A smart sole structure for measuring ground reaction force, and a method for integrating the coordinates of foot position and movement.

Abstract

This invention provides a method and system for performing pressure profiling and coordinate integration. [Solution] The pressure profiling method includes: receiving raw data from multiple sensors incorporated into a pair of shoes worn by the user using a processor; preprocessing the raw data using the processor to generate preprocessed data; calculating the user's ground reaction force using the preprocessed data using the processor; and using the ground reaction force derived from the preprocessed data to perform at least one of the following: generating posture adjustment recommendations, generating reports, and performing additional data analysis using the preprocessed data.

Description

Technical Field

[0001] The present disclosure generally relates to methods and systems for performing pressure profiling and coordinate integration.

Background Art

[0002] Measurement of foot pressure is important for a number of reasons, such as wound prevention, gait monitoring, and treatment. Conventional smart insoles and shoes are capable of measuring plantar pressure or the vertical force under the foot. However, in order to understand a user's musculoskeletal system, it is essential to measure the user's ground reaction force (GRF), which has not been done with conventional smart insoles and shoes.

[0003] In the prior art, a method of measuring the shear component of force using one or more sensors attached above the insole of a shoe has been disclosed. Although the method discloses tracking of horizontal forces, the overall shear force acting on the body has not been measured or calculated. The overall shear force acting on the body results from the combination of the shear force under the foot and other shear forces applied by the shoe body to other parts of the foot (e.g., the sides or the top of the foot), and is greater than the horizontal force measured at the insole level.

[0004] Currently, there is no technology for measuring the shear force (horizontal force) under the foot. Specifically, the prior art has focused only on the pressure distribution directly under the human foot. No clear quantitative measurement has been made of the ground reaction force (GRF) between the foot and the ground. Quantifying GRF using a smart insole opens the way to a comprehensive assessment of the human musculoskeletal system in a natural environment. Such data and analysis not only provide important insights into industrial safety but also yield insights into human engineering during work.

[0005] A multi-layered insole capable of measuring ground reaction force (GRF) between the foot and the ground is needed. The measured GRF can be used to monitor and analyze ergonomics in industrial work and prevent potential injuries.

[0006] Furthermore, while conventional smart insoles and shoes can measure plantar pressure or vertical force beneath the sole and report it in the local coordinate system of each foot's insole, information such as the relative position of the foot is crucial for a more comprehensive assessment of posture and the musculoskeletal system. Currently, there is no technology that can measure plantar pressure and force and report them in a single proper coordinate system to perform such an assessment.

[0007] Conventional technologies disclose methods for tracking the position and movement of each foot using sensors such as IMUs (Inertial Measurement Units). However, this method cannot establish a correlation between the two feet, nor can it further track or analyze that correlation.

[0008] A method is needed that can report the relative position of each insole within a specific coordinate system. The information obtained from this specific coordinate system can be used to calculate the precise location of the overall center of pressure (COP) originating from both feet, which is crucial for conducting a comprehensive biomechanical analysis of the entire body. [Overview of the project]

[0009] Aspects of this disclosure relate to an innovative method for performing pressure profiling. The method may include the steps of: receiving raw data from a plurality of sensors incorporated into a pair of shoes worn by a user; preprocessing the raw data with the processor to generate preprocessed data; calculating the user's ground reaction force using the preprocessed data with the processor; and using the ground reaction force derived from the preprocessed data, performing at least one of the following with the processor: generating posture adjustment recommendations, generating reports, and performing additional data analysis.

[0010] Aspects of this disclosure relate to a non-temporary computer-readable medium for storing instructions for performing pressure profiling. The instructions may include steps of: receiving raw data from a plurality of sensors incorporated into a pair of shoes worn by a user; preprocessing the raw data to generate preprocessed data; calculating the user's ground reaction force using the preprocessed data; and using the ground reaction force derived from the preprocessed data to perform at least one of the following: generating posture adjustment recommendations, generating reports, and performing additional data analysis.

[0011] Aspects of this disclosure relate to an innovative server system for performing pressure profiling. The system includes memory and a processor that communicates with the memory, the processor being configured to receive raw data from a plurality of sensors embedded in a pair of shoes worn by a user, to preprocess the raw data to generate preprocessed data, to use the preprocessed data to calculate the user's ground reaction force, and to use the ground reaction force derived from the preprocessed data to perform at least one of the following: generating posture adjustment recommendations, generating reports, and performing additional data analysis.

[0012] Aspects of this disclosure relate to an innovative system for performing pressure profiling. The system includes a memory and a processor that communicates with the memory, the processor being configured to receive raw data from a plurality of sensors embedded in a pair of shoes worn by a user, to preprocess the raw data to generate preprocessed data, to use the preprocessed data to calculate the user's ground reaction force, and to use the ground reaction force derived from the preprocessed data to perform at least one of the following: generating posture adjustment recommendations, generating reports, and performing additional data analysis.

[0013] Aspects of this disclosure relate to an innovative method for performing coordinate integration. The method may include the steps of: a processor receiving data from a plurality of sensors incorporated in a pair of shoes while a user is performing one or more standardized actions; the processor determining the relative position of the user's feet based on the data; the processor generating an integrated coordinate model from the relative position of the feet; and the processor calculating the position of the user's overall center of pressure (CoP) based on the integrated coordinate model.

[0014] In some embodiments, the method may further include a step of a processor determining the orientation of the user's feet based on the data, and the processor is configured to generate an integrated coordinate model from both the relative position and orientation of the feet.

[0015] In some embodiments, the method may further include a step in which the processor performs at least one of the following using the position of the user's overall center of pressure (CoP): generating posture adjustment recommendations, generating an assessment report, and generating rehabilitation recommendations.

