concentration over a predetermined period of time and the sensor detection results for the predetermined period of time; and a fifth step of determining the status of a person in the room based on the change in concentration detected in the second step and the sensor detection results received in the third step, wherein the fifth step is a program that determines that an abnormality has occurred in the person when both a first condition is met: that the detected carbon dioxide concentration has continuously detected a state where it has fallen to a certain standard for a predetermined period of time and the sensor detection results do not detect a person in the room for a certain period of time, and a second condition is that the person is in an estimated time period where a person is at home and is estimated to be in the room based on the behavioral pattern information.

9. The program according to claim 8, wherein the fourth step generates the behavior pattern information based on the concentration change, the timing of the concentration change, the sensor detection result, and the timing of the change in the sensor detection result.

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