Information processing system, information processing method and program

Abstract

An information processing system, an information processing method, and a program are provided that can provide more useful sensing using a wireless communication system. [Solution] A method executed by an information processing system includes a first acquisition step of acquiring channel state information transmitted from a wireless communication device, an identification step of inputting the channel state information to a first processing model and causing the first processing model to output motion information related to a subject's motion, and an evaluation step of inputting the motion information to a second processing model and causing the second processing model to output an evaluation related to the subject's motion corresponding to the motion information. Here, the first processing model is a trained model that receives channel state information as input and is trained to be able to output motion information of the subject corresponding to the channel state information, and the second processing model is a processing model that receives motion information as input and is able to output an evaluation corresponding to the motion information.

Description

[Technical Field]

[0001] The present invention relates to an information processing system, an information processing method, and a program. [Background technology]

[0002] Patent Document 1 is a document relating to a method performed in a wireless local area network (WLAN) system. Patent Document 1 proposes a new signal transmission / reception procedure between non-access point stations (STAs) when sensing measurement is initiated by a non-access point (AP) station. Patent Document 1 proposes a sensing measurement procedure performed by an AP when a non-AP STA transmits a sensing start frame to the AP. Patent Document 1 also proposes a procedure in which a non-AP STA transmits a sensing start frame to the AP, causing the AP to request the responder STA to transmit an NDP frame. Patent Document 1 also proposes a method for configuring frames transmitted and received in the procedure. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Special Publication No. 2024-500889 Summary of the Invention [Problem to be solved by the invention]

[0004] However, there is still room for improvement in sensing technology using wireless communication systems. [Means for solving the problem]

[0005] According to one aspect of the present invention, there is provided an information processing system comprising at least one processor, the processor being configured to execute the following steps by reading a program: a first acquisition step acquiring channel state information transmitted from a wireless communication device; a determination step inputting the channel state information into a first processing model and causing the first processing model to output behavior information regarding the behavior of a subject; and an evaluation step inputting the behavior information into a second processing model and causing the second processing model to output an evaluation regarding the behavior of the subject corresponding to the behavior information, wherein the first processing model is a trained model that has been trained to be able to input channel state information and output behavior information of a subject corresponding to the channel state information, and the second processing model is a processing model that is able to input behavior information and output an evaluation corresponding to the behavior information.

[0006] According to one aspect of the present invention, it is possible to provide an information processing system or the like that can provide more useful sensing using a wireless communication system. [Brief explanation of the drawings]

[0007] [Figure 1] 1 is a configuration diagram illustrating an information processing system 1. FIG. [Figure 2] FIG. 2 is a block diagram showing the hardware configuration of the server 2. [Figure 3] FIG. 2 is a block diagram showing a hardware configuration of an information processing device 3. [Figure 4] FIG. 2 is a block diagram showing the hardware configuration of the wireless communication device 4. [Figure 5] 2 is a flowchart showing an outline of processing executed by the information processing system 1. [Figure 6] This shows an example of a WiFi signal when the door between rooms A and B is closed. [Figure 7] This shows an example of the WiFi signal when the door between rooms A and B is open. [Figure 8] 9 is a floor plan 9 showing an example of a location where a wireless communication device 4 is installed. [Figure 9] 2 is an activity diagram showing an example of the flow of processing executed by the information processing system 1. FIG. [Figure 10] FIG. 2 is a diagram showing an example of the flow of information in the information processing system 1. [Figure 11] 10 is a diagram showing an example of the relationship between specified small action information and recognized actions. FIG. DETAILED DESCRIPTION OF THE INVENTION

[0008] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be described below with reference to the accompanying drawings. Various features shown in the following embodiments can be combined with each other.

[0009] Incidentally, the program for realizing the software appearing in one embodiment may be provided as a non-transitory computer-readable medium, or may be provided so that it can be downloaded from an external server, or may be provided so that the program is started on an external computer and its functions are realized on a client terminal (so-called cloud computing).

[0010] Furthermore, various information processing according to an embodiment may realize input and output corresponding to the input. Here, the form of information referenced in such information processing (hereinafter referred to as reference information) is not limited as long as an output is obtained as a result of the input. The reference information may be, for example, rule-based information such as a database, a lookup table, or a predetermined function (including a decision formula such as a regression formula constructed using a statistical method), a trained model that has previously trained the correlation between input and output, or a large-scale language model that can output a desired result by inputting a prompt.

[0011] In one embodiment, a "unit" may include, for example, a combination of hardware resources implemented by a circuit in the broad sense and software information processing that can be specifically realized by these hardware resources. In one embodiment, various information is handled, and this information is represented, for example, by physical values ​​of signal values ​​representing voltage and current, high and low signal values ​​as a binary bit set consisting of 0 or 1, or quantum superposition (so-called quantum bits), and communication and calculations can be performed on a circuit in the broad sense.

[0012] Furthermore, a circuit in the broad sense is a circuit realized by at least an appropriate combination of a circuit, circuitry, processor, memory, etc. The processor may be a general-purpose processor or a dedicated circuit. That is, it includes an application specific integrated circuit (ASIC), a programmable logic device (e.g., a simple programmable logic device (SPLD), a complex programmable logic device (CPLD), and a field programmable gate array (FPGA)), etc.

[0013] 1. Hardware Configuration This section explains the hardware configuration.

[0014] <Information Processing System 1> FIG. 1 is a configuration diagram showing an information processing system 1. The information processing system 1 includes a server 2, an information processing device 3, and a wireless communication device 4. The server 2, the information processing device 3, and the wireless communication device 4 are configured to be able to communicate with each other via a telecommunications line (network). The wireless communication device 4 may include a wireless communication device 4a and a wireless communication device 4b. The wireless communication device 4a may be configured to be able to communicate with the server 2 and the information processing device 3 via the telecommunications line (network), and the wireless communication device 4b may be configured to be able to communicate with the wireless communication device 4a. A plurality of wireless communication devices 4b may be provided for the wireless communication device 4a.

[0015] Here, a system exemplified as information processing system 1 is made up of one or more devices or components. Therefore, it should be noted that the information processing system 1 includes either the server 2 alone or the server 2, the information processing device 3, and the wireless communication device 4. The information processing device 3 may have the functions of the wireless communication device 4; more specifically, the information processing system 1 may include an element selected from the group consisting of the server 2, the information processing device 3, and the wireless communication device 4. The unselected element may not be included in the information processing system 1, but may be electrically connected to the selected element as an external element. These components will be described below.

[0016] <Server 2> 2 is a block diagram showing the hardware configuration of server 2. Server 2 includes a communication unit 21, a storage unit 22, and a control unit 23, and these components are electrically connected via a communication bus 20 inside server 2. Each component will be further described below.

[0017] The communication unit 21 is preferably a wired communication means such as USB, IEEE1394, Thunderbolt (registered trademark), wired LAN network communication, etc., but may also include wireless LAN network communication, mobile communication such as 3G / LTE / 5G, BLUETOOTH (registered trademark) communication, Zigbee, THREAD (registered trademark), etc. as needed. In other words, it is more preferable to implement it as a collection of multiple communication means. In other words, the server 2 may communicate various information from the outside via the communication unit 21 and the network.