[0016] Aspects of this disclosure relate to a non-temporary computer-readable medium for storing instructions for performing coordinate integration. The instructions may include steps of: receiving data from a plurality of sensors incorporated in a pair of shoes while a user is performing one or more standardized actions; determining the relative position of the user's feet based on the data; generating an integrated coordinate model from the relative positions of the feet; and calculating the position of the user's overall center of pressure (CoP) based on the integrated coordinate model.

[0017] In some embodiments, the instruction may further include the step of the processor determining the orientation of the user's feet based on the data, and the processor is configured to generate an integrated coordinate model from both the relative position and orientation of the feet.

[0018] In some embodiments, the instruction may further include the processor performing at least one of the following steps using the user's overall center of pressure (CoP) position: generating posture adjustment recommendations, generating an assessment report, and generating rehabilitation recommendations.

[0019] Aspects of this disclosure relate to an innovative server system for performing coordinate integration. The system includes a memory and a processor that communicates with the memory, the processor being configured to receive data from a plurality of sensors incorporated in a pair of shoes while a user is performing one or more standardized actions, to determine the relative position of the user's feet based on the data, to generate an integrated coordinate model from the relative position of the feet, and to further calculate the position of the user's overall center of pressure (CoP) based on the integrated coordinate model.

[0020] In some embodiments, the processor is further configured to determine the orientation of the user's feet based on the data, and the processor is configured to generate an integrated coordinate model from both the relative position and orientation of the feet.

[0021] In some embodiments, the processor is further configured to use the user's overall center of pressure (CoP) position to perform at least one of the following: generating posture adjustment recommendations, generating assessment reports, and generating rehabilitation recommendations.

[0022] Aspects of this disclosure relate to an innovative system for performing coordinate integration. The system includes a memory and a processor that communicates with the memory, the processor being configured to receive data from a plurality of sensors incorporated in a pair of shoes while a user is performing one or more standardized actions, to determine the relative position of the user's feet based on the data, to generate an integrated coordinate model from the relative position of the feet, and to further calculate the position of the user's overall center of pressure (CoP) based on the integrated coordinate model.

[0023] In some embodiments, the processor is further configured to determine the orientation of the user's foot based on the data, and the processor is configured to generate an integrated coordinate model from both the relative position of the foot and the orientation of the foot.

[0024] In some embodiments, the processor is further configured to perform at least one of generating posture adjustment recommendations, generating an evaluation report, and generating rehabilitation recommendations using the position of the user's overall center of pressure (CoP).

[0025] Regarding the general architecture implementing each feature of the present disclosure, it will be described below with reference to the drawings. The drawings and the related descriptions are provided to show exemplary embodiments of the present disclosure and do not limit the scope of the present disclosure. Throughout the drawings, reference numerals are reused to indicate the correspondence between corresponding elements.

Brief Description of the Drawings

[0026] [Figure 1] FIG. 1 shows a schematic view of a shoe utilizing a multi-layer smart sole system according to an exemplary embodiment. [Figure 2] FIG. 2 shows an exemplary system architecture 200 of the smart sole system 104 according to an exemplary embodiment. [Figure 3] FIG. 3 shows an exemplary process flow 300 for performing pressure profiling according to an exemplary embodiment. [Figure 4] FIG. 4 shows an exemplary display screen 400 according to an exemplary embodiment. [Figure 5] [[ID=]8]]FIG. 5 shows two conventional smart sole systems 500. [Figure 6] FIG. 6 shows an exemplary system architecture 600 of an integrated coordinate system (UCS). [Figure 7] FIG. 7 shows an exemplary smart sole system 700 utilizing a sensor 602. [Figure 8]Figure 8 shows an exemplary process flow 800 for performing coordinate integration. [Figure 9] Figure 9 shows an exemplary display screen 900 according to an exemplary embodiment. [Figure 10] Figure 10 shows an exemplary computing environment with exemplary computer equipment suitable for use in several embodiments. [Modes for carrying out the invention]

[0027] The following detailed description provides details of the drawings and exemplary embodiments of this application. Reference numerals and descriptions of elements that overlap between drawings are omitted for clarity. Terms used in this description are illustrative and not limiting. For example, the use of the term “automatic” includes fully automatic or semi-automatic embodiments, and depending on the desired embodiment, the user or administrator may control certain aspects of the embodiment. Selections may be made by the user via a user interface or other input means, or implemented by a desired algorithm. The exemplary embodiments described herein may be used individually or in combination, and the functions of the exemplary embodiments may be implemented by any means depending on the desired embodiment.

[0028] Figure 1 shows a schematic diagram of a shoe utilizing a multilayer smart sole system according to an exemplary embodiment. As shown in Figure 1, the shoe 100 may include components such as a shoe body 102 and a smart sole system 104. The smart sole system 104 may form an integral part of the shoe 100, or it may be one or more independent units / modules that can be inserted into a general / conventional shoe.

[0029] The smart sole system 104 may include components such as an insole 106, a midsole 108, and an outsole 110. Multiple sensors are embedded in the insole 106 and / or part of the midsole 108 or outsole 110. These sensors are used to acquire raw data of the pressure profile between the external contact surface of the shoe utilizing the smart sole system 104 and the user's foot. This raw data may include, for example, data measuring the vertical reaction force acting on the user's foot, or data measuring both the vertical reaction force and the shear reaction force. The embedded sensors may include (i) pressure sensors, or (ii) a combination of a pressure sensor and one or more inertial measurement unit (IMU) sensors, temperature sensors, and humidity sensors. The pressure sensor is used to measure the vertical reaction force (e.g., pressure) acting on the foot. In some embodiments, by incorporating additional sensors into the pressure sensor, it is possible to track both the vertical reaction force and the shear reaction force to generate or measure three-dimensional reaction forces.

