Companion animal device and operating method thereof

Abstract

The present invention relates to a companion animal device and an operating method thereof. The companion animal device comprises: a weight detection sensor for detecting the weight of a companion animal; and a signal processing device for processing signals from the weight detection sensor. The signal processing device may: acquire a plurality of datasets on the basis of the signals from the weight detection sensor; calculate, for each of the plurality of datasets, a first parameter corresponding to the respiration of the companion animal and a second parameter corresponding to the heart rate of the companion animal; determine a first dataset among the plurality of datasets on the basis of the first parameter; and determine, from the first dataset on the basis of the second parameter, a second dataset used for determining the state of the companion animal, wherein each of the plurality of datasets may include first data corresponding to the respiration of the companion animal and second data corresponding to the heart rate of the companion animal.

Description

Device for pets and method of operation thereof

[0001] The present disclosure relates to a device for a pet and a method of operating the same, and more specifically, to a device for a pet capable of monitoring the condition of a pet and a method of operating the same.

[0002] As the number of households raising pets continues to increase, active research is being conducted on products designed to promote pet health and enhance the convenience of pet care. For example, various devices are being developed, such as waste disposal systems and water dispensers that always provide clean water to pets.

[0003] Unlike humans, pets cannot communicate directly with their owners, making it difficult for owners to easily check their health status. Regularly visiting a veterinary clinic to monitor a pet's health can be a burden for owners in terms of both time and cost. Furthermore, in cases of health problems that are difficult to detect visually, the pet's health may have already deteriorated by the time the owner notices an issue.

[0004] The present disclosure aims to solve the aforementioned problems and other problems.

[0005] Another objective is to provide a device for pets and a method of operation thereof that can monitor the condition of a pet using the weight of the pet.

[0006] Another objective is to provide a device for pets and a method of operation thereof that can improve the accuracy of monitoring the condition of pets by utilizing various parameters.

[0007] Another objective is to provide a device for pets and a method of operation thereof that can provide information related to the condition of the pet to the guardian.

[0008] A device for a pet according to one embodiment of the present disclosure for achieving the above objective includes a weight sensing sensor for detecting the weight of a pet and a signal processing device for processing a signal from the weight sensing sensor, wherein the signal processing device acquires a plurality of data sets based on the signal from the weight sensing sensor, calculates a first parameter corresponding to the respiration of the pet and a second parameter corresponding to the heart rate of the pet for each of the plurality of data sets, determines a first data set among the plurality of data sets based on the first parameter, and determines a second data set among the first data sets used for determining the state of the pet based on the second parameter, and each of the plurality of data sets may include first data corresponding to the respiration of the pet and second data corresponding to the heart rate of the pet.

[0009] A method of operation for a pet device according to one embodiment of the present disclosure for achieving the above objective comprises, in the method of operation for a pet device, acquiring a plurality of data sets based on a signal from a weight sensing sensor that detects the weight of the pet; calculating a first parameter corresponding to the respiration of the pet for each of the plurality of data sets; calculating a second parameter corresponding to the heart rate of the pet for each of the plurality of data sets; determining a first data set among the plurality of data sets based on the first parameter; and determining a second data set among the first data sets used for determining the state of the pet based on the second parameter, wherein each of the plurality of data sets may include first data corresponding to the respiration of the pet and second data corresponding to the heart rate of the pet.

[0010] The effects of the device for pets and the method of operation thereof according to the present disclosure are described as follows.

[0011] According to at least one embodiment of the present disclosure, the condition of a pet can be monitored using the weight of the pet.

[0012] According to at least one embodiment of the present disclosure, the accuracy of monitoring the condition of a pet can be improved by using various parameters.

[0013] According to at least one embodiment of the present disclosure, information related to the condition of a pet can be provided to a guardian.

[0014] FIGS. 1a and FIGS. 1b are drawings referenced in the description of a device for pets according to one embodiment of the present disclosure.

[0015] FIG. 2 is an internal block diagram of a device for pets according to one embodiment of the present disclosure.

[0016] Figure 3 is an example of an internal block diagram of the signal processing device of Figure 2.

[0017] FIG. 4 is a drawing referenced in the description of a respiration signal and a heart rate signal according to one embodiment of the present disclosure.

[0018] FIGS. 5a and 5b are drawings referenced in the description of a configuration disposed inside a device for pets according to one embodiment of the present disclosure.

[0019] FIG. 6 is a flowchart of a method of operation for a pet device according to one embodiment of the present disclosure.

[0020] FIGS. 7 to 13 are drawings referenced in the description of the operation of a device for pets according to an embodiment of the present disclosure.

[0021] The present disclosure is described in detail below with reference to the drawings. In the drawings, parts unrelated to the description have been omitted to provide a clear and concise explanation of the present disclosure, and the same reference numerals are used throughout the specification for identical or extremely similar parts.

[0022] The suffixes "module" and "part" for components used in the following description are assigned solely for the ease of drafting this specification and do not inherently confer any particularly significant meaning or role. Accordingly, the terms "module" and "part" may be used interchangeably.

[0023] In this application, terms such as “comprising” or “having” are intended to specify the existence of the features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, and should be understood as not precluding the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof.

[0024] Additionally, in this specification, terms such as first, second, etc. may be used to describe various elements, but these elements are not limited by these terms. These terms are used only to distinguish one element from another.

[0025] Artificial intelligence refers to the field of researching artificial intelligence or the methodologies to create it, while machine learning refers to the field of researching methodologies to define the various problems addressed in the field of artificial intelligence and to solve them.

[0026] Machine learning is also defined as an algorithm that improves the performance of a task through consistent experience.

[0027] An Artificial Neural Network (ANN) is a model used in machine learning that can refer to a model capable of problem-solving, composed of artificial neurons (nodes) that form a network through the connection of synapses.

[0028] An artificial neural network can be defined by connection patterns between neurons in different layers, a learning process that updates model parameters, and an activation function that generates output values.

[0029] An artificial neural network may include an input layer, an output layer, and optionally one or more hidden layers. Each layer may include one or more neurons, and the artificial neural network may include synapses connecting the neurons. In an artificial neural network, each neuron may output a function value of an activation function for input signals, weights, and biases input through the synapses.