[0018] The memory unit 22 stores various pieces of information defined above. This can be implemented, for example, as a storage device such as a solid state drive (SSD) that stores various programs and the like related to the server 2 executed by the control unit 23, or as a memory such as a random access memory (RAM) that stores temporarily required information (arguments, arrays, etc.) related to the program operations. The memory unit 22 stores various programs, variables, etc. related to the server 2 executed by the control unit 23.

[0019] The control unit 23 processes and controls the overall operations related to the server 2. The control unit 23 is, for example, a central processing unit (CPU) not shown. The control unit 23 realizes various functions related to the server 2 by reading out predetermined programs stored in the storage unit 22. In other words, information processing by software stored in the storage unit 22 is specifically realized by the control unit 23, which is an example of hardware, and each step related to each function described below can be executed. This will be described in further detail in the next section. Note that the control unit 23 is not limited to being single, and multiple control units 23 may be provided for each function. A combination of these may also be used.

[0020] <Information processing device 3> 3 is a block diagram showing the hardware configuration of the information processing device 3. The information processing device 3 includes a communication unit 31, a storage unit 32, a control unit 33, a display unit 34, an input unit 35, and an output unit 36, and these components are electrically connected via a communication bus 30 inside the information processing device 3. Each component will be further described below. The description of the communication unit 31, the storage unit 32, and the control unit 33 will be omitted as they are the same as the description of each unit in the server 2.

[0021] The communication unit 31 can receive channel state information from the wireless communication device 4. Specifically, the communication unit 31 can receive channel state information from the wireless communication device 4 via wireless communication means such as wireless LAN network communication, mobile communication such as 3G / LTE / 5G, BLUETOOTH (registered trademark) communication, Zigbee, THREAD (registered trademark), etc. The communication unit 31 can also communicate with the wireless communication device 4 via wired communication means. Furthermore, the communication unit 31 can communicate with the server 2, etc. via wired communication means such as USB, IEEE1394, Thunderbolt (registered trademark), wired LAN network communication, etc., or wireless communication means such as wireless LAN network communication, mobile communication such as 3G / LTE / 5G, BLUETOOTH (registered trademark) communication, Zigbee, THREAD (registered trademark), etc.

[0022] The display unit 34 may be included in the housing of the information processing device 3 or may be externally attached. The display unit 34 displays a graphical user interface (GUI) screen that can be operated by the user. This is preferably implemented by selectively using display devices such as a CRT display, a liquid crystal display, an organic EL display, and a plasma display depending on the type of the information processing device 3.

[0023] The input unit 35 may be included in the housing of the information processing device 3 or may be externally attached. The input unit 35 may be configured, for example, with a sound collector such as a microphone, and may collect external sounds and output sound signals indicative of the collected sounds. The sound signals are transferred as command signals to the control unit 33 via the communication bus 30, and the control unit 33 may execute predetermined control or calculations as necessary. Furthermore, the information accepted by the input unit 35 is not limited to the above-mentioned voice and the like. Specifically, the input unit 35 may be configured to be integrated with the display unit 34 and to be capable of accepting input from the user via a touch panel, switch buttons, a mouse, a QWERTY keyboard, or the like.

[0024] The output unit 36 ​​may be included in the housing of the information processing device 3 or may be externally attached. For example, the output unit 36 ​​may be configured with a speaker or the like and output a voice or a signal sound generated by the information processing system 1. Alternatively, for example, the output unit 36 ​​may be configured with a light-emitting means and output light emission based on information generated by the information processing system 1. For example, the output unit 36 ​​may output information generated by the information processing system 1 from an external speaker or light-emitting means. Alternatively, for example, the output unit 36 ​​may cooperate with a voice-assistant-compatible smart home appliance and output information generated by the information processing system 1 as a voice or the like from the smart home appliance.

[0025] A smartphone, a tablet terminal, a personal computer, a wearable device, an IoT device, etc. can be used as the information processing device 3. An IoT device is a device that can communicate via a network.

[0026] <Wireless communication device 4> 4 is a block diagram showing the hardware configuration of the wireless communication device 4. The wireless communication device 4 includes a communication unit 41, a storage unit 42, a control unit 43, an input unit 44, and an output unit 45, and these components are electrically connected via a communication bus 40 inside the wireless communication device 4. Each component will be further described. The description of the communication unit 41, storage unit 42, and control unit 43 will be limited to the parts that are different from the description of each unit in the server 2. The description of the input unit 44 and output unit 45 will be omitted because they are the same as the description of each unit in the information processing device 3.

[0027] The communication unit 41 can transmit and receive various information via a network. The wireless communication device 4 can communicate with the server 2 and the information processing device 3 by wired communication means such as USB, IEEE1394, Thunderbolt (registered trademark), wired LAN network communication, etc., or wireless communication means such as wireless LAN network communication, mobile communication such as 3G / LTE / 5G, BLUETOOTH (registered trademark) communication, Zigbee, THREAD (registered trademark), etc. When there are multiple wireless communication devices 4, the multiple wireless communication devices 4 may communicate with each other. For communication between the multiple wireless communication devices 4, short-range wireless communication such as RFID or NFC may be used.

[0028] The communication unit 41 can transmit and receive information using wireless communication technology. The communication unit 41 includes, for example, a wireless LAN module. A wireless LAN is a type of local area network that provides network connections between devices within a certain range. A wireless LAN can transmit and receive information through wireless communication without requiring physical wiring. The communication unit 41 preferably uses a wireless LAN module, which is a wireless communication technology. The wireless LAN module is an integrated hardware component for providing wireless LAN functionality. The wireless LAN module controls the transmission and reception of wireless signals and performs processing in accordance with the Wi-Fi standard (IEEE 802.11) to communicate with a network. The wireless LAN module has a built-in transceiver and operates in conjunction with an antenna.

[0029] The transceiver is configured to be able to transmit and receive radio signals. The transceiver includes a transmitter that transmits radio signals and a receiver that receives reflected radio signals with high sensitivity. The transmitter can control the strength and pattern of the transmitted signal. It is preferable that the transmitter can transmit a stable signal in a specific frequency band. The receiver may include a high-precision analog-to-digital converter for detailed analysis of changes in the received signal. That is, the transmitter converts digital data into a radio signal and transmits it from the antenna, and the receiver converts the received radio signal into digital data.

[0030] An antenna is a component that transmits and receives wireless signals. An omnidirectional antenna that can receive signals equally from all directions may be used as an antenna. A wireless LAN chip may have an external antenna or a built-in antenna.

[0031] It is recommended that the WLAN module be MIMO (Multiple Input Multiple Output) compatible. Using multiple antennas allows for the reception of changes in the amplitude and phase of WLAN signals traveling through multiple different paths, enabling detailed channel analysis. Using multiple antennas also allows for the detection of signal reflections and the effects of obstacles. Using multiple antennas allows for signals to be transmitted and received from different angles and positions. Using multiple antennas also allows for the acquisition of channel status information, such as signal phase, amplitude, and delay. It is even more preferable to use a WiFi module based on the IEEE 802.11bf standard for WLAN. IEEE 802.11bf is a standard for WiFi-based sensing. IEEE 802.11bf defines technology for detecting environmental changes and motion using WiFi signals.