[0030] The midsole 108 of the smart sole system 104 may include one or more midsole layers 108-1, 108-2, ... 108-n. Each of the midsole layers 108-1 to 108-n may have any combination of one or more sensors embedded in it, such as pressure sensors, IMU sensors, temperature sensors, and humidity sensors.

[0031] When used in a pair of shoes, the smart sole system 104 can acquire a three-dimensional pressure profile between the user's foot and the external contact surface of the shoe, and derive the ground reaction force (GRF) acting on the entire human body based on this. Specifically, a mathematical distribution function in the vertical direction (normal to the ground) is applied to pressure mapping information received from (i) the insole 106 and (ii) the midsole 108 or outsole 110, and a three-dimensional (3D) pressure profile is reconstructed over the entire volume of the sole (between the ground and the sole of the foot). The vertical component of the GRF can be derived / calculated by integrating the pressure mapping information / data over the sole region. The shear component of the GRF can be derived / calculated from the 3D pressure profile of the sole. The overall GRF is a composite vector of the vertical and shear components, representing the force at the sole / ground interface.

[0032] Figure 2 shows an exemplary system architecture 200 of the smart sole system 104 according to an exemplary embodiment. As shown in Figure 2, the smart sole system 104 may include components such as a sensor 202, a processor 204, and a memory 206. In alternative embodiments, the smart sole system 104 may include different or additional components.

[0033] Sensor 202 may be embedded in part of the insole 106 and midsole 108 or outsole 110 as shown in Figure 1. Sensor 202 may include (i) a pressure sensor, or (ii) a combination of a pressure sensor and one or more inertial measurement unit (IMU) sensors, a temperature sensor, and a humidity sensor. The pressure sensor is used to measure the vertical reaction force (e.g., pressure) acting on the foot. Three-dimensional reaction forces can be generated or measured by incorporating additional sensors into the pressure sensor to enable tracking of both vertical and shear reaction forces. The measured sensor data is transmitted from sensor 202 to processor 204.

[0034] The processor 204 processes the data collected from the sensor 202 and calculates the ground reaction force (GRF) acting on the human body. In some embodiments, the processor 204 performs preprocessing on the data received from the sensor 202, which may include one or more processes such as analog-to-digital data conversion, data reduction, data or feature extraction, data transformation, data cleaning, and normalization. Furthermore, in some embodiments, the processor 204 may use the data collected from the sensor 202 to perform additional data analysis such as fatigue monitoring, load evaluation, and performance tracking.

[0035] Memory 206 stores instructions executed by processor 204, data collected from sensor 202, data calculated by processor 204 (e.g., GRF), and instructions / programs used by each component of smart sole system 104. Memory 206 may be any storage medium, not limited to read-only memory, random access memory, solid-state devices and drives, or other tangible or non-temporary media suitable for storing electronic information.

[0036] The user device 220 communicates with the system architecture 200 via the network 240 and receives information from the system architecture 200. The network 240 may be the internet, a local area network, a wide area network, a telephone network, a cellular network, a satellite network, or a combination thereof. In some embodiments, requests to perform GRF calculations / calculations may be issued by a user or operator via the user device 220 and received by the processor 204.

[0037] Examples of user devices 220 include, but are not limited to, highly portable devices such as smartphones, devices mounted in vehicles or other machinery, and devices carried by people or animals; mobile devices such as tablets, notebooks, laptops, personal computers, portable televisions, and radios; and non-portable devices such as desktop computers, other computers, information kiosks, and televisions and radios with one or more processors built in or connected.

[0038] In some embodiments, an artificial intelligence (AI) model may be stored in memory 206 and used to generate recommendations based on GRF calculated in real time by the processor 204. Recommendations may include, but are not limited to, one or more of the following: posture or motion adjustments to reduce impact force, product recommendations to minimize GRF, or exercises to improve body alignment.

[0039] In some embodiments, the derivation and training of the AI ​​model may be performed by the server 230 using historical data (e.g., sensor data, previously generated recommendations, etc.). The trained AI model is then transmitted from the server 230, received by the smart soul system 104, and stored in memory 206. Communication between the server 230 and the system architecture 200 takes place via the network 240. The AI ​​model may include, but is not limited to, convolutional neural networks (CNNs), recurrent neural networks (RNNs), deep RNNs (DRNNs), Q-learning networks (QNs), deep Q-learning networks (DQNs), decision trees, k-nearest neighbors, etc. The RNN may include long short-term memory (LSTM).

[0040] In an alternative embodiment, instead of storing the AI ​​model in memory 206, the calculated GRF and associated data may be sent to the server 230 for recommendation generation. Specifically, recommendation generation is performed by the AI ​​model stored in the server 230. Furthermore, in an alternative embodiment, remote storage may be used instead of memory 206. For example, cloud storage may be used to perform the data storage function.

[0041] In some embodiments, the calculated GRF, related information / data, and / or recommendations may be transmitted to the user device 220 for verification. The information and recommendations may be presented in real time as graphs, charts, text, etc., via a graphical user interface (GUI) on the user device 220.

[0042] Figure 3 shows an exemplary process flow 300 for performing pressure profiling according to an exemplary embodiment. The process flow 300 begins in step S302, where raw data is received from multiple sensors incorporated into a pair of shoes worn by the user. The raw data may include, for example, data measuring the vertical reaction force acting on the user's feet, and data measuring both the vertical reaction force and the shear reaction force. In step S304, the raw data is preprocessed to generate preprocessed data. Preprocessing may be performed by performing one or more operations such as data cleaning, data organization, data transformation, data normalization, and data reduction.