[0030] Model parameters refer to parameters determined through learning, including synaptic connection weights and neuron biases. Hyperparameters refer to parameters that must be set before training in a machine learning algorithm, including the learning rate, number of iterations, mini-batch size, and initialization function.

[0031] The objective of training an artificial neural network can be viewed as determining model parameters that minimize the loss function. The loss function can be used as an indicator to determine optimal model parameters during the training process of an artificial neural network.

[0032] Machine learning can be classified into supervised learning, unsupervised learning, and reinforcement learning depending on the learning method.

[0033] Supervised learning refers to a method of training an artificial neural network with labels provided for the training data; a label can refer to the correct answer (or result value) that the artificial neural network must infer when training data is input into it.

[0034] Unsupervised learning can refer to a method of training an artificial neural network without being given labels for the training data.

[0035] Reinforcement learning can refer to a learning method that trains an agent defined within an environment to select an action or sequence of actions that maximizes the cumulative reward in each state.

[0036] Machine learning implemented as a deep neural network (DNN) containing multiple hidden layers among artificial neural networks is also called deep learning, and deep learning is a part of machine learning.

[0037] In the following, machine learning is used to include deep learning.

[0038] In the following, directions are defined based on the Cartesian coordinate system. In the Cartesian coordinate system, the x-axis direction can be defined as the left-right direction. In this case, the direction pointing towards +x relative to the origin can be defined as the right direction, and the direction pointing towards -x can be defined as the left direction. Furthermore, the y-axis direction can be defined as the front-back direction. In this case, the direction pointing towards +y relative to the origin can be defined as the front-side direction, and the direction pointing towards -y can be defined as the rear-side direction. Additionally, the z-axis direction can be defined as the up-down direction. In this case, the direction pointing towards +z relative to the origin can be defined as the upward direction, and the direction pointing towards -z can be defined as the downward direction.

[0039] Referring to FIG. 1a, the device (10) for pets may include a housing (11) that forms an exterior. In the present disclosure, the housing (11) is described as having a rectangular shape, but is not limited thereto.

[0040] The pet device (10) may include a seating portion (12) positioned on the upper side of the housing (11) and exposed to the outside. A pet may be positioned on the seating portion (12). The seating portion (12) may be made of a material that allows the pet to feel comfortable. The seating portion (12) may be formed of a soft material.

[0041] The pet device (10) may include an output device (131) capable of outputting a notification to a guardian. In this disclosure, the output device (131) is described as being a display, but is not limited thereto. For example, the output device (131) may be composed of a light-emitting diode (LED). The display (131) may be positioned on one side of the housing (11). For example, the display (131) may be positioned on the side of the housing (11) facing forward of the pet device (10).

[0042] The pet device (10) may include a power terminal (141). The power terminal (141) may be positioned on one side of the housing (11). For example, the power terminal (141) may be positioned on the upper surface of the housing (11) facing the upper direction of the pet device (10). The power terminal (141) may be positioned to be exposed to the outside. The power terminal (141) may be electrically connected to an auxiliary device when the auxiliary device performing a predetermined operation related to the pet is mounted on the housing (11). The pet device (10) may supply power to the auxiliary device mounted on the housing (11) through the power terminal (141). According to one embodiment, the power terminal (141) may be composed of a pogo pin.

[0043] The auxiliary device may include a pet house capable of housing a pet inside, an automatic feeder that supplies a set amount of food at a set time, and an automatic water dispenser that supplies a set amount of beverage at a set time. The auxiliary device may operate using power supplied from the pet device (10).

[0044] The pet device (10) and the auxiliary device can communicate via a wired method and / or a wireless method. For example, the pet device (10) can communicate with the auxiliary device through pairing using a Bluetooth method. The pet device (10) can determine the type of auxiliary device mounted on the housing (11) based on data received from the auxiliary device. The pet device (10) can transmit a command to the auxiliary device to control the performance of a predetermined operation. The pet device (10) can transmit information about the pet to the auxiliary device. The pet device (10) can output a screen regarding the operation status of the auxiliary device through the display (131) based on data received from the auxiliary device.

[0045] Referring to FIG. 1b, the pet device (10) can communicate with the user terminal (20) via a wired method and / or a wireless method. For example, the pet device (10) can communicate with the user terminal (20) via pairing using a Bluetooth method. The user terminal (20) may be a portable device such as a smartphone or a tablet PC.

[0046] The user terminal (20) can communicate with the server (30). The user terminal (20) can communicate by connecting to the server (30) via a network (40), such as the internet. For example, the user terminal (20) can update the firmware of the pet device (10) based on data received from the server (30). For example, the user terminal (20) can transmit data regarding the status of the pet received from the pet device (10) to the server (30). At this time, the server (30) can transmit the result of determining the status of the pet based on the data received from the user terminal (20) to the user terminal (20).

[0047] FIG. 2 is an internal block diagram of a device for pets according to one embodiment of the present disclosure.

[0048] Referring to FIG. 2, the pet device (10) may include a communication interface (110), a sensor (120), an input / output interface (130), a memory (150) and / or a signal processing device (160).

[0049] The communication interface (110) may include at least one communication module for communication with an external device and / or network. For example, the communication interface (110) may include a communication module for WLAN (Wireless LAN), Wi-Fi (Wireless Fidelity), Wibro (Wireless broadband), WiMAX (World Interoperability for Microwave Access), HSDPA (High Speed ​​Downlink Packet Access), Bluetooth, Bluetooth Low Energy (BLE), Zigbee, etc. For example, the communication interface (110) may include a communication module for wired communication such as USB (universal serial bus).

[0050] The sensor (120) may include at least one sensor.

[0051] The sensor (120) may include a weight sensing sensor (121) that detects the weight of the pet. The weight sensing sensor (121) may output a signal that changes in response to the weight of the pet. For example, the weight sensing sensor (121) may include a load cell.

[0052] The sensor (120) may include a temperature sensor (123) that detects the temperature of the mounting portion (12). The temperature sensor (123) may be positioned adjacent to the mounting portion (12).

[0053] The input / output interface (130) may include an input device that receives commands from a user and / or an output device that outputs information to the user. For example, the input device may include a touch panel, a physical button, a microphone, etc. For example, the output device may include a display device that outputs visual information such as a display or a light-emitting diode (LED), an audio device that outputs auditory information such as a speaker or a buzzer, etc.