[0032] The communication unit 41 may be provided with a function for establishing a mesh network with the information processing device 3 or another wireless communication device 4. Specifically, the communication unit 41 includes a mesh network-compatible module and transfers information received from the information processing device 3 or another wireless communication device 4 to another device. The mesh network-compatible module is, for example, a communication module compatible with a Wi-Fi mesh network, a communication module compatible with the Zigbee protocol, or a communication module compatible with the Thread protocol. The communication unit 41 may also include a dual-protocol communication module compatible with both a wireless LAN module and a mesh network-compatible module.

[0033] The memory unit 42 stores various information defined above. This may be implemented, for example, as a storage device such as a solid-state drive (SSD) that stores various programs related to the wireless communication device 4 executed by the control unit 43, or as a memory such as a random access memory (RAM) that stores temporarily required information (arguments, arrays, etc.) related to program calculations. The memory unit 42 stores various programs and variables related to the wireless communication device 4 executed by the control unit 43. The memory unit 42 can also store signals received via the communication unit 41 (i.e., received signals) and signals transmitted via the communication unit 41 (i.e., transmitted signals). The memory unit 42 can also temporarily or long-term store channel state information of wireless LAN signals and data within the mesh network.

[0034] The control unit 43 processes and controls the overall operation of the wireless communication device 4. For example, the control unit 43 may receive signals via the communication unit 41, process the received signals, generate transmission signals, and execute control for signal transmission. The control unit 43 may perform data framing, error checking, retransmission control, encryption, and the like based on the IEEE 802.11 standard. The control unit 43 is, for example, a digital signal processor (DSP) for analyzing changes in the amplitude, phase, and the like of received signals in real time. The DSP processes channel state information (CSI), received signal strength indicator (RSSI), and the like. The control unit 43 is, for example, a central processing unit (CPU). The CPU further analyzes the data processed by the DSP. The control unit 43 realizes various functions related to the wireless communication device 4 by reading out predetermined programs stored in the storage unit 42. That is, information processing by software stored in the storage unit 42 is specifically realized by the control unit 43, which is an example of hardware, to manage data collection from sensors, data preprocessing, data storage in local storage, transmission to the cloud, and the like.

[0035] The information processing system 1 may include only one wireless communication device 4, but preferably includes multiple wireless communication devices 4. When multiple wireless communication devices 4 are used, the information processing system 1 may include multiple wireless communication devices 4a and multiple wireless communication devices 4b.

[0036] The wireless communication device 4a may be a master device and the wireless communication device 4b may be a slave device. The wireless communication device 4a may be configured as a master device to be able to communicate with the server 2 and the information processing device 3 via a telecommunication line (network), and the wireless communication device 4b may be configured as a slave device to be able to communicate with the wireless communication device 4a.

[0037] The wireless communication device 4 may be any device capable of communication, such as a router, smartphone, smart speaker, wearable device, or IoT device. IoT devices include smart home appliances, smart lighting, smart doors, smart locks, smart sensors, smart cameras, and smart plugs. Smart home appliances include refrigerators, washing machines, and air conditioners that can communicate. The wireless communication device 4 may also include a motion sensor, a temperature sensor, a humidity sensor, an infrared sensor, a weight sensor, and the like.

[0038] Using multiple wireless communication devices 4 offers many advantages, including improved spatial resolution, improved measurement accuracy, wider coverage, increased redundancy and reliability, and data collection from different perspectives. When multiple devices are installed in different locations, the environment can be observed from more perspectives, improving spatial resolution. This allows for more precise detection of object position and movement. Combining measurement results from different devices can average out errors and obtain more reliable data. Using multiple devices increases the coverage range and enables sensing of a larger area. Having multiple wireless communication devices 4 improves the reliability of the entire system because, even if one device fails, the other devices can continue to provide data. In addition, increased data redundancy improves the accuracy of sensing results. Devices installed in different locations can collect data from different angles and directions, allowing for more comprehensive information about the environment and objects.

[0039] It is preferable to use the 2.4 GHz, 5 GHz, or 6 GHz radio bands for wireless communication. This allows wireless signals to go around obstructions, reducing the risk of blind spots that can be a problem when using cameras or infrared sensors.

[0040] 2. Functional configuration In this section, the functional configuration of this embodiment will be described. The control unit 23 or the control unit 43 is configured to execute, for example, the following steps. The following steps can be optionally omitted.

[0041] The control unit 23 is configured to be able to acquire information from the wireless communication device 4 or another device as a first acquisition step. Furthermore, the control unit 23 is configured to be able to acquire various pieces of information by reading out various pieces of information stored in a storage area that is at least a part of the memory unit 22 and writing the read out information to a working area that is at least a part of the memory unit 22. The storage area is, for example, an area of ​​the memory unit 22 that is implemented as a storage device such as an SSD. The working area is, for example, an area that is implemented as a memory such as a RAM. The control unit 23 is configured to be able to acquire channel state information transmitted from the wireless communication device 4 as a first acquisition step. Furthermore, the control unit 23 may be configured to be able to acquire converted channel state information from the wireless communication device 4 as a first acquisition step.

[0042] The control unit 23 is configured to be able to acquire information from the information processing device 3 or another device as a second acquisition step. The control unit 23 may be configured to be able to acquire various information related to the subject as a second acquisition step. The control unit 23 may be configured to be able to acquire health information of the subject.

[0043] In the identification step, the control unit 23 identifies motion information based on the channel state information acquired in the first acquisition step. In the identification step, the control unit 23 may be configured to input the motion information into a first processing model and output motion information related to the subject's motion from the first processing model, thereby being able to identify the motion information. In the identification step, the control unit 23 can numerically convert the changes in radio waves calculated from the channel state information into specific motions, and organize the numerically converted specific motions to identify the motion information.

[0044] The first processing model is a trained model that is trained to receive channel state information as input and output motion information of a subject corresponding to the channel state information. The first processing model may be a training model that is trained using a combination of training channel state information and training motion information linked to the training channel state information as training data.

[0045] The first processing model is preferably a processing model that excels in processing time-series data with long-term dependency. Examples of processing models that excel in processing time-series data with long-term dependency include LSTM (Long Short-Term Memory), GRU (Gated Recurrent Unit), Transformer, and Bidirectional RNN. The first processing model is preferably a processing model that performs calculations by combining an LSTM layer and an attention layer.

[0046] The LSTM layer is a type of recurrent neural network (RNN) designed to learn long-term dependencies. It has a gate mechanism (input gate, forget gate, and output gate) that allows it to appropriately retain or forget past information. The attention layer learns the importance of each element in a sequence and focuses processing on the important information. Attention dynamically calculates which parts of the information are important at each point in time and reflects this in the output. This allows focusing on important parts even in long sequences. For example, when channel state information is input to the first processing model, it is passed to the LSTM layer, which learns the long-term dependencies of the channel state information. The output of the LSTM is input to the next attention layer as a sequence of hidden states of the channel state information. The attention layer focuses on specific parts of the sequence data and generates a context vector by taking a weighted average based on the importance at each point in time. The context vector may be input to a fully connected layer, and behavior information may be output as a classification result.