[0043] The process then proceeds to step S306, where the user's ground reaction force (GRF) is calculated using the preprocessed data. In step S308, at least one of the following is performed using the ground reaction force derived from the preprocessed data: generating posture adjustment recommendations, generating a report, and performing additional data analysis. In some embodiments, an artificial intelligence (AI) model may be derived and used to perform one or more of the following: generating posture adjustment recommendations, generating a report, or performing additional / advanced data analysis.

[0044] AI models may include, but are not limited to, recurrent neural networks (RNNs), deep RNNs (DRNNs), Q-learning networks (QNs), deep Q-learning networks (DQNs), decision trees, k-nearest neighbors, etc. RNNs may also include long short-term memory (LSTM). In some embodiments, the AI ​​model may be a large-scale multimodal language model that handles different types of input data such as text, images, audio, and video. The AI ​​model is iteratively trained using historical data as training input, and the training parameters are adjusted to generate posture adjustment recommendations, analysis reports, and additional / advanced data analysis.

[0045] In some embodiments, posture adjustment recommendations, analysis reports, and additional / advanced data analysis may be sent to a user device (e.g., user device 220) for confirmation. Examples of user devices include, but are not limited to, mobile devices such as smartphones, tablets, notebooks, laptops, and personal computers, as well as non-portable devices such as desktop computers, information kiosks, and televisions.

[0046] Figure 4 shows an exemplary display screen 400 according to an exemplary embodiment. In some embodiments, the display screen 400 may be a graphical user interface (GUI) accessed by a user on a user device (e.g., user device 220). As shown in Figure 4, the display screen 400 can display information such as a panel 402 and a graphic display area 404. Selectable information contained in the panel 402 may include, but are not limited to, real-time view, recording, analysis, editing, display, and center of pressure (CoP). Other displayable information may include firmware information, settings, licenses, and documentation. The graphic display area 404 may contain graphic information showing differences in pressure distribution of the insole layer, CoP position over time, GRF over time, etc.

[0047] The exemplary embodiments described above may have multiple advantages and benefits. For example, by using a multi-layer insole structure, the ground reaction force (GRF) between the foot and the ground can be measured. The unique combination and arrangement of sensors provides a means of measuring the shear force (horizontal force) component beneath the sole of the foot, which is not available in the prior art. Furthermore, the combination and arrangement of sensors requires minimal effort to set up and can be used in existing footwear products. The measured GRF can be used to further monitor and analyze ergonomics in industrial work, which can help prevent potential injuries.

[0048] Currently, there is no technology to measure the shear force (horizontal force) beneath the sole of the foot. Specifically, conventional technology focuses only on the pressure distribution directly beneath the human foot. Clear quantitative measurements of the ground reaction force (GRF) between the foot and the ground have not been performed.

[0049] This exemplary embodiment relates to a method and system for tracking and analyzing the correlation between an individual's / user's feet. For example, it is possible to track which foot is forward while a person is walking and determine the positional relationship with the other foot. This embodiment generates information necessary to perform a comprehensive biomechanical analysis of the whole body. Furthermore, this embodiment estimates the center of pressure on the user's feet, which is an important element in performing balance assessment. The coordination between an individual's / user's feet is crucial for evaluating the whole-body musculoskeletal system and provides information necessary to perform a comprehensive biomechanical analysis of the whole body, including posture, load distribution, and precise external forces applied to the feet.

[0050] Figure 5 shows two conventional smart sole systems 500. Each smart sole system 500 is capable of generating and reporting data for each foot, but the relationship between the coordinate systems ((X,Y,Z) and (X',Y',Z')) of both feet cannot be detected or tracked using the smart sole systems 500. For example, while the pressure distribution on each foot can be measured and mapped individually, the geometric relationship (e.g., distance, position, etc.) between high-pressure points on the left foot and high-pressure points on the right foot cannot be determined. When using two smart sole systems 500, it is not possible to estimate any identifiable correlations.

[0051] Figure 6 shows an exemplary system architecture 600 of the Unified Coordinate System (UCS). As shown in Figure 6, the system architecture 600 may include components such as sensors 602 and processors 604. In alternative embodiments, the system architecture 600 may include different or additional components.

[0052] Sensor 602 may be embedded in the insole, midsole, or outsole of a pair of shoes. Sensor 602 may (i) include at least one inertial measurement unit (IMU) sensor in each shoe, or (ii) include a combination of an IMU sensor and one or more sensors such as a temperature sensor, humidity sensor, gyroscope, accelerometer, magnetometer, etc. The IMU sensor is used to measure foot acceleration, angular velocity, orientation, gravity, etc., while the user is performing one or more standardized actions. Sensors other than the IMU sensor may be incorporated to improve accuracy or to provide additional information useful for performing further analysis. Three-dimensional reaction forces can be generated or measured by incorporating additional sensors into the pressure sensor so that both vertical and shear reaction forces can be tracked. The measured sensor data is transmitted from sensor 602 to processor 604.

[0053] In some embodiments, the processor 604 is a processor located on a server and communicates with the sensor 602 via a network (e.g., network 620). Network 620 may be the Internet, a local area network, a wide area network, a telephone network, a cellular network, a satellite network, or a combination thereof. The processor 604 may include components such as a calibration module 606 and an artificial intelligence (AI) module 608.

[0054] The calibration module 606 performs calibration by comparing the data received from the sensor 602 with reference data. The calibration module 606 may perform additional functions, such as result testing and data validation, to verify that the sensor 602 is operating as expected and producing the desired results. Calibrated data is generated by the calibration module 606 and received by the AI ​​module 608. In some embodiments, the calibration module 606 may also perform data preprocessing by performing one or more operations such as data cleaning, data organization, data transformation, data normalization, and data reduction. In alternative embodiments, preprocessing may be performed by the processor 604 before the data is received by the calibration module 606.