[0054] The input / output interface (130) can transmit data corresponding to a command input by a user through an input device to other component(s) of the pet device (10). The input / output interface (130) can output information corresponding to data received from other component(s) of the pet device (10) through an output device.

[0055] The power supply circuit (140) can supply power throughout the pet device (10). For example, the power supply circuit (140) can supply power to a signal processing device (170) that can be implemented in the form of a System On Chip (SOC), sensors included in the sensor (120), a display (131), etc.

[0056] The power supply circuit (140) may be equipped with a converter (not shown) that converts AC power into DC power, and a DC / DC converter (not shown) that converts the level of DC power. The power supply circuit (140) may supply DC power to a power terminal (141). At this time, power may be supplied to an auxiliary device electrically connected to the power terminal (141).

[0057] The memory (150) can store programs for each signal processing and control within the signal processing device (160). The memory (150) can store data processed by the signal processing device (160) and data to be processed. For example, the memory (150) can store application programs designed for the purpose of performing various tasks that can be processed by the signal processing device (160). The memory (150) can selectively provide some of the stored application programs upon request from the signal processing device (160).

[0058] The memory (150) may include at least one of volatile memory (e.g., DRAM, SRAM, SDRAM, etc.) or non-volatile memory (e.g., flash memory, hard disk drive (HDD), solid-state drive (SSD), etc.).

[0059] The signal processing device (160) can control the overall operation of the pet device (10). The signal processing device (160) can be connected to each component provided in the pet device (10). The signal processing device (160) can control the overall operation of each component by transmitting and / or receiving signals to and from each component.

[0060] The signal processing device (160) may include at least one processor. The signal processing device (160) may control the overall operation of the pet device (10) using the processor. Here, the processor may be a general processor such as a CPU (central processing unit). Of course, the processor may be a dedicated device such as an ASIC or a processor based on other hardware.

[0061] The signal processing device (160) can determine the condition of the pet based on at least one of the sensors included in the sensor (120). For example, the signal processing device (160) can detect the weight of the pet through the weight detection sensor (121). For example, the signal processing device (160) can detect the respiration rate and / or heart rate of the pet through the weight detection sensor (121). For example, the signal processing device (160) can detect the body temperature of the pet through the temperature sensor (123).

[0062] The signal processing device (160) can transmit data regarding the condition of the pet to the user terminal (20) through the communication interface (11). For example, the pet device (10) can transmit data regarding the pet's heart rate, respiratory rate, weight, etc. to the user terminal (20) paired via Bluetooth.

[0063] The signal processing device (160) can output a screen regarding the condition of the pet through the display (131). For example, the signal processing device (160) can output a predetermined screen through the display (131) showing the pet's heart rate, respiratory rate, weight, etc.

[0064] The signal processing device (160) can acquire a ballistocardiography (BCG) signal based on the signal of the weight sensing sensor (121). The signal of the weight sensing sensor (121) may change in response to minute movements that occur when the pet's heart beats. Therefore, the signal of the weight sensing sensor (121) can correspond to a ballistocardiography (BCG) signal.

[0065] Figure 3 is an example of an internal block diagram of the signal processing device of Figure 2.

[0066] Referring to FIG. 3, the signal processing device (160) may include an amplifier (310) that amplifies the signal of the weight sensing sensor (121), a first filter (321), a second filter (323), an A / D converter (Analog-to-Digital converter) (330) that converts the analog signal into a digital signal, and / or a DSP (Digital Signal Processor) (340).

[0067] The first filter (321) can filter the signal of the weight sensing sensor (121). The first filter (321) can correspond to a first frequency band. The first filter (321) can filter the signal of the weight sensing sensor (121) so that a signal of a frequency corresponding to the first frequency band is output.

[0068] The first filter (321) may be a low-pass filter (LPF). For example, the cutoff frequency of the first filter (321) may be a first frequency (e.g., 1 Hz). At this time, the first filter (321) may filter the signal of the weight sensing sensor (121) so that a signal corresponding to a frequency lower than the first frequency is output.

[0069] The signal of the weight sensing sensor (121) filtered through the first filter (321) can correspond to the respiration of the pet. That is, the low-frequency component of the signal of the weight sensing sensor (121) can correspond to the respiration of the pet.

[0070] The second filter (323) can filter the signal of the weight detection sensor (121). The second filter (323) can correspond to a second frequency band. The second filter (323) can filter the signal of the weight detection sensor (121) so that a signal of a frequency corresponding to the second frequency band is output. At this time, the frequency included in the second frequency band may exceed the frequency included in the first frequency band.

[0071] The second filter (323) may be a band-pass filter (BPF). For example, the cutoff frequency of the second filter (323) may be a second frequency (e.g., 8 Hz) and a third frequency (e.g., 12 Hz) that exceeds the first frequency. At this time, the second filter (323) may filter the signal of the weight sensor (121) so that a signal corresponding to a frequency greater than or equal to the second frequency and less than the third frequency is output.

[0072] The signal of the weight sensing sensor (121) filtered through the second filter (323) can correspond to the heartbeat of the pet. That is, the high-frequency component of the signal of the weight sensing sensor (121) can correspond to the heartbeat of the pet.

[0073] The A / D converter (330) can convert the signal output from the first filter (321) into a digital signal corresponding to the pet's breathing and output it. Hereinafter, the signal corresponding to the pet's breathing may be referred to as a breathing signal. For example, referring to reference numeral 401 in FIG. 4, the signal processing device (160) can obtain a breathing signal from the signal of the weight detection sensor (121).

[0074] The A / D converter (330) can convert the signal output from the second filter (323) into a digital signal corresponding to the heart rate of the pet and output it. Hereinafter, the signal corresponding to the heart rate of the pet may be referred to as a heart rate signal. For example, referring to reference numeral 402 in FIG. 4, the signal processing device (160) can obtain a heart rate signal from the signal of the weight sensing sensor (121).

[0075] The DSP (340) can process the signal output from the A / D converter (330).

[0076] The DSP (340) can detect the peak value of the signal output from the A / D converter (330). For example, the DSP (340) can detect the peak of the respiration signal. For example, the DSP (340) can detect the peak of the heart rate signal.