[0047] The control unit 23 inputs channel state information to, for example, a "sitting" action identification model, a "standing" action identification model, and a "standing" action identification model. The "sitting" action identification model is configured to be able to identify "sitting" action information, the "standing" action identification model is configured to be able to identify "standing" action information, and the "standing" action identification model is configured to be able to identify "standing" action information. In this way, it is preferable that the action recognition model uses a learning model trained using as training data a combination of learning channel state information and learning action information for each action linked to the learning channel state information.

[0048] As the identification step, the control unit 23 may input the channel state information to each of a plurality of small movement identification models and cause each of the plurality of small movement identification models to output small movement information. The first processing model may include a plurality of small movement identification models. The plurality of small movement identification models are trained models that are trained so as to receive channel state information as input and output small movement information corresponding to the channel state information, and the small movement information may be information about a portion of the subject's movement.

[0049] As the identification step, the control unit 23 may input the plurality of small movement information output from the plurality of small movement identification models into the movement recognition model, and output the movement information. The first processing model may include a plurality of small movement identification models and a movement recognition model. When the movement recognition model receives the small movement information, which is a specific movement converted into numerical values, it can combine the small movement information to estimate a complex movement and output the movement information. The action recognition model may estimate the meaning of complex movements by comparing and transitioning data before and after.

[0050] In the evaluation step, the control unit 23 inputs the movement information into the second processing model and causes the second processing model to output an evaluation regarding the subject's movement corresponding to the movement information. In the evaluation step, the control unit 23 may input a plurality of pieces of small movement information into the second processing model and cause the second processing model to output an evaluation. If the first processing model includes a plurality of small movement identification models, the control unit 23 may input a plurality of pieces of small movement information output from the plurality of small movement identification models into the second processing model and cause the second processing model to output an evaluation.

[0051] The second processing model is a processing model that receives motion information as input and is capable of outputting an evaluation corresponding to the motion information. The second processing model, for example, calculates the amount of activity of the subject based on the motion information, and can predict the progression of dementia and the level of care required from the amount of activity. Furthermore, the second processing model, for example, can predict the progression of dementia and the level of care required from the pattern of motion based on the motion information.

[0052] The second processing model may be a processing model capable of outputting an evaluation of the subject's physical state corresponding to the motion information.

[0053] The second processing model may be a processing model capable of outputting an evaluation of the subject's physical information corresponding to the motion information associated with a specific disease. When the second processing model is a model that outputs an evaluation of the subject's physical information corresponding to the motion information associated with a specific disease, the evaluation includes detection of a sign related to the specific disease.

[0054] The control unit 23 is configured to be able to generate various information in the generation step. In the generation step, the control unit 23 can generate a proposal regarding an action to be recommended to the subject based on the evaluation. In the generation step, the control unit 23 may refer to a prediction system based on medical knowledge when generating a proposal regarding an action to be recommended to the subject based on the evaluation. The prediction system may accumulate information regarding the relationship between movement information, evaluation results based on the movement information, and dementia or level of care required, or may accumulate information regarding movement information, evaluation results based on the movement information, and movements related to diseases.

[0055] In the generation step, the control unit 23 may generate a proposal using an artificial intelligence module. The artificial intelligence module is configured to receive input from the control unit 23 and return an instructed output. The artificial intelligence module has a trained model constructed by a learning method such as supervised learning, unsupervised learning, or self-supervised learning. Supervised learning performs machine learning using training data (training data). The training data is composed of pairs of input data for learning and output data (correct answer data). Furthermore, the language model may not only be one trained for a specific task, but also a general-purpose model that can be used for a wide range of tasks.

[0056] The artificial intelligence module may be an AI (Artificial Intelligence) equipped with a language model such as a Transformer including GPT (Generative Pretrained Transformer, including GPT-1, GPT-2, GPT-3, and GPT-4), BERT (Bidirectional Encoder Representations from Transformers), and BART (Bidirectional and Auto-regressive Transformer), or a Recurrent Neural Network (RNN).

[0057] The artificial intelligence module includes, as artificial intelligence, a general-purpose natural language processing learning model such as a large-scale language model (LLM) that has learned a huge amount of data. Such a general-purpose learning model includes a language model that can handle various tasks without fine tuning using one-shot learning, few-shot learning, etc. Furthermore, a general-purpose learning model can also handle various tasks using zero-shot learning. The artificial intelligences used in the control unit 23 may be separate learning models or may be a common general-purpose learning model.

[0058] More typically, the artificial intelligence module may have a large-scale language model. That is, the artificial intelligence module may generate suggestions based on evaluations based on the large-scale language model. Note that the large-scale language model is a deep learning model that pre-trains a language model that models human spoken language based on its occurrence probability from a huge amount of data. Note that the models that the artificial intelligence module can have are not limited to the above.

[0059] The control unit 23 presents various pieces of information as a presentation step. The information can be presented to the user via the display unit 34 or another device. For example, the control unit 23 is configured to be able to present a suggestion as a presentation step. The control unit 23 may cause the information processing device 3 to display the suggestion as a presentation step. In such a case, for example, the control unit 23 may control the display unit 34 to display visual information such as a screen, an image including a still image or a video, an icon, or a message. The control unit 23 may present the suggestion by voice as a presentation step.

[0060] The control unit 23 manages various information as a management step. The control unit 23 may be configured to be able to manage acquired channel state information as a management step. The control unit 23 may be configured to be able to manage acquired operation information as a management step. The control unit 23 may be configured to be able to manage acquired information about the subject as a management step. The control unit 23 may be configured to be able to manage acquired evaluations as a management step. The control unit 23 may manage operation information in association with the evaluations as a management step. Furthermore, part of the management steps may be executed by the control unit 33 or the control unit 43.

[0061] As a display control step, the control unit 23 controls the display unit 34 to display visual information such as a screen, an image including a still image or a video, an icon, a message, etc. The control unit 23 may generate only rendering information for displaying the visual information on the display unit 34. As a display control step, the control unit 23 may display the visual information in a manner visible to the user. As a display control step, the control unit 23 may display a transition of the evaluation so that the user can view it. As a display control step, the control unit 23 may display visual information generated based on the operation information so that the visual information can be viewed.

[0062] The control unit 43 is configured to be able to convert the channel state information under predetermined conditions as a conversion step. As a conversion step, the control unit 43 can perform data compression, dimensionality reduction, extraction using an amplified autocorrelation function, sampling, etc. on the channel state information. Note that the control unit 43 may be configured to execute part of the processing of the conversion step.

[0063] The control unit 43 may compress the data of the channel state information as a conversion step. By compressing the data, redundancy of the data can be reduced and the data volume can be reduced. As a conversion step, the control unit 43 may compress the data by deleting some information from the data.