[0055] AI module 608 generates a coordinate system containing the coordinates of the primary markers of both feet using calibrated data. First, AI module 608 generates the initial position, relative position, and orientation of the user's feet through the calibrated data. Next, AI module 608 combines the initial position, relative position, and foot orientation to generate an integrated coordinate model. The coordinates of the primary markers in the integrated coordinate model are updated when sensor outputs are received during the execution of various standardized actions. Specifically, the coordinates (X,Y,Z) of the primary marker of the left foot and the coordinates (X',Y',Z') of the primary marker of the right foot are integrated and represented as coordinates (X",Y",Z). The data / information is received in real time by calibration module 606 to track and update the position and orientation of the feet in the integrated coordinate model. Analysis of the real-time data may be performed based on a predetermined frequency (e.g., 1Hz, 1kHz, 1mHz, etc.) or the user's preference.

[0056] In some embodiments, the derivation and training of the integrated coordinate model may be performed using historical data (e.g., sensor data, previously generated recommendations, etc.). The integrated coordinate model may include, but is not limited to, convolutional neural networks (CNNs), recurrent neural networks (RNNs), deep RNNs (DRNNs), Q-learning networks (QNs), deep Q-learning networks (DQNs), decision trees, k-nearest neighbors, etc. The RNN may include long short-term memory (LSTM).

[0057] The AI ​​module 608 utilizes the output of the integrated coordinate model to further calculate the user's overall center of pressure (CoP) location. In some embodiments, the AI ​​module 608 may use the user's overall CoP location to perform at least one of the following: generating posture adjustment recommendations, generating evaluation reports, and generating rehabilitation recommendations. Furthermore, in some embodiments, the AI ​​module 608 may use the user's overall CoP location to generate an AI recommendation model for generating posture adjustment recommendations, evaluation reports, or rehabilitation recommendations.

[0058] In some embodiments, the calibrated data and reports / recommendations generated by the AI ​​module 608 may be sent to a user device 630 for verification. The information and recommendations may be presented in real time as graphs, charts, text, etc., via a graphical user interface (GUI) on the user device 630. Furthermore, in some embodiments, the sensor data, calibrated data, trained AI model, and output of the AI ​​model may be stored in memory 610. Memory 610 may be any storage medium, not limited to read-only memory, random access memory, solid-state devices and drives, or other tangible or non-temporary media suitable for storing electronic information. In alternative embodiments, remote storage may be used instead of memory 610. For example, cloud storage may be used to perform data storage functions.

[0059] Examples of user devices 630 include, but are not limited to, highly portable devices such as smartphones, devices mounted in vehicles or other machinery, and devices carried by people or animals; mobile devices such as tablets, notebooks, laptops, personal computers, portable televisions, and radios; and non-portable devices such as desktop computers, other computers, information kiosks, and televisions and radios with one or more processors.

[0060] The user device 630 communicates with the system architecture 600 / UCS via the network 620. The network 620 may be the Internet, a local area network, a wide area network, a telephone network, a cellular network, a satellite network, or a combination thereof. In some embodiments, a request to perform the calculation / calculation of the center of pressure (CoP) may be issued by the user or operator via the user device 630 and received by the processor 604.

[0061] Figure 7 shows a smart sole system 700 utilizing sensor 602 according to an exemplary embodiment. Unlike the conventional smart sole system 500, the smart sole system 700 can receive data from both feet and integrate and analyze the coordinates of both feet. Specifically, the coordinates (X,Y,Z) of the left foot and the coordinates (X',Y',Z') of the right foot are quantified and integrated as coordinates (X",Y",Z). The integrated coordinates are identified as the center of pressure (CoP) of both feet and have various applications in the fields of biomechanics and ergonomics analysis. Furthermore, the CoP also contributes to a more accurate calculation of the center of gravity.

[0062] The smart sole system 700 may constitute an integrated part of a pair of shoes, or it may be an independent unit / module inserted into a general / conventional shoe (e.g., an insole). In some embodiments, the smart sole system 700 may include a pair of insoles embedded with sensors for performing data collection.

[0063] Figure 8 shows an exemplary process flow 800 for performing coordinate integration according to an exemplary embodiment. The process flow 800 begins at step S802, where the user performs one or more standardized actions while receiving data from multiple sensors (e.g., IMU sensors) incorporated into a pair of shoes. In some embodiments, the multiple sensors may be embedded in the insoles of the pair of shoes. The data may include, but is not limited to, data measuring linear acceleration along each axis, data measuring angular velocity / rotational velocity around each axis, and the like.

[0064] In step S804, the data is preprocessed to generate preprocessed data. This data may be processed by performing one or more operations such as data cleaning, data organization, data transformation, data normalization, and data reduction. In step S806, the orientation of the user's feet is determined based on this data. In step S808, the relative position of the user's feet is determined based on this data. In step S810, the initial position of the user's feet is determined based on this data. In step S812, an integrated coordinate model is generated from the initial position, relative position, and foot orientation. In some embodiments, one or more of (i) determining the foot orientation, (ii) determining the relative position of the feet, (iii) determining the initial position of the feet, or (iv) generating the integrated coordinate model may be performed using an AI model. The AI ​​model may also be used to derive the initial position of the user's feet through this data.

[0065] AI models may include, but are not limited to, recurrent neural networks (RNNs), deep RNNs (DRNNs), Q-learning networks (QNs), deep Q-learning networks (DQNs), decision trees, k-nearest neighbors, etc. RNNs may also include long short-term memory (LSTM). In some embodiments, the AI ​​model may be a large-scale multimodal language model that handles different types of input data such as text, images, audio, and video. The AI ​​model is iteratively trained using historical data as training input, and the training parameters are adjusted to determine the relative position of the feet, the orientation of the feet, and / or an integrated coordinate model.

