Waste identification method, waste identification device, and waste identification program

1 . An excreta identification method performed by a computer, the method comprising:

acquiring sound data collected by a microphone arranged in a toilet;

identifying which of defecation, urination, flatulating, and the generation of an environmental sound in the toilet has occurred from the acquired sound data by inputting as an input value the acquired sound data into an identification model that has been subjected to machine learning,

in which sound data including any of a defecation sound, a urination sound, a flatulating sound, and the environmental sound in the toilet is input into the identification model during the machine learning as an input value, and

in which an output value of the identification model during the machine learning identifies which of the defecation, the urination, the flatulating, and the generation of the environmental sound in the toilet has occurred; and

outputting an identification result as an output value of the identification model, identifying which of the defecation, the urination, the flatulating, and the generation of the environmental sound in the toilet has occurred, wherein

the identification model correlates the urination sound with the speed at which the urine is excreted, and classifies input urination sounds into plural sound levels, each sound level corresponding to an excretion speed of a urine stream, wherein the identification model is subjected to machine learning

in which each of the plural sound levels of the urination sounds is input as an input value to the identification model during machine learning and each input urination sound level is correlated with a corresponding urination excretion speed, and

in which an output value of identification model during machine learning is the excretion speed associated with a corresponding input urination sound level, and

in the identifying operation the identification model identifies the sound level of the urination sound in the acquired sound data in the event a urination sound is present in the acquired sound data, the method further comprising:

measuring the duration of continuous urination based on the duration of the urination sound; and

calculating a voided volume of urine voided during urination using the identified sound level of urination, the urination excretion speed corresponding to the identified sound level, and the measured duration.

2 . The excreta identification method according to claim 1 , wherein

the identification model correlates the state of feces excreted during the defecation with a defecation sound produced during the defecation,

the identification model is subjected to machine learning in which the input value input into the identification model during machine learning is sound data representing the defecation sound that the identification model correlates with the state of feces, and in which the state of feces is an output value, and

in the identification operation, the state of feces is identified by inputting the acquired sound data into the identification model.

3 . An excreta identification method performed by a computer, the method comprising:

acquiring sound data collected by a microphone arranged in a toilet;

identifying which of defecation, urination, flatulating, and the generation of an environmental sound in the toilet has occurred from the acquired sound data by inputting as an input value the acquired sound data into an identification model that has been subjected to machine learning,

in which sound data including any of a defecation sound, a urination sound, a flatulating sound, and the environmental sound in the toilet is input into the identification model during the machine learning as an input value, and

in which an output value of the identification model during the machine learning identifies which of the defecation, the urination, the flatulating, and the generation of the environmental sound in the toilet has occurred; and

outputting an identification result as an output value of the identification model, identifying which of the defecation, the urination, the flatulating, and the generation of the environmental sound in the toilet has occurred, wherein

the identification model is subjected to machine learning in which sound data of the sound of water splashing on the microphone during the urination, the defecation, the flatulating, or the generation of the environmental sound is an input value input into the identification model, and in which the output value represents the state of the water splashing on the microphone, and

in the identification operation, the identification model determines that water is splashed on the microphone by inputting the acquired sound data to the identification model.

4 . The excreta identification method according to claim 1 , wherein the acquiring operation acquires sound data of sounds generated from the time one who excretes feces, urine, or flatulence sits on a toilet seat to the time when the one who excretes ends their sitting on the toilet seat.

5 . The excreta identification method according to claim 1 , wherein the microphone is arranged inside a toilet bowl.

6 . An excreta identification device comprising:

an acquisition unit that acquires sound data collected by a microphone arranged in a toilet;

an identification unit that identifies which of defecation, urination, flatulating, and the generation of an environmental sound in the toilet has occurred from the acquired sound data by inputting as an input value the acquired sound data into an identification model that has been subjected to machine learning

in which sound data including any of a defecation sound, a urination sound, a flatulating sound, and the environmental sound in the toilet is input into the identification model during the machine learning as an input value, and

in which an output value of the identification model during the machine learning identifies which of the defecation, the urination, the flatulating, and the generation of the environmental sound in the toilet has occurred; and

an output unit that outputs an identification result as an output value of the identification model, identifying which of the defecation, the urination, the flatulating, and the generation of the environmental sound in the toilet has occurred, wherein

the identification model correlates the urination sound with the speed at which the urine is excreted, and classifies input urination sounds into plural sound levels, each sound level corresponding to an excretion speed of a urine stream, wherein the identification model is subjected to machine learning

in which each of the plural sound levels of the urination sounds is input as an input value to the identification model during machine learning and each input urination sound level is correlated with a corresponding urination excretion speed, and

in which an output value of identification model during machine learning is the excretion speed associated with a corresponding input urination sound level, and

in the identifying operation the identification model identifies the sound level of the urination sound in the acquired sound data in the event a urination sound is present in the acquired sound data,

the device further comprising:

a measuring unit that measures the duration of continuous urination based on the duration of the urination sound; and

a calculation unit that calculates a voided volume of urine voided during urination using the identified sound level of urination, the urination excretion speed corresponding to the identified sound level, and the measured duration.

7 . A non-transitory computer readable recording medium storing an excreta identification program causing a computer to function to:

acquire sound data collected by a microphone arranged in a toilet;

identify which of defecation, urination, flatulating, and the generation of an environmental sound in the toilet has occurred from the acquired sound data by inputting as an input value the acquired sound data into an identification model that has been subjected to machine learning

in which sound data including any of a defecation sound, a urination sound, a flatulating sound, and the environmental sound in the toilet is input into the identification model during the machine learning as an input value, and

in which an output value of the identification model during the machine learning identifies which of the defecation, the urination, the flatulating, and the generation of the environmental sound in the toilet has occurred; and

output an identification result as an output value of the identification model, identifying which of the defecation, the urination, the flatulating, and the generation of the environmental sound in the toilet has occurred, wherein

the identification model correlates the urination sound with the speed at which the urine is excreted, and classifies input urination sounds into plural sound levels, each sound level corresponding to an excretion speed of a urine stream, wherein the identification model is subjected to machine learning

in which each of the plural sound levels of the urination sounds is input as an input value to the identification model during machine learning and each input urination sound level is correlated with a corresponding urination excretion speed, and

in which an output value of identification model during machine learning is the excretion speed associated with a corresponding input urination sound level, and

in the identifying operation the identification model identifies the sound level of the urination sound in the acquired sound data in the event a urination sound is present in the acquired sound data,

the program further causing the computer to function to:

measure the duration of continuous urination based on the duration of the urination sound; and

calculate a voided volume of urine voided during urination using the identified sound level of urination, the urination excretion speed corresponding to the identified sound level, and the measured duration.