[0064] The control unit 43 may perform a dimensionality reduction of the channel state information as a transformation step. Reducing the number of features of the data can reduce the data volume. The control unit 43 may perform a linear transformation of the data in a direction that maximizes the variance of the data as a transformation step, thereby reducing the dimension of the principal components. The control unit 43 may also perform a dimensionality reduction of the data by selecting only important features from the original data as a transformation step. The control unit 43 may also perform a transformation step by using a neural network to learn a low-dimensional representation of the data, thereby retaining only important information in the process of encoding (compressing) and decoding (reconstructing) the data.

[0065] As a conversion step, the control unit 43 may extract characteristics of dynamic objects from the channel state information using an amplified autocorrelation function (A-ACF). An autocorrelation function is a function that measures the correlation between different time points in a time series data set. This allows the degree to which data is temporally related to one another to be evaluated. The amplified autocorrelation function is processed based on the autocorrelation function to emphasize specific dynamic characteristics or periodicity, and can be used to emphasize and detect the characteristics of specific dynamic objects. The amplified autocorrelation function can highlight information related to the movement or changes of dynamic objects, particularly in the analysis of dynamic objects. As a conversion step, the control unit 43 may extract characteristics of dynamic objects from the channel state information using an amplified autocorrelation function (A-ACF) that reflects only the characteristics of the dynamic objects, and then perform dimensionality reduction. As a preprocessing step for dimensionality reduction, by extracting characteristics of dynamic objects using the A-ACF, dimensionality reduction can be performed while maintaining the periodicity and patterns of the data.

[0066] As a conversion step, the control unit 43 may perform sampling to select some data points from the channel state information. By sampling, the data volume can be reduced. As a conversion step, the control unit 43 may reduce the data volume by selecting data at regular intervals along the time axis. Furthermore, as a conversion step, the control unit 43 may thin out data in areas with little fluctuation and retain data in areas with significant fluctuation.

[0067] 3. Information processing flow This section describes the flow of an information processing method executed by the information processing system 1. As shown below, the information processing method includes each step executed by the information processing system. The information processing program of this embodiment causes a computer to execute each step of the information processing system. Note that the order of the processes can be changed as appropriate, multiple processes may be executed simultaneously, or some processes may be omitted.

[0068] 3.1 Overview 5 is a flowchart showing an outline of processing executed by the information processing system 1. In this processing, first, as a first acquisition step, the control unit 23 acquires channel state information transmitted from a wireless communication device (step S001). Next, as an identification step, the control unit 23 inputs the channel state information into a first processing model and causes the first processing model to output motion information related to the motion of the subject (step S002). Next, as an evaluation step, the control unit 23 inputs the motion information into a second processing model and causes the second processing model to output an evaluation related to the motion of the subject corresponding to the motion information (step S003).

[0069] In summary, an information processing system according to one embodiment includes at least one processor. The processor is configured to execute the following steps by reading a program. In a first acquisition step, the control unit 23 acquires channel state information transmitted from the wireless communication device. In an identification step, the control unit 23 inputs the channel state information to a first processing model and causes the first processing model to output motion information related to the motion of the subject. In an evaluation step, the control unit 23 inputs the motion information to a second processing model and causes the second processing model to output an evaluation related to the motion of the subject corresponding to the motion information. Here, the first processing model is a trained model that receives channel state information as input and is trained to be able to output motion information of the subject corresponding to the channel state information, and the second processing model is a processing model that receives motion information as input and is able to output an evaluation corresponding to the motion information. With this aspect, it is possible to identify the motion of the subject based on the channel state information transmitted from the wireless communication device 4 and output an evaluation according to the motion of the subject.

[0070] 3.2 Specific examples An example of a mechanism by which the wireless communication device 4 collects channel state information will be described below with reference to FIGS. 6 and 7. A wireless communication device 4a is installed in room A, and a wireless communication device 4b is installed in room B. There is a door between rooms A and B. The wireless communication device 4b transmits a WiFi signal. FIG. 6 shows an example of the WiFi signal when the door between rooms A and B is closed. FIG. 7 shows an example of the WiFi signal when the door between rooms A and B is open. The WiFi signal transmitted from the wireless communication device 4b experiences reflection, refraction, and scattering as it passes through the surrounding environment, and therefore the strength and phase of the WiFi signal change depending on whether the door is open or closed.

[0071] Channel State Information (CSI) is information about the state of a communication channel that is communicated from a receiver to a transmitter in wireless communications. CSI details the characteristics of the communication path from the transmitter to the receiver. CSI includes information about the strength and phase changes of a WiFi signal.

[0072] The wireless communication device 4a receives a WiFi signal transmitted by the wireless communication device 4b. The WiFi signal is divided into multiple subcarriers using Orthogonal Frequency-Division Multiplexing (OFDM) technology and then transmitted. The multiple subcarriers refer to multiple narrow bandwidths obtained by dividing a wide bandwidth. The received signal pattern changes depending on the surrounding environment. WiFi communication (especially standards such as IEEE 802.11ac and 802.11ax) uses tens to hundreds of subcarriers.

[0073] WiFi sensing is a sensing technology that can detect environmental changes and movements using WiFi signals. Analyzing channel state information from multiple subcarriers enables human movement tracking, gesture recognition, object detection, and indoor monitoring. Wireless sensing is a sensing technology that uses wireless communication signals. Detailed movement detection of people and objects in remote indoor environments, previously achieved using cameras or wearables, can now be achieved simply by installing a wireless communication device, with high accuracy and excellent privacy protection. Wireless sensing is expected to be applied to monitoring the elderly and children, operational support for elderly care facilities, security services for companies and homes, health management services, and usage monitoring services for conference rooms and accommodation facilities. Wireless sensing is expected to be used in restrooms, construction sites, educational settings such as nurseries and schools, entertainment, drone control, vehicle abandonment detection, energy conservation, and home automation.

[0074] WiFi signals have the advantage of penetrating walls and obstacles, working effectively in both visible and obstructed environments, and maintaining performance in low-light conditions regardless of the time of day. Furthermore, WiFi signals offer a higher level of privacy protection compared to video camera capture. Compared to radar technology, WiFi devices are much more cost-effective and commonly integrated into a variety of commercial products, making them easy to use in a variety of environments. This makes WiFi-based motion detection useful for security, smart home technology, health monitoring applications, and more.

[0075] The details of the above information processing will be described below as an example with reference to FIGS.

[0076] FIG. 8 is a floor plan 9 showing an example of installation locations of the wireless communication devices 4. The floor plan 9 is the layout of a room in which the wireless communication devices 4 are installed. The floor plan 9 includes one bedroom and one toilet. In the floor plan 9, as an example, the wireless communication device 4a is installed in the bedroom, the wireless communication device 4b1 is installed beside the bed in the bedroom, and the wireless communication device 4b2 is installed in the toilet. The wireless communication device 4a, the wireless communication device 4b1, and the wireless communication device 4b2 each form an oval sensing area. In the floor plan 9, the wireless communication device 4a and the wireless communication device 4b1 are installed in the lower left area and the wireless communication device 4b2 is installed in the upper right area, thereby making it possible to sense all of the rooms in the floor plan 9.