[0066] In step S814, the user's overall center of pressure (CoP) is calculated based on the integrated coordinate model. In step S816, one or more of the following are performed using the user's overall CoP: generating posture adjustment recommendations, generating an evaluation report, or generating rehabilitation recommendations. In some embodiments, one or more reports or recommendations may be generated using an AI model. The AI ​​model is iteratively trained using historical data as training input, and its training parameters are adjusted to generate posture adjustment recommendations, evaluation reports, and rehabilitation recommendations.

[0067] In some embodiments, posture adjustment recommendations, evaluation reports, and rehabilitation recommendations may be transmitted in real time to a user device (e.g., user device 630) for confirmation. Examples of user devices include, but are not limited to, mobile devices such as smartphones, tablets, notebooks, laptops, and personal computers, as well as non-portable devices such as desktop computers, information kiosks, and televisions.

[0068] Figure 9 shows an exemplary display screen 900 according to an exemplary embodiment. In some embodiments, the display screen 900 may be a graphical user interface (GUI) accessed by a user on a user device (e.g., user device 630). As shown in Figure 9, the display screen 900 can display information such as a panel 902 and a graphic display area 904. Selectable information included in the panel 902 includes, but is not limited to, real-time view, recording, analysis, editing, display, and center of pressure (CoP). Other displayable information may include firmware information, settings, licenses, and documentation. The graphic display area 904 may include graphic information showing the Unified Coordinate System (UCS) in graphic form, the CoP position over time, the foot position over time, etc.

[0069] The exemplary embodiments described above may offer various advantages and benefits, such as improved accuracy in calculating / deriving the center of pressure (CoP) through unique combinations and arrangements of sensors. Specifically, bilateral CoP is an important measurement in balance assessment and musculoskeletal assessment of the human body, and can be more accurately calculated / derived from the relative position and orientation of the feet using a Unified Coordinate System (UCS). Conventional smart insoles can measure plantar pressure or the vertical force component under the sole of the foot, but since the measurement data is generated for each foot, it is not possible to derive bilateral CoP. Improved CoP in the lower extremities helps to enhance comprehensive biomechanical analysis of the user's body (e.g., industrial ergonomics analysis and evaluation, patient assessment, balance analysis, musculoskeletal analysis, etc.).

[0070] Figure 10 shows an exemplary computing environment with exemplary computer equipment suitable for use in several embodiments. The computer equipment 1005 in the computing environment 1000 may include one or more processing units, cores, or processors 1010, memory 1015 (e.g., RAM, ROM, etc.), internal storage 1020 (e.g., magnetic, optical, solid-state storage, organic storage, etc.), and / or I / O interfaces 1025, all of which may be coupled to a communication mechanism or bus 1030 for communicating information, or incorporated into the computer equipment 1005. Depending on the desired embodiment, the I / O interface 1025 may be configured to receive images from a camera or provide images to a projector or display.

[0071] The computer device 1005 may be communicatively connected to the input / user interface 1035 and the output device / interface 1040. Either or both of the input / user interface 1035 and the output device / interface 1040 may be wired or wireless interfaces and may be detachable. The input / user interface 1035 may include physical or virtual devices, components, sensors, or interfaces that can be used to provide input (e.g., buttons, touchscreen interfaces, keyboards, pointing / cursor controls, microphones, cameras, Braille, motion sensors, accelerometers, optical readers, etc.). The output device / interface 1040 may include displays, televisions, monitors, printers, speakers, Braille, etc. In some embodiments, the input / user interface 1035 and the output device / interface 1040 may be incorporated into or physically connected to the computer device 1005. In other embodiments, another computer device may provide or perform the functions of the input / user interface 1035 and the output device / interface 1040 to the computer device 1005.

[0072] Examples of computer devices 1005 include, but are not limited to, highly portable devices such as smartphones, devices mounted in vehicles or other machines, and devices carried by people or animals; mobile devices such as tablets, notebooks, laptops, personal computers, portable televisions, and radios; and non-portable devices such as desktop computers, other computers, information kiosks, and televisions and radios with one or more processors. Computer devices 1005 may be communicatively connected to external storage 1045 and network 1050 (for example, via I / O interface 1025) and can communicate with any number of networked components, devices, and systems, including one or more computer devices of the same or different configurations. Computer devices 1005 or any connected computer devices may function, provide services, or be referred to as servers, clients, thin servers, general-purpose machines, special-purpose machines, or other names.

[0073] The IO interface 1025 includes, but is not limited to, wired and / or wireless interfaces that use any communication or input / output protocol or standard (e.g., Ethernet, 802.11x, Universal System Bus, WiMAX, modem, cellular network protocol, etc.) to send and receive information to and from all connected components, devices, and networks within the computing environment 1000. The network 1050 may be a network such as the Internet, a local area network, a wide area network, a telephone network, a cellular network, a satellite network, or a combination thereof.

[0074] The computer device 1005 can communicate using or through computer-compatible or computer-readable media, which include temporary and non-temporary media. Temporary media include transmission media (e.g., metal cables, optical fibers), signals, carrier waves, etc. Non-temporary media include magnetic media (e.g., disks and tapes), optical media (e.g., CD-ROMs, digital video discs, Blu-ray® discs), solid-state media (e.g., RAM, ROMs, flash memory, solid-state storage), and other non-volatile storage or memory.

[0075] Computer device 1005 may be used to implement techniques, methods, applications, processes, or computer executable instructions in several exemplary computing environments. Computer executable instructions may be obtained from temporary media, stored in non-temporary media, or obtained from non-temporary media. Executable instructions may be generated from one or more programming languages, scripting languages, or machine languages ​​(e.g., C, C++, C#, Java, Visual Basic, Python, Perl, JavaScript, etc.).