[0077] According to embodiments of the present disclosure, various detection methods may be used for detecting peaks of a signal. For example, methods used for detecting peaks of a signal may include methods for calculating differentiation or moving averages of the signal, methods using a specific algorithm (e.g., SciPy library), and methods using a pre-trained learning model using machine learning. A trained artificial neural network may be referred to as a learning model.

[0078] The DSP (340) can detect the condition of the pet. For example, the DSP (340) can calculate the number of breaths of the pet based on the peak of the breath signal. For example, the DSP (340) can calculate the heart rate of the pet based on the peak of the heart rate signal.

[0079] In the present disclosure, the signal processing device (160) is described as including an amplifier (310), a first filter (321), a second filter (323), an A / D converter (330), and a DSP (340), but is not limited thereto. That is, at least some of the amplifier (310), the first filter (321), the second filter (323), the A / D converter (330), and the DSP (340) may be implemented in a configuration distinct from the signal processing device (160).

[0080] Meanwhile, FIG. 3 is merely an example referenced in the description of the operation of the signal processing device (160), and the present disclosure is not limited thereto.

[0081] FIGS. 5a and 5b are drawings referenced in the description of a configuration disposed inside a device for pets according to one embodiment of the present disclosure.

[0082] Referring to FIGS. 5a and 5b, a weight sensing sensor (121) may be disposed inside the housing (11). The weight sensing sensor (121) may be disposed in a first layer (510) located inside the housing (11). The weight sensing sensor (121) may be disposed corresponding to the center of the first layer (510).

[0083] The device for pets (10) may include at least one protruding member (510). The protruding member (510) may be disposed on a second layer (502) disposed on the upper side of a first layer (501).

[0084] The second layer (502) may be placed on the lower side of the seating portion (12). The second layer (502) may bear part of the load applied to the inside of the pet device (10) by the pet.

[0085] The protruding member (510) can be formed in a shape corresponding to a dome structure corresponding to a hemisphere. The cross-sectional area of ​​the protruding member (510) corresponding to a plane perpendicular to the vertical direction can be increased toward the downward direction.

[0086] The protruding member (510) may be positioned to correspond to the center of the second layer (502). For example, a plurality of protruding members (510) may be positioned side by side in a predetermined area (500) corresponding to the center of the second layer (502). The predetermined area (500) may be an area having a predetermined length (L) and a predetermined width (W) corresponding to the center of the second layer (502).

[0087] As a second layer (502) having a protruding member (510) is positioned on the upper side of a first layer (501) having a weight sensing sensor (121), minute movements caused by the heartbeat of a pet located on the seating portion (12) can be detected more accurately through the weight sensing sensor (121).

[0088] FIG. 6 is a flowchart of a method of operation for a pet device according to one embodiment of the present disclosure.

[0089] Referring to FIG. 6, the pet device (10) can acquire a signal from a weight detection sensor (121) in operation S610. The pet device (10) can detect the weight of the pet based on the signal from the weight detection sensor (121).

[0090] The pet device (10) can acquire a data set based on the signal of the weight sensing sensor (121) in operation S620. The pet device (10) can store the acquired data set in memory (150). Here, the data set can acquire data corresponding to the pet's respiration (hereinafter, respiration data) and data corresponding to the pet's heart rate (hereinafter, heart rate data).

[0091] The pet device (10) can acquire a data set according to a predetermined period. For example, if the predetermined period is set to 30 seconds, the pet device (10) can periodically repeat the operation of configuring the data acquired during the 30 seconds into a single data set.

[0092] The pet device (10) can acquire a respiration signal based on the signal of the weight sensing sensor (121). The pet device (10) can acquire respiration data based on the respiration signal. The respiration data may include the level of the peak of the respiration signal, the interval between the peaks of the respiration signal, the number of breaths of the pet, etc.

[0093] The pet device (10) can acquire a heart rate signal based on the signal from the weight sensing sensor (121). The pet device (10) can acquire heart rate data based on the heart rate signal. The heart rate data may include the level of the peak of the heart rate signal, the heart rate of the pet, etc.

[0094] According to one embodiment, the pet device (10) can acquire a data set based on the signal of the weight detection sensor (121) when the weight of the pet detected based on the signal of the weight detection sensor (121) is greater than or equal to a predetermined weight. Here, the predetermined weight may be a weight that serves as a criterion for determining whether the pet is positioned on the seating portion (12). That is, the pet device (10) can acquire respiration data and heart rate data based on the signal of the weight detection sensor (121) when the pet is positioned on the seating portion (12).

[0095] The pet device (10) can calculate multiple parameters for each of the multiple data sets in the S630 operation.

[0096] A device (10) for a pet can calculate a parameter corresponding to the respiration of the pet (hereinafter referred to as a respiration parameter) for each of a plurality of data sets. For example, the respiration parameter may include a standard deviation for the level of a peak of a respiration signal (hereinafter referred to as a level standard deviation), a standard deviation for the interval between peaks of a respiration signal (hereinafter referred to as an interval standard deviation), a respiration rate, etc.

[0097] A device (10) for a pet can calculate a parameter (hereinafter referred to as a heart rate parameter) corresponding to the heart rate of the pet for each of a plurality of data sets. For example, the heart rate parameter may include the level difference between the minimum and maximum values ​​of the level of the heart rate signal, the prominence of the peak of the heart rate signal, the heart rate, etc. Here, the level difference between the minimum and maximum values ​​of the level of the heart rate signal may refer to the peak-to-peak of the heart rate signal. The prominence may be a value indicating the degree to which the peak of the heart rate signal stands out compared to surrounding values. The prominence of the peak of the heart rate signal included in the heart rate parameter may be a representative value such as the average value or median value of the prominence.

[0098] The pet device (10) can determine whether the acquisition of data used to determine the condition of the pet is completed in the S640 operation. For example, the pet device (10) can check whether multiple data sets for a preset period (e.g., 1 day) are acquired.

[0099] In operation S650, when the acquisition of data used to determine the condition of the pet is completed, the pet device (10) can determine a candidate for a data set used to determine the condition of the pet (hereinafter, candidate data set) based on the respiration parameter.

[0100] The respiration parameter can indicate the degree of stability of the pet. That is, the pet device (10) can determine a candidate data set among a plurality of data sets by considering the degree of stability of the pet. For example, as the pet's posture becomes more stable, the level standard deviation and the interval standard deviation may be smaller.