[0077] FIG. 9 is an activity diagram showing an example of the flow of processing executed by the information processing system 1. This example of the flow may fall within the scope defined in the above-mentioned overview. The following description will be given along with each activity in this activity diagram. Note that the information processing may include any exception handling not shown. Exception handling includes the interruption of the information processing or the omission of each process. Selections or inputs made in the information processing may be based on user operation or may be made automatically without user operation.

[0078] The control unit 43 of the wireless communication device 4a acquires the channel state information transmitted by the wireless communication device 4a and the wireless communication device 4b (activity A101).

[0079] Subsequently, the control unit 43 may convert the channel state information under predetermined conditions as a conversion step (activity A102). In the wireless communication device 4a, the control unit 43 performs a conversion process on the channel state information as a conversion step, thereby adjusting the data format and data quality of the channel state information. Furthermore, in the wireless communication device 4a, the control unit 43 performs a conversion process to compress the data of the channel state information as a conversion step, thereby reducing the load on the server 2.

[0080] In the transformation step, the control unit 43 preferably performs data compression or dimensionality reduction processing on the channel state information under predetermined conditions. In the transformation step, the control unit 43 preferably extracts useful features from the channel state information using an autocorrelation function. In the transformation step, the control unit 43 preferably extracts useful features from the channel state information by extracting an amplified autocorrelation function that reflects only the characteristics of dynamic objects. This aspect can reduce the influence of environmental factors such as scattering / reflection from static objects such as furniture and building structures. This can reduce the need for time-consuming data collection and retraining when using the system in a new environment.

[0081] Next, the control unit 43 of the wireless communication device 4a transmits the channel state information to the control unit 23 of the server 2 (activity A103). The control unit 43 may transmit the converted channel state information to the control unit 23 of the server 2.

[0082] Next, as a first acquisition step, the control unit 23 acquires the channel state information transmitted from the wireless communication device 4a via the network (activity A104). Here, if the channel state information has been converted in activity A102, the control unit 23 can acquire the converted channel state information as the first acquisition step.

[0083] As a specifying step, the control unit 23 inputs the channel state information into the first processing model. The first processing model may be a model that specifies a plurality of small actions (activity A105).

[0084] The first processing model is a trained model that is trained so as to receive channel state information as input and output motion information of a subject corresponding to the channel state information.

[0085] The motion information is information about a motion of a subject that corresponds to the channel state information and is identified based on the channel state information. The subject is a human or an animal. The motion of the subject may be information about the subject's motion, such as "sitting," "standing up," "walking," or "falling." The motion information of the subject may include, for example, the presence or absence of motion, the magnitude of the motion, the location of the motion, the breathing rate, specific motion, etc.

[0086] However, when each piece of motion information is broken down into smaller motions, small motions common to various motions may be included. Therefore, as an identification step, the control unit 23 may input channel state information to multiple small motion identification models and output multiple pieces of small motion information. As an identification step, the control unit 23 may identify information on small motions obtained by breaking down the motion information, and then recognize the motion information by combining the identified small motion information. The first processing model may include a small motion identification model that identifies small motion information and a motion recognition model that recognizes motions from combinations of small motion information, etc. This aspect makes it possible to identify a wider variety of motions than when motions are identified using only a motion identification model.

[0087] FIG. 10 is a diagram showing an example of the flow of information by the information processing system 1. The control unit 23 inputs channel state information to the A action identification model, the B action identification model, the C action identification model, and the D action identification model. The A action identification model is a trained model that has been trained to be able to output A action information, the B action identification model is B action information, the C action identification model is C action information, and the D action identification model is D action information. Next, the control unit 23 can input the A to D action information, which is the identified small action information, into the action recognition model. The control unit 23 can acquire the action information output from the action recognition model. The control unit 23 can obtain an evaluation result by inputting the output action information into an evaluation system.

[0088] FIG. 11 is a diagram showing an example of the relationship between identified small movement information and recognized movements. If the identified small movement is a repetition of movement A, movement B, and movement A, the recognized movement will be "continuous sitting." On the other hand, if the identified small movement is a sequence of movement A, movement B, movement C, and movement D, the recognized movement will be "standing up." Movements A and B are common to both continuing to sit and standing up. By dividing a movement into small movements in this way, it is possible to capture more partial characteristics of the movement, thereby improving detection accuracy.

[0089] In other words, the first processing model may include a plurality of small action identification models, which are trained models that are trained to receive channel state information as input and output small action information corresponding to the channel state information, and the small action information may be information about a portion of the subject's action. In the identification step, the control unit 23 may input the channel state information to each of a plurality of small movement identification models and output small movement information from each of the plurality of small movement identification models. The movement information may be, for example, standing, sitting, rising, walking, etc., or may be small movements (small movements) obtained by breaking down large movements such as standing, sitting, rising, and walking.

[0090] Next, the control unit 23 acquires the movement information related to the movement of the subject output from the first processing model. If the first processing model is a plurality of small movement identification models, the control unit 23 may acquire the plurality of small movement information output from the small movement identification models (activity A106).

[0091] Next, the control unit 23 may input multiple pieces of small movement information to the movement recognition model as an identification step (activity A107). The movement recognition model is a processing model that receives multiple pieces of small movement information as input and is capable of outputting movement information based on the multiple pieces of small movement information. When multiple pieces of small movement information are input, the movement recognition model outputs movement information. The movement recognition model is preferably a model that can recognize movement information by combining multiple pieces of small movement information. Because common small movement information can be used to identify multiple pieces of movement information, it becomes possible to identify more complex movements with fewer movement identification models. The movement recognition model may identify more advanced and complex movements, for example, by combining small movement information as parallel simultaneous processing or chronological serial processing.

[0092] In activity A107, the control unit 23 may further combine the movement information output from the movement recognition model in a time series. For example, the control unit 23 may recognize that a "standing up" movement is performed after a "sitting down" movement, and then a "walking movement" is performed after that. Furthermore, the control unit 23 may store the movement information identified by the movement recognition model in association with subject information. When storing the movement information in association with subject information, the control unit 23 may store the time of occurrence of the movement information, the duration of the movement information, etc. together with the identified movement information.

[0093] Subsequently, the control unit 23 can acquire the motion information regarding the motion of the subject output from the motion recognition model (activity A108).

[0094] Subsequently, the control unit 23 may acquire information about the subject via the information processing device 3 (activity A109).

[0095] Next, the control unit 23 inputs the movement information to the second processing model as an evaluation step. The control unit 23 may input a plurality of pieces of small movement information to the second processing model as an evaluation step. The second processing model may be, for example, an evaluation system that receives movement information as input and outputs an evaluation corresponding to the movement information. The control unit 23 may input subject information about the subject to the evaluation system along with the movement information (activity A110).

[0096] In the evaluation step, the control unit 23 tally and analyzes the movement information based on the time when the movement information occurred, the duration of the movement information, the number of times or frequency of occurrence of the movement information in a given period, the total number of occurrences in a given period, etc. Based on the analysis results of the movement information, the control unit 23 may evaluate the presence or absence of activity, the amount of activity, lifestyle patterns, frequency of going out, etc. The control unit 23 may perform the evaluation using an evaluation system capable of performing these evaluations.

[0097] The evaluation may be a quantification of the elderly person's living conditions and health conditions.