[0076] The processor 1010 may run under any operating system (OS) (not shown) in a native or virtual environment. One or more applications may be deployed, which may include a logical unit 1060, an application programming interface (API) unit 1065, an input unit 1070, an output unit 1075, and an inter-unit communication mechanism 1095 for communication between units, the OS, and other applications (not shown). The units and elements described are subject to change in design, function, configuration, or implementation, and are not limited to those described. The processor 1010 may take the form of a hardware processor such as a central processing unit (CPU), or it may be a combination of hardware and software units.

[0077] In some embodiments, when information or execution instructions are received by the API unit 1065, they may be transmitted to one or more other units (e.g., a logical unit 1060, an input unit 1070, and an output unit 1075). In some cases, the logical unit 1060 may be configured to control the flow of information between units and to direct the services provided by the API unit 1065, the input unit 1070, and the output unit 1075. For example, the flow of one or more processes or implementations may be controlled by the logical unit 1060 alone or in conjunction with the API unit 1065. The input unit 1070 may be configured to take input for the operations described in the exemplary embodiments, and the output unit 1075 may be configured to provide output based on the operations described in the exemplary embodiments.

[0078] The processor 1010 may be configured to receive raw data from multiple sensors incorporated into a pair of shoes worn by the user, as shown in Figures 1 to 4. The processor 1010 may also be configured to preprocess the raw data to generate preprocessed data, as shown in Figures 1 to 4. Furthermore, the processor 1010 may be configured to calculate the user's ground reaction force (GRF) using the preprocessed data, as shown in Figures 1 to 4. Furthermore, the processor 1010 may be configured to use the ground reaction force derived from the preprocessed data to perform at least one of the following: generating posture adjustment recommendations, generating reports, and performing additional data analysis, as shown in Figures 1 to 4.

[0079] The processor 1010 may be configured to receive data from multiple sensors incorporated into a pair of shoes while the user is performing one or more standardized actions, as shown in Figures 6 to 9. Furthermore, the processor 1010 may be configured to determine the relative position of the user's feet based on this data, as shown in Figures 6 to 9. Furthermore, the processor 1010 may be configured to generate an integrated coordinate model from the relative position of the feet, as shown in Figures 6 to 9. Furthermore, the processor 1010 may be configured to calculate the position of the user's overall center of pressure (CoP) based on the integrated coordinate model, as shown in Figures 6 to 9.

[0080] The processor 1010 may be configured to determine the orientation of the user's feet based on the data, as shown in Figures 6 to 9. Furthermore, the processor 1010 may be configured to use the position of the user's overall center of pressure (CoP) to perform at least one of the following: generating posture adjustment recommendations, generating evaluation reports, and generating rehabilitation recommendations, as shown in Figures 6 to 9.

[0081] Part of the detailed explanation is presented in terms of symbolic representations of algorithms and operations relating to calculations within a computer. These algorithmic descriptions and symbolic representations are means used by experts in the field of data processing technology to communicate the essence of their innovations to other experts in the field. An algorithm is a set of defined steps that lead to a desired final state or result. In exemplary embodiments, the steps performed require physically manipulating tangible quantities to achieve a tangible result.

[0082] Unless otherwise explicitly stated, it will be understood by those skilled in the art that throughout this specification, any use of terms such as “processing,” “calculation,” “calculation,” “determination,” and “display” may include operations and processes performed by a computer system or other information processing device. These operations and processes include manipulating and transforming data expressed as physical (electronic) quantities in the registers and memory of the computer system to store other data similarly expressed as physical quantities in the memory or registers of the computer system, or in other information storage, transmission, or display devices.

[0083] Embodiments described herein may also relate to apparatus for performing the operations described herein. Such apparatus may be configured specifically for a required purpose, or may include one or more general-purpose computers that are selectively started or reconfigured by one or more computer programs. These computer programs may be stored in computer-readable media, such as computer-readable storage media or computer-readable signaling media. Computer-readable storage media include, but are not limited to, tangible media such as optical disks, magnetic disks, read-only memory, random-access memory, solid-state devices and drives, and other tangible or non-temporary media suitable for storing electronic information. Computer-readable signaling media may include media such as carrier waves. The algorithms and representations presented herein are not inherently related to any particular computer or other apparatus. Computer programs may include pure software implementations that include instructions for performing the operations of a desired embodiment.

[0084] Various general-purpose systems can be used with programs and modules according to the exemplary embodiments described herein, or it may be convenient to construct more specialized devices to perform desired method steps. Furthermore, the exemplary embodiments are not described with reference to any particular programming language. Those skilled in the art will understand that various programming languages ​​can be used to implement the teachings of the exemplary embodiments described herein. Instructions in a programming language may be executed by one or more processing units, such as a central processing unit (CPU), a processor, or a controller.

[0085] As is well known to those skilled in the art, the operations described above can be performed by hardware, software, or a combination of software and hardware. Each aspect of the exemplary embodiment may be implemented using circuits and logic devices (hardware), while other aspects may be implemented using instructions (software) stored on a machine-readable medium. When such instructions are executed by a processor, the processor performs a method for performing the embodiments of the present application. Furthermore, some of the exemplary embodiments of the present application may be performed by hardware alone, while other exemplary embodiments may be performed by software alone. In addition, each of the described functions may be performed by a single unit, or distributed among multiple components and performed in any manner. When performed by software, the method may be performed by a processor such as a general-purpose computer based on instructions stored on a computer-readable medium. If necessary, such instructions may be stored on the medium in compressed and / or encrypted form.