[0101] According to one embodiment, a device for pets (10) can determine a first number (e.g., 10) of first candidate data sets among a plurality of data sets in order of smallest level standard deviation. Additionally, the device for pets (10) can determine a second number (e.g., 10) of second candidate data sets among a plurality of data sets in order of smallest interval standard deviation.

[0102] At this time, the pet device (10) can determine the data set that is duplicated in the first candidate data set and the second candidate data set as the candidate data set used to determine the condition of the pet.

[0103] According to one embodiment, a device for a pet (10) can determine a third number (e.g., 10) of third candidate data sets in order of the smallest number of respirations of the pet among a plurality of data sets. At this time, the device for a pet (10) can determine a data set that is duplicated in the first candidate data set, the second candidate data set, and the third candidate data set as a candidate data set used to determine the condition of the pet.

[0104] According to one embodiment, a device for pets (10) can calculate a predetermined value representing the degree of stability of a pet using a predetermined calculation formula. For example, the level standard deviation and the interval standard deviation may correspond to variables of the predetermined calculation formula. At this time, the device for pets (10) can determine a candidate data set among a plurality of data sets based on the predetermined value calculated through the predetermined calculation formula.

[0105] The pet device (10) can determine a data set (hereinafter referred to as the final data set) used to determine the condition of the pet based on heart rate parameters in the S660 operation.

[0106] Heart rate parameters can indicate the accuracy of the detection of the ballistocardiogram. That is, the pet device (10) can determine the final data set among candidate data sets determined based on the respiration parameters, taking into account the accuracy of the detection of the ballistocardiogram. For example, the closer the pet's chest is to the seating area, the greater the prominence of the peak of the heart rate signal may be.

[0107] According to one embodiment, a device for pets (10) can determine the data set with the greatest prominence for the peak of the heart rate signal among candidate data sets determined based on respiratory parameters as the final data set.

[0108] Meanwhile, the pet device (10) can determine a predetermined number (e.g., 10) of fourth candidate data sets among a plurality of data sets in order of the smallest peak-to-peak of the heart rate signal. At this time, the pet device (10) can determine the data set with the largest prominence for the peak of the heart rate signal as the final data set among the candidate data set determined based on the respiration parameter and the data set included in duplicate in the fourth candidate data set.

[0109] The device (10) for a pet can store data regarding the condition of the pet in memory (150). Here, the data regarding the condition of the pet may include the pet's body temperature, the pet's weight, the respiratory rate, heart rate, etc. corresponding to the final data set.

[0110] A pet device (10) can communicate with a user terminal (20) and / or a server (30). The pet device (10) can transmit data regarding the condition of the pet to the user terminal (20) and / or the server (30). The user terminal (20) and / or the server (30) can store data regarding the condition of the pet transmitted from the pet device (10).

[0111] When a device for pets (10) determines a final data set from multiple data sets according to a preset period (e.g., 1 day), the final data set provided by the device for pets (10) can be stored at a preset period (e.g., 1 day). Accordingly, the user can check the accumulated state of the pet, changes in the state of the pet, etc. based on the data set stored at a preset period (e.g., 1 day).

[0112] FIGS. 7 to 13 are drawings referenced in the description of the operation of a device for pets according to an embodiment of the present disclosure.

[0113] Referring to reference numeral 701 of FIG. 7, the pet device (10) can acquire a respiration signal. The pet device (10) can detect peaks (711, 712, 713) of the respiration signal. In the present disclosure, detecting valleys of the waveform of the respiration signal as peaks (711, 712, 713) is described as an example, but is not limited thereto.

[0114] A device for pets (10) can calculate the level of the peaks of the respiration signal, the interval between the peaks of the respiration signal, the number of respirations of the pets, the standard deviation of the level, the standard deviation of the interval, etc., based on the result of detecting the peaks (711, 712, 713) of the respiration signal.

[0115] Referring to reference numeral 702 of FIG. 7, the pet device (10) can acquire a heart rate signal. The pet device (10) can detect a peak of the heart rate signal. Based on the result of detecting the peak of the heart rate signal, the pet device (10) can calculate the heart rate, the prominence of the peak of the heart rate signal, the peak-to-peak (pp) of the heart rate signal, etc.

[0116] Referring to Figures 8 and 9, the waveform of the respiration signal may differ depending on the degree of stability of the pet.

[0117] When the posture and breathing of the pet are relatively stable (801, 802), the level of the peaks of the breathing signal and the interval between peaks may be constant. Therefore, when the posture and breathing of the pet are relatively stable (801, 802), the level standard deviation and the interval standard deviation of the breathing signal may be relatively small.

[0118] Meanwhile, when the pet’s posture, breathing, etc. are relatively unstable, such as when the pet’s breathing is irregular (901) or when there is a change in the pet’s posture (902), the level of the peaks of the breathing signal and / or the interval between peaks may not be constant. Therefore, when the pet’s posture, breathing, etc. are relatively unstable (901, 902), the level standard deviation and / or interval standard deviation of the breathing signal may be relatively large.

[0119] Referring to FIGS. 10 to 13, information regarding the condition of a pet based on data regarding the condition of a pet provided by the pet device (10) can be provided to the user.

[0120] Referring to FIG. 10, the user terminal (20) can output a screen (1000) showing the state of the pet. For example, the user terminal (20) can output a screen (1000) showing the state of the pet based on data regarding the state of the pet stored in the pet device (10), the user terminal (20), and / or the server (30).

[0121] A screen (1000) displaying the condition of a pet may include a user interface (1011) corresponding to the condition of the pet, such as the pet's body temperature, weight, heart rate, and respiratory rate. A screen (1000) displaying the condition of a pet may include a message (1012) indicating the condition of the pet.

[0122] When a user selects one of the pet states included in the user interface (1011), a detailed screen corresponding to the selected pet state can be displayed.

[0123] Referring to FIG. 11, the user terminal (20) can output a first screen (1100) corresponding to the number of breaths of the pet based on data regarding the condition of the pet.

[0124] Referring to reference numeral 1101, the first screen (1100) may include a message (1111) indicating the number of breaths of a pet, the number of breaths (1112) of a pet detected at each preset period (e.g., 1 day), etc.