[0098] In activity A110, the control unit 23 may input, as the second processing model, a processing model capable of outputting an evaluation of the subject's physical condition corresponding to the movement information. In this manner, the control unit 23 can acquire an evaluation of the subject's physical condition. For example, the control unit 23 can calculate the subject's activity amount based on the movement information, and predict the progression of dementia and the level of care required from the activity amount. Furthermore, for example, the control unit 23 can predict the progression of dementia and the level of care required from the movement pattern based on the movement information.

[0099] Subsequently, the control unit 23 may acquire an evaluation output from an evaluation system, which is an example of the second processing model. The evaluation is, for example, an evaluation regarding the subject's behavior corresponding to the behavior information (activity A111).

[0100] Furthermore, in activity A110, the control unit 23 may input, as the second processing model, a processing model capable of outputting an evaluation of the subject's physical information corresponding to the movement information associated with a predetermined disease. The evaluation may include detection of a sign of the predetermined disease. In this manner, the control unit 23 can obtain an evaluation of the subject's physical information corresponding to the movement information associated with the predetermined disease. The control unit 23 may detect a sign of Parkinson's disease, for example, by determining whether or not there is small movement information specific to Parkinson's disease.

[0101] Subsequently, the control unit 23 can associate the output evaluation with the subject information, store it in a database, and manage it (activity A112).

[0102] Next, as a generation step, the control unit 23 can generate a proposal regarding an action recommended for the subject based on the evaluation (activity A113). The control unit 23 may input the evaluation to a proposal system and generate a proposal recommended for the subject based on the evaluation. The control unit 23 may generate a proposal for the subject based on the acquired evaluation results, such as whether the subject is active, the amount of activity, lifestyle patterns, and frequency of going out. The control unit 23 may also generate a proposal based on evaluation results, such as a prediction of the progression of dementia or a prediction of the level of care required.

[0103] When the control unit 23 generates a suggestion regarding an action to be recommended to the subject based on the evaluation in the generation step, the suggestion may be generated using a large-scale language model.

[0104] In activity A109, the control unit 23 may acquire health information of the subject as a second acquisition step, and the control unit 23 may generate a proposal based on the evaluation and the health information as a presentation step. In this manner, the control unit 23 can generate a proposal according to the health condition of the subject.

[0105] Next, as a presentation step, the control unit 23 transmits the generated proposal to the information processing device 3 via the network (activity A114). The information processing device 3 can present the received proposal.

[0106] The control unit 23 may present the suggestion by voice via the output unit 36 ​​of the information processing device 3. The control unit 23 may also present the suggestion by voice via the output unit 45 of the wireless communication device 4. By having a dialogue with the subject by voice, it is possible to promote activities for maintaining and improving the subject's QOL.

[0107] Here, in the presentation step, the control unit 23 may present the proposal to an information processing device 3 owned by a user other than the subject. For example, the control unit 23 may present the proposal to an information processing device 3 owned by a family member of the subject. Furthermore, for example, the control unit 23 may present the proposal to an information processing device 3 of a medical professional related to the subject.

[0108] Subsequently, the control unit 23 may receive feedback regarding the presented evaluation and proposal via the output unit 36 ​​of the information processing device 3. The control unit 23 may receive the feedback via an information processing device 3 owned by the subject, an information processing device 3 owned by the subject's family, or an information processing device 3 of a medical professional related to the subject. For example, when the control unit 23 presents an evaluation result that the subject is trying to stand up in activity A114, the information processing device 3 may receive feedback regarding whether the evaluation result was correct or not and transmit the feedback to the control unit 23.

[0109] Subsequently, the control unit 23 can update the first processing model or the second processing model based on the received feedback. In this manner, the first processing model can output more accurate motion information regarding the subject's motion, and the second processing model can output more accurate evaluations corresponding to the motion information.

[0110] 4. Variations Furthermore, the following aspects may be adopted: The above-described information processing aspects are merely examples, and the present invention is not limited to these, and can be modified as appropriate within the scope of the technical concept of the invention.

[0111] The control unit 23 may manage the output evaluations in chronological order as a management step. Furthermore, the control unit 23 may cause the information processing device 3 to display the transition of the evaluations in a manner that is visible to the user as a display control step. For example, the control unit 23 may cause the information processing device 3 to display the presence or absence of activity, the amount of activity, lifestyle patterns, frequency of going out, etc. over time. Furthermore, the control unit 23 may cause the information processing device 3 to display the time of occurrence of the operation information, the duration of the operation information, the number of occurrences and frequency of the operation information in an arbitrary period, the total number of occurrences in an arbitrary period, etc.

[0112] The control unit 23 may manage the operation information in association with the evaluation as a management step. Furthermore, the control unit 23 may visibly display visual information generated based on the operation information as a display control step. For example, the control unit 23 may cause the information processing device 3 to display a graph or table including the time of occurrence of the operation information, the duration of the operation information, the number of occurrences or frequency of the operation information in a given period, the total number of occurrences in a given period, etc.

[0113] In activity A106, the control unit 23 may input a plurality of pieces of small movement information to an evaluation system, which is an example of the second processing model, as an evaluation step. In this manner, evaluation can be performed focusing on specific small movements of the subject. The control unit 23 can perform evaluation based on small movements specific to Parkinson's disease, for example.

[0114] The overall configuration shown in Fig. 1 is an example and is not limited to this. For example, the server 2 may be distributed across two or more devices, or may be replaced by a cloud computing system. Furthermore, all processing may be performed by the server 2, or all processing may be performed by the information processing device 3. An application may be installed on the information processing device 3, and the information processing device 3 and the server 2 may work together to execute the processing described above.

[0115] The server 2 may be an on-premise server or a cloud server. The cloud server 2 may provide the above functions and processes in the form of, for example, SaaS (Software as a Service) or cloud computing.

[0116] In the above embodiment, the server 2 performs various storage and control operations, but multiple external devices may be used instead of the server 2. That is, various information and programs may be distributed and stored in multiple external devices using block chain technology or the like.

[0117] It may be provided in the following manner.

[0118] (1) An information processing system comprising at least one processor, the processor being configured to execute each of the following steps by reading a program: a first acquisition step acquiring channel state information transmitted from a wireless communication device; an identification step inputting the channel state information into a first processing model and causing the first processing model to output motion information regarding the motion of a subject; and an evaluation step inputting the motion information into a second processing model and causing the second processing model to output an evaluation regarding the motion of the subject corresponding to the motion information, wherein the first processing model is a trained model that receives the channel state information as input and is trained to be able to output the motion information of the subject corresponding to the channel state information, and the second processing model is a processing model that receives the motion information as input and is able to output the evaluation corresponding to the motion information.

[0119] (2) In the information processing system described in (1) above, the first processing model includes a plurality of small movement identification models, each of which is a trained model that has been trained to receive the channel state information and output small movement information corresponding to the channel state information, and the small movement information is information regarding a portion of the subject's movement, and in the identification step, the channel state information is input to each of the plurality of small movement identification models, and the small movement information is output from each of the plurality of small movement identification models, and in the evaluation step, the plurality of pieces of small movement information are input to the second processing model, and the evaluation is output.