[0086] Furthermore, other embodiments of the present application will be obvious to those skilled in the art from the description herein and the practice of the teachings herein. Each aspect and / or component of the exemplary embodiments described herein can be used alone or in any combination. This specification and the exemplary embodiments should be considered for illustrative purposes only, and the true scope and spirit of the present application are shown by the following claims. [Explanation of symbols]

[0087] 100…Shoe, 102…Shoe body, 104…Smart sole system, 106…Insole, 108…Midsole, 110…Outsole, 202…Sensor, 204…Processor, 206…Memory, 220…User device, 230…Server, 240…Network

Claims

1. A pressure profiling method, The processor receives raw data from multiple sensors embedded in a pair of shoes worn by the user, The processor preprocesses the raw data to generate preprocessed data, The processor calculates the user's ground reaction force using the pre-processed data, The processor performs at least one of the following using the ground reaction force derived from the preprocessed data: generating posture adjustment recommendations, generating reports, and performing additional data analysis. including, Pressure profiling method.

2. In the pressure profiling method according to claim 1, Multiple sensors are embedded in at least one of the insole, midsole, and outsole of a pair of shoes. Multiple sensors acquire the pressure profile between the user's foot and the external contact surface of the pair of shoes as raw data. Pressure profiling method.

3. In the pressure profiling method according to claim 2, The midsole includes at least one midsole layer in each of the pair of shoes. Pressure profiling method.

4. In the pressure profiling method according to claim 1, The raw data includes (i) data measuring the vertical reaction force acting on the user's foot, or (ii) data measuring both the vertical reaction force and the shear reaction force acting on the user's foot. Pressure profiling method.

5. In the pressure profiling method according to claim 1, The plurality of sensors include (i) a pressure sensor, or (ii) a combination of the pressure sensor and one or more inertial measurement unit (IMU) sensors, temperature sensors, or humidity sensors. Pressure profiling method.

6. In the pressure profiling method according to claim 1, The processor is configured to perform at least one of the following using an artificial intelligence (AI) model: generating posture adjustment recommendations, generating reports, and performing additional data analysis. Pressure profiling method.

7. A pressure profiling system, Multiple sensors, A processor that communicates with multiple sensors, Equipped with, The aforementioned processor, The system receives raw data from multiple sensors embedded in a pair of shoes worn by the user. The raw data is preprocessed to generate preprocessed data. Using the pre-processed data, the user's ground reaction force is calculated. Using the ground reaction force obtained from the pre-processed data, at least one of the following is performed: generation of posture adjustment recommendations, report generation, and additional data analysis. It is configured in such a way. Pressure profiling system.

8. In the pressure profiling system according to claim 7, Multiple sensors are embedded in at least one of the insole, midsole, and outsole of a pair of shoes. Multiple sensors acquire the pressure profile between the user's foot and the external contact surface of the pair of shoes as raw data. Pressure profiling system.

9. In the pressure profiling system according to claim 8, The midsole includes at least one midsole layer in each of the pair of shoes. Pressure profiling system.

10. In the pressure profiling system according to claim 7, The raw data includes (i) data measuring the vertical reaction force acting on the user's foot, or (ii) data measuring both the vertical reaction force and the shear reaction force. Pressure profiling system.

11. In the pressure profiling system according to claim 7, The processor uses an artificial intelligence (AI) model to perform at least one of the following: generating posture adjustment recommendations, generating reports, and performing additional data analysis. It is configured in such a way. Pressure profiling system.

12. A method for integrating coordinates, While the user is performing one or more standardized actions, the processor receives data from multiple sensors embedded in a pair of shoes, The processor determines the relative position of the user's feet based on the data, The aforementioned processor generates an integrated coordinate model from the relative position of the feet, The processor calculates the position of the user's overall center of pressure (CoP) based on the integrated coordinate model, including, Coordinate integration method

13. In the coordinate integration method described in claim 12, Furthermore, the processor determines the orientation of the user's feet based on the data, The processor is configured to generate the integrated coordinate model from both the relative position of the foot and the orientation of the foot. Coordinate integration method.

14. In the coordinate integration method described in claim 13, The processor is configured to determine the relative position and orientation of the user's feet using an artificial intelligence (AI) model. The AI ​​model derives the initial position, relative position, and orientation of the user's feet through the data. Coordinate integration method.

15. In the coordinate integration method described in claim 12, Furthermore, the processor includes performing at least one of the following actions using the user's overall center of pressure (CoP): generating posture adjustment recommendations, generating evaluation reports, and generating rehabilitation recommendations. Coordinate integration method.

16. In the coordinate integration method described in claim 12, Multiple sensors are embedded in the insoles of a pair of shoes. Coordinate integration method.

17. In the coordinate integration method described in claim 12, The plurality of sensors include a plurality of inertial measurement unit (IMU) sensors, Coordinate integration method.

18. A coordinate integration system, Multiple sensors, The system comprises a processor that communicates with multiple sensors, The aforementioned processor, While the user is performing one or more standardized actions, data is received from multiple sensors embedded in a pair of shoes. Based on the aforementioned data, the relative position of the user's feet is determined. A unified coordinate model is generated from the relative position of the foot, Based on the aforementioned integrated coordinate model, the system is configured to calculate the position of the user's overall center of pressure (CoP). Coordinate integration system.

19. In the coordinate integration system according to claim 18, The processor is further configured to determine the orientation of the user's feet based on the data, The processor is configured to generate the integrated coordinate model from both the relative position and orientation of the foot. The processor is configured to determine the relative position and orientation of the user's feet using an artificial intelligence (AI) model. The AI ​​model derives the initial position, relative position, and orientation of the user's feet through the data. Coordinate integration system.

20. In the coordinate integration system according to claim 18, The processor is further configured to perform at least one of the following using the user's overall center of pressure (CoP): generating posture adjustment recommendations, generating evaluation reports, and generating rehabilitation recommendations. Coordinate integration system.

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