[0125] Referring to reference numeral 1102, the first screen (1100) may include a message (1121) indicating the number of breaths of a pet, and a result (1122) of accumulating the number of breaths of a pet detected at each interval for a preset period (e.g., 1 day).

[0126] Referring to FIG. 12, the user terminal (20) can output a second screen (1200) corresponding to the heart rate of the pet based on data regarding the condition of the pet.

[0127] Referring to reference numeral 1201, the second screen (1200) may include a message (1211) indicating the heart rate of the pet, the heart rate (1212) of the pet detected at each preset period (e.g., 1 day), etc.

[0128] Referring to reference numeral 1202, the second screen (1200) may include a message (1221) indicating the heart rate of the pet, and a result (1222) of accumulating the heart rate of the pet detected at each interval of a preset period (e.g., 1 day).

[0129] Referring to FIG. 13, the user terminal (20) can output a third screen (1300) corresponding to the result of comprehensively determining the condition of the pet based on data regarding the condition of the pet.

[0130] The user terminal (20) and / or server (30) can produce a result of comprehensively determining the condition of the pet based on data regarding the condition of the pet by using a previously learned learning model in relation to the condition of the pet.

[0131] The third screen (1300) may include a message (1310) indicating the result of comprehensively judging the condition of the pet, a table (1320) regarding the result of comprehensively judging the condition of the pet, etc.

[0132] As described above, according to at least one embodiment of the present disclosure, the condition of a pet can be monitored using the weight of the pet.

[0133] In addition, according to at least one embodiment of the present disclosure, the accuracy of monitoring the condition of a pet can be improved by using various parameters.

[0134] In addition, according to at least one embodiment of the present disclosure, information related to the condition of a pet can be provided to a guardian.

[0135] Referring to FIGS. 1 to 13, a device (10) for a pet according to one aspect of the present disclosure includes a weight sensing sensor (121) for detecting the weight of a pet and a signal processing device (160) for processing a signal from the weight sensing sensor (121). The signal processing device (160) acquires a plurality of data sets based on the signal from the weight sensing sensor (121), calculates a first parameter corresponding to the respiration of the pet and a second parameter corresponding to the heart rate of the pet for each of the plurality of data sets, determines a first data set among the plurality of data sets based on the first parameter, and determines a second data set among the first data sets used for determining the state of the pet based on the second parameter, and each of the plurality of data sets may include a first data corresponding to the respiration of the pet and a second data corresponding to the heart rate of the pet.

[0136] Additionally, according to one aspect of the present disclosure, the signal processing device (160) further comprises a first filter (321) corresponding to a first frequency band; and a second filter (323) corresponding to a second frequency band, wherein the second filter (323) includes a second frequency exceeding a first frequency included in the first frequency band, and the signal processing device (160) can acquire the first data based on a first signal that the first filter (321) outputs by filtering the signal of the weight sensing sensor (121), and acquire the second data based on a second signal that the second filter (323) outputs by filtering the signal of the weight sensing sensor (121).

[0137] Additionally, according to one aspect of the present disclosure, the signal processing device (160) may acquire a breathing signal corresponding to the breathing of the pet based on the signal of the weight sensing sensor (121), and calculate the first parameter including a first standard deviation for the level of the peak value of the breathing signal and a second standard deviation for the interval of the peak value of the breathing signal.

[0138] Additionally, according to one aspect of the present disclosure, the signal processing device (160) may determine a first number of first candidate data sets in order of smallest first standard deviation among the plurality of data sets, determine a second number of second candidate data sets in order of smallest second standard deviation among the plurality of data sets, and determine a data set that is included in duplicate in the first candidate data set and the second candidate data set as the first data set.

[0139] Additionally, according to one aspect of the present disclosure, the signal processing device (160) calculates the number of breaths of the pet based on the peak value of the breath signal, determines a third number of third candidate data sets in order of the smallest number of breaths of the pet among the plurality of data sets, and can determine the data set that is duplicated in the first candidate data set, the second candidate data set, and the third candidate data set as the first data set.

[0140] Additionally, according to one aspect of the present disclosure, the signal processing device (160) may acquire a heart rate signal corresponding to the heart rate of the pet based on the signal of the weight sensing sensor (121), and calculate the second parameter including a prominence for the peak value of the heart rate signal based on the level of the peak value of the heart rate signal.

[0141] Additionally, according to one aspect of the present disclosure, the signal processing device (160) may determine the data set with the largest prominence among the first data sets as the second data set.

[0142] Additionally, according to one aspect of the present disclosure, the second parameter includes a level difference between a minimum value and a maximum value of the level of the heart rate signal, and the signal processing device (160) determines a predetermined number of candidate data sets among the plurality of data sets in order of the smallest level difference, and among the data sets that are duplicated in the first data set and the candidate data sets, the data set with the largest prominence can be determined as the second data set.

[0143] Additionally, according to one aspect of the present disclosure, the apparatus may further include a housing (11); a first layer (501) located on the inside of the housing (11) on which the weight sensing sensor (121) is disposed; and a second layer (502) located on the upper side of the first layer (501) on which at least one protruding member (510) is disposed.

[0144] Additionally, according to one aspect of the present disclosure, the protruding member (510) may be formed in a shape corresponding to a dome structure corresponding to a hemisphere.

[0145] A method of operation of a device (10) for a pet according to one aspect of the present disclosure comprises: acquiring a plurality of data sets based on a signal from a weight sensing sensor (121) that detects the weight of the pet; calculating a first parameter corresponding to the respiration of the pet for each of the plurality of data sets; calculating a second parameter corresponding to the heart rate of the pet for each of the plurality of data sets; determining a first data set among the plurality of data sets based on the first parameter; and determining a second data set among the first data sets used for determining the state of the pet based on the second parameter, wherein each of the plurality of data sets may include a first data corresponding to the respiration of the pet and a second data corresponding to the heart rate of the pet.

[0146] Additionally, according to one aspect of the present disclosure, the operation of acquiring the plurality of data sets comprises: an operation of acquiring the first data based on a first signal output by a first filter (321) corresponding to a first frequency band filtering the signal of the weight sensing sensor (121); and an operation of acquiring the second data based on a second signal output by a second filter (323) corresponding to a second frequency band filtering the signal of the weight sensing sensor (121), wherein the first frequency included in the first frequency band may be less than the second frequency included in the second frequency band.