[0120] (3) In the information processing system described in (1) or (2) above, the identification step includes a plurality of small movement identification models and an action recognition model, and in the identification step, the channel state information is input to the plurality of small movement identification models and a plurality of pieces of small movement information is output, and the plurality of small movement information is input to the action recognition model and a plurality of pieces of small movement information is output, each of the plurality of small movement identification models is a trained model that has been trained to be able to input the channel state information and output small movement information corresponding to the channel state information, and the small movement information is information about a part of the subject's movement, and the action recognition model is a processing model that is able to input a plurality of pieces of small movement information and output the movement information based on the plurality of pieces of small movement information.

[0121] (4) In the information processing system described in any one of (1) to (3) above, the second processing model is a processing model capable of outputting the evaluation of the subject's physical state corresponding to the motion information.

[0122] (5) In the information processing system described in any one of (1) to (4) above, the second processing model is a processing model capable of outputting the evaluation of the subject's physical information corresponding to the motion information associated with a specified disease, and the evaluation includes detection of signs related to the specified disease.

[0123] (6) An information processing system according to any one of (1) to (5) above, further comprising, in the generation step, generating a suggestion regarding a behavior recommended for the subject based on the evaluation, and, in the presentation step, presenting the suggestion.

[0124] (7) In the information processing system described in (6) above, the second acquisition step further acquires health information of the subject, and the presentation step presents the suggestion generated based on the assessment and the health information.

[0125] (8) In the information processing system described in (7) above, in the presenting step, the suggestion is presented by voice.

[0126] (9) In the information processing system described in (7) or (8) above, in the presenting step, the suggestion is presented to a user other than the subject.

[0127] (10) In the information processing system described in any one of (1) to (9) above, the management step further manages the outputted evaluations in chronological order, and the display control step further displays the progress of the evaluations in a manner that is visible to the user.

[0128] (11) In the information processing system described in (10) above, in the management step, the operation information is managed in correspondence with the evaluation, and in the display control step, visual information generated based on the operation information is displayed in a visible manner.

[0129] (12) In the information processing system described in any one of (1) to (11) above, the system further includes, in the conversion step, converting the channel state information under predetermined conditions, and in the first acquisition step, acquiring the converted channel state information.

[0130] (13) An information processing method, comprising steps executed by the information processing system according to any one of (1) to (12) above.

[0131] (14) A program for causing a computer to execute each step of the information processing system described in any one of (1) to (12) above. Of course, this is not the case.

[0132] Finally, while various embodiments of the present invention have been described, these are presented by way of example only and are not intended to limit the scope of the invention. The novel embodiments may be embodied in various other forms, and various omissions, substitutions, and modifications may be made without departing from the spirit of the invention. Such embodiments and modifications are intended to be included within the scope and spirit of the invention, as well as within the scope of the inventions and their equivalents as defined in the accompanying claims. [Explanation of symbols]

[0133] 1: Information processing system 2: Server 20: Communication bus 21: Communications Department 22: Storage section 23: Control section 3: Information processing equipment 30: Communication bus 31: Communications Department 32: Storage section 33: Control section 34:Display section 35: Input section 36: Output section 4: Wireless communication devices 4a: Wireless communication device 4b1: Wireless communication devices 4b2: Wireless communication devices 40: Communication bus 41: Communications Department 42: Storage section 43: Control section 44: Input section 45: Output section 9: Floor plan A: Room B: Room

Claims

1. An information processing system, at least one processor; The processor is configured to execute the following steps by reading the program: In a first obtaining step, channel state information transmitted from a wireless communication device is obtained; In the identification step, the channel state information is input to a first processing model, and small movement information relating to a part of the subject's movement is output from each of a plurality of small movement identification models constituting the first processing model; In the evaluation step, a plurality of pieces of small movement information are input to a second processing model, and an evaluation regarding the movement of the subject is output from the second processing model, wherein: the first processing model includes a plurality of small action specific models; each of the plurality of small action identification models is a trained model that is trained to receive the channel state information as an input and to be able to output the small action information corresponding to the channel state information; The second processing model is a processing model that receives the plurality of pieces of small action information as input and is capable of outputting the evaluation corresponding to the plurality of pieces of small action information.

2. An information processing system, at least one processor; The processor is configured to execute the following steps by reading the program: In a first obtaining step, channel state information transmitted from a wireless communication device is obtained; In the identifying step, the channel state information is input to a first processing model; a process of inputting the channel state information into each of a plurality of small action identification models constituting the first processing model, and causing each of the plurality of small action information relating to a part of the subject's action to be output, and inputting the plurality of small action information into an action recognition model, and causing each of the plurality of small action information to be output, the process of In the evaluation step, the motion information is input to a second processing model, and an evaluation regarding the motion of the subject corresponding to the motion information is output from the second processing model; wherein the first processing model includes a plurality of small action identification models and the action recognition model; Each of the plurality of small movement identification models is a trained model that is trained to receive the channel state information as an input and output small movement information corresponding to the channel state information, and the small movement information is information about a part of the movement of the subject; the action recognition model is a processing model that receives the plurality of pieces of small action information as input and is capable of outputting the action information based on the plurality of pieces of small action information; The second processing model is a processing model that receives the motion information as input and is capable of outputting the evaluation corresponding to the motion information.

3. 2. The information processing system according to claim 1, The system, wherein the second processing model is a processing model capable of outputting the assessment of the subject's physical state corresponding to the plurality of small movement information.

4. 2. The information processing system according to claim 1, the second processing model is a processing model capable of outputting the evaluation of the physical information of the subject corresponding to the plurality of pieces of small movement information associated with a predetermined disease, The system, wherein the evaluation includes detecting a prognosis for the predetermined disease.

5. 2. The information processing system according to claim 1, Furthermore, in the generating step, a suggestion regarding a recommended action for the subject is generated based on the evaluation; Furthermore, in the presenting step, the system presents the proposal.

6. In the information processing system according to claim 5, Furthermore, in the second acquisition step, health information of the subject is acquired; In the presenting step, the system presents the suggestion generated based on the assessment and the health information.

7. In the information processing system according to claim 6, In the presenting step, the system presents the suggestion by voice.

8. In the information processing system according to claim 6, In the presenting step, the system presents the proposal to a user other than the subject.

9. 2. The information processing system according to claim 1, Furthermore, in the management step, the output evaluations are managed in chronological order, Furthermore, in the display control step, the system displays the transition of the evaluation so that it can be visually recognized by the user.

10. In the information processing system according to claim 9, In the management step, the plurality of pieces of small action information are managed in association with the evaluation; In the display control step, visual information generated based on the plurality of pieces of small action information is displayed in a visibly recognizable manner.

11. 2. The information processing system according to claim 1, Furthermore, in the conversion step, the channel state information is converted under a predetermined condition; In the first obtaining step, the converted channel state information is obtained.

12. An information processing method, comprising: An information processing method comprising the steps executed by the information processing system according to any one of claims 1 to 11.

13. A program, A program for causing a computer to execute each step of the information processing system according to any one of claims 1 to 11.

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