[0147] Additionally, according to one aspect of the present disclosure, the method further includes an operation of obtaining a breathing signal corresponding to the breathing of the pet based on a signal from the weight sensing sensor (121), and the operation of calculating the first parameter may include a first standard deviation for the level of the peak value of the breathing signal and a second standard deviation for the interval of the peak value of the breathing signal.

[0148] Additionally, according to one aspect of the present disclosure, the operation of determining the first data set may include: the operation of determining a first number of first candidate data sets among the plurality of data sets in order of smallest first standard deviation; the operation of determining a second number of second candidate data sets among the plurality of data sets in order of smallest second standard deviation; and the operation of determining a data set that is duplicated in the first candidate data set and the second candidate data set as the first data set.

[0149] Additionally, according to one aspect of the present disclosure, the operation of determining the first data set may include: the operation of calculating the respiratory rate of the pet based on the peak value of the respiratory signal; the operation of determining a third number of third candidate data sets among the plurality of data sets in order of the smallest respiratory rate of the pet; and the operation of determining the data set that is included in duplicate in the first candidate data set, the second candidate data set, and the third candidate data set as the first data set.

[0150] Additionally, according to one aspect of the present disclosure, the method further includes an operation of obtaining a heart rate signal corresponding to the heart rate of the pet based on a signal from the weight sensing sensor (121), and the operation of calculating the second parameter may include an operation of calculating the second parameter including a prominence for the peak value of the heart rate signal based on the level of the peak value of the heart rate signal.

[0151] Additionally, according to one aspect of the present disclosure, the operation of determining the second data set may include the operation of determining the data set with the largest prominence among the first data sets as the second data set.

[0152] Additionally, according to one aspect of the present disclosure, the second parameter includes a level difference between a minimum value and a maximum value of the level of the heart rate signal, and the operation of determining the second data set may include: determining a predetermined number of candidate data sets among the plurality of data sets in order of the smallest level difference; and determining the data set with the largest prominence among the data sets included in duplicate in the first data set and the candidate data sets as the second data set.

[0153] A recording medium according to one aspect of the present disclosure stores a computer-readable program for performing a method of operation of a device (10) for a pet, wherein the method of operation includes: acquiring a plurality of data sets based on a signal from a weight sensing sensor (121) that detects the weight of the pet; calculating a first parameter corresponding to the respiration of the pet for each of the plurality of data sets; calculating a second parameter corresponding to the heart rate of the pet for each of the plurality of data sets; determining a first data set among the plurality of data sets based on the first parameter; and determining a second data set among the first data sets used for determining the condition of the pet based on the second parameter, wherein each of the plurality of data sets may include a first data corresponding to the respiration of the pet and a second data corresponding to the heart rate of the pet.

[0154] Additionally, according to one aspect of the present disclosure, the method of operation further comprises: an operation of obtaining a respiration signal corresponding to the respiration of the pet based on a signal of the weight sensing sensor (121); and an operation of obtaining a heart rate signal corresponding to the heart rate of the pet based on a signal of the weight sensing sensor (121); wherein the operation of calculating the first parameter includes a first standard deviation for the level of the peak value of the respiration signal and a second standard deviation for the interval of the peak value of the respiration signal; and the operation of calculating the second parameter may include a second parameter including a prominence for the peak value of the heart rate signal based on the level of the peak value of the heart rate signal.

[0155] The attached drawings are intended only to facilitate understanding of the embodiments disclosed in this specification, and the technical concept disclosed in this specification is not limited by the attached drawings and should be understood to include all modifications, equivalents, and substitutions that fall within the concept and technical scope of this disclosure.

[0156] Meanwhile, the method of operation of the present disclosure can be implemented as processor-readable code on a processor-readable recording medium. A processor-readable recording medium includes all types of recording devices in which data that can be read by a processor is stored. Examples of processor-readable recording media include ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc., and also include implementation in the form of a carrier wave, such as transmission over the Internet. Furthermore, the processor-readable recording medium may be distributed across networked computer systems, so that processor-readable code can be stored and executed in a distributed manner.

[0157] Furthermore, although preferred embodiments of the present disclosure have been illustrated and described above, the present disclosure is not limited to the specific embodiments described above. It is understood that various modifications can be made by those skilled in the art without departing from the essence of the present disclosure as claimed in the claims, and such modifications should not be understood individually from the technical spirit or perspective of the present disclosure.

Claims

1. A weight-sensing sensor that detects the weight of a pet and It includes a signal processing device that processes the signal of the weight sensing sensor, and The above signal processing device is, Based on the signal of the weight sensing sensor above, a plurality of data sets are obtained, and For each of the above plurality of data sets, a first parameter corresponding to the respiration of the pet and a second parameter corresponding to the heart rate of the pet are calculated, and Based on the first parameter above, a first data set among the plurality of data sets is determined, and Based on the second parameter above, a second data set used for determining the condition of the pet among the first data sets is determined, and A device for a pet, characterized in that each of the above plurality of data sets includes a first data corresponding to the respiration of the pet and a second data corresponding to the heartbeat of the pet.

2. In Paragraph 1, A first filter corresponding to a first frequency band; and It further includes a second filter corresponding to a second frequency band, which includes a second frequency that exceeds a first frequency included in the first frequency band, and The above signal processing device is, Based on the first signal output by the first filter filtering the signal of the weight sensing sensor, the first data is obtained, and A device for pets characterized by acquiring the second data based on the second signal output by the second filter filtering the signal of the weight sensing sensor.

3. In Paragraph 1, The above signal processing device is, Based on the signal of the weight sensing sensor, a breathing signal corresponding to the breathing of the pet is obtained, and A device for a pet, characterized by calculating the first parameter including the first standard deviation for the level of the peak value of the respiration signal and the second standard deviation for the interval of the peak value of the respiration signal.

4. In Paragraph 3, The above signal processing device is, Among the plurality of data sets above, a first number of first candidate data sets are determined in order of smallest first standard deviation, and Among the plurality of data sets above, a second number of second candidate data sets are determined in order of smallest second standard deviation, and A device for pets characterized by determining the data set that is duplicated in the first candidate data set and the second candidate data set as the first data set.

5. In Paragraph 4, The above signal processing device is, Based on the peak value of the above respiration signal, the respiration rate of the pet is calculated, and Among the above plurality of data sets, a third number of third candidate data sets are determined in order of decreasing respiration rate of the pet, and A device for pets characterized by determining the data set that is duplicated in the first candidate data set, the second candidate data set, and the third candidate data set as the first data set.

6. In Paragraph 1, The above signal processing device is, Based on the signal of the weight sensing sensor, a heart rate signal corresponding to the heart rate of the pet is obtained, and A device for a pet, characterized by calculating the second parameter including the prominence of the peak value of the heart rate signal based on the level of the peak value of the heart rate signal.

7. In Paragraph 6, The above signal processing device is, A device for pets characterized by determining the data set with the largest prominence among the first data sets as the second data set.

8. In Paragraph 6, The second parameter above includes the level difference between the minimum and maximum values ​​of the heart rate signal level, and The above signal processing device is, Among the above plurality of data sets, a predetermined number of candidate data sets are determined in order of smallest level difference, and A device for pets characterized by determining the data set with the largest prominence among the data sets that are duplicated in the first data set and the candidate data set as the second data set.

9. In Paragraph 1, Housing; A first layer located on the inner side of the housing where the weight sensing sensor is disposed; and A device for a pet, characterized by further including a second layer located on the upper side of the first layer, wherein at least one protruding member is disposed thereon.

10. In Paragraph 9, A device for pets, characterized in that the above-mentioned protruding member is formed in a shape corresponding to a dome structure corresponding to a hemisphere.

11. In a method of operating a device for companion animals, The operation of acquiring multiple data sets based on the signal of a weight sensing sensor that detects the weight of a pet; For each of the above plurality of data sets, the operation of calculating a first parameter corresponding to the respiration of the pet; For each of the plurality of data sets above, the operation of calculating a second parameter corresponding to the heart rate of the pet; An operation to determine a first data set among the plurality of data sets based on the first parameter above; and Based on the second parameter above, the operation includes determining a second data set among the first data sets used for determining the state of the pet, and A method of operation for a pet device, characterized in that each of the above plurality of data sets includes a first data corresponding to the respiration of the pet and a second data corresponding to the heartbeat of the pet.

12. In Paragraph 11, The operation of acquiring the above multiple data sets is, An operation of acquiring the first data based on a first signal output by a first filter corresponding to a first frequency band filtering the signal of the weight sensing sensor; and The method includes an operation of acquiring the second data based on a second signal output by a second filter corresponding to a second frequency band that filters the signal of the weight sensing sensor, and A method of operation for a pet device, characterized in that the first frequency included in the first frequency band is less than the second frequency included in the second frequency band.

13. In Paragraph 11, The method further includes the operation of acquiring a breathing signal corresponding to the breathing of the pet based on the signal of the weight sensing sensor. The operation of calculating the first parameter above is, A method of operation for a pet device, characterized by including an operation of calculating a first parameter, which includes a first standard deviation for the level of the peak value of the respiration signal and a second standard deviation for the interval of the peak value of the respiration signal.

14. In Paragraph 13, The operation of determining the first data set above is, The operation of determining a first number of first candidate data sets among the plurality of data sets in order of smallest first standard deviation; The operation of determining a second number of second candidate data sets among the plurality of data sets above in order of the smallest second standard deviation; and A method of operation for a pet device characterized by including an operation of determining a data set that is duplicated in the first candidate data set and the second candidate data set as the first data set.

15. In Paragraph 14, The operation of determining the first data set above is, The operation of calculating the respiratory rate of the pet based on the peak value of the above respiratory signal; The operation of determining a third number of third candidate data sets among the plurality of data sets above in order of decreasing respiration rate of the pet; and A method of operation for a pet device, characterized by including an operation of determining a data set that is duplicated in the first candidate data set, the second candidate data set, and the third candidate data set as the first data set.

16. In Paragraph 11, The method further includes the operation of acquiring a heart rate signal corresponding to the heart rate of the pet based on the signal of the weight sensing sensor. The operation of calculating the above second parameter is, A method of operation for a pet device, characterized by including an operation of calculating a second parameter including a prominence for the peak value of the heart rate signal based on the level of the peak value of the heart rate signal.

17. In Paragraph 16, The operation of determining the above second data set is, A method of operation for a pet device, characterized by including an operation to determine the data set with the largest prominence among the first data sets as the second data set.

18. In Paragraph 16, The second parameter above includes the level difference between the minimum and maximum values ​​of the heart rate signal level, and The operation of determining the above second data set is, The operation of determining a predetermined number of candidate data sets among the plurality of data sets in order of smallest level difference; and A method of operation for a pet device, characterized by including an operation to determine the data set with the largest prominence among the data sets that are duplicated in the first data set and the candidate data set as the second data set.

19. A recording medium storing a computer-readable program for performing a method of operating a device for companion animals, The above method of operation is, The operation of acquiring multiple data sets based on the signal of a weight sensing sensor that detects the weight of a pet; For each of the above plurality of data sets, the operation of calculating a first parameter corresponding to the respiration of the pet; For each of the plurality of data sets above, the operation of calculating a second parameter corresponding to the heart rate of the pet; An operation to determine a first data set among the plurality of data sets based on the first parameter above; and Based on the second parameter above, the operation includes determining a second data set among the first data sets used for determining the state of the pet, and A recording medium characterized in that each of the above plurality of data sets includes a first data corresponding to the respiration of the pet and a second data corresponding to the heartbeat of the pet.

20. In Paragraph 19, The above method of operation is, The operation of acquiring a breathing signal corresponding to the breathing of the pet based on the signal of the weight sensing sensor; and The method further includes the operation of acquiring a heart rate signal corresponding to the heart rate of the pet based on the signal of the weight sensing sensor. The operation of calculating the first parameter above is, The method includes an operation to calculate the first parameter, comprising a first standard deviation for the level of the peak value of the breath signal and a second standard deviation for the interval of the peak value of the breath signal. The operation of calculating the above second parameter is, A recording medium characterized by including an operation to calculate a second parameter, which includes a prominence for the peak value of the heart rate signal based on the level of the peak value of the heart rate signal.

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