CN114176571A - Motion state detection system based on sole pressure - Google Patents
Motion state detection system based on sole pressure Download PDFInfo
- Publication number
- CN114176571A CN114176571A CN202210052518.4A CN202210052518A CN114176571A CN 114176571 A CN114176571 A CN 114176571A CN 202210052518 A CN202210052518 A CN 202210052518A CN 114176571 A CN114176571 A CN 114176571A
- Authority
- CN
- China
- Prior art keywords
- pressure data
- pressure
- motion state
- data
- preset
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/1036—Measuring load distribution, e.g. podologic studies
- A61B5/1038—Measuring plantar pressure during gait
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/1118—Determining activity level
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
- A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
- A61B5/6802—Sensor mounted on worn items
- A61B5/6804—Garments; Clothes
- A61B5/6807—Footwear
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
- A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B2562/00—Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
- A61B2562/02—Details of sensors specially adapted for in-vivo measurements
- A61B2562/0247—Pressure sensors
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Surgery (AREA)
- Public Health (AREA)
- Pathology (AREA)
- Veterinary Medicine (AREA)
- Biomedical Technology (AREA)
- Heart & Thoracic Surgery (AREA)
- Medical Informatics (AREA)
- Molecular Biology (AREA)
- Biophysics (AREA)
- Animal Behavior & Ethology (AREA)
- General Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Physiology (AREA)
- Dentistry (AREA)
- Oral & Maxillofacial Surgery (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Psychiatry (AREA)
- Signal Processing (AREA)
- Evolutionary Computation (AREA)
- Fuzzy Systems (AREA)
- Mathematical Physics (AREA)
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
Abstract
The utility model provides a motion state detection system based on sole pressure, which collects multiple groups of pressure data corresponding to a ground treading event by detecting the ground treading event corresponding to an object to be detected, wherein each group of pressure data comprises pressure data at a plurality of preset sampling positions in the sole of the object to be detected; determining the pace corresponding to the ground stepping event; aiming at each group of the multiple groups of pressure data, determining a Hilbert characteristic diagram corresponding to the group of pressure data, and the corresponding landing time length of each pressure data in the group of pressure data in the event of landing; inputting the Hilbert characteristic diagram, the pace and the landing duration into a pre-trained motion state classification model, and determining a motion state corresponding to each pressure data; and marking the corresponding motion state for the pressure data. Can have higher applicability for its motion state of sole pressure data mark that detects automatically, promoted the efficiency for the motion state that sole pressure data mark corresponds.
Description
Technical Field
The disclosure relates to the technical field of mechanical detection, in particular to a motion state detection system based on plantar pressure.
Background
The detection of the plantar pressure is the basis for analyzing and measuring abnormal plantar stress distribution and gait, and has important significance for the etiological analysis, diagnosis and functional evaluation of motor system diseases, so that the plantar pressure detection is more and more widely applied clinically. The distribution of the pressure on the sole of the foot can reflect the information of the physiology, structure and function of the lower limbs and even the whole body, i.e. the dynamic characteristics of the foot in the process of movement. When the function of lower limbs and the structure in feet are slightly changed, the distribution of the pressure load of the sole of foot is changed, so that the change of the pressure of the sole of foot under different states of the human body (between normal people and patients, between standing and gait) is researched, the pressure of the sole of foot can be further analyzed, the stress condition and the physiological and pathological parameters of all parts of the human body can be obtained, and the stress condition, the physiological and pathological parameters, the medical history and other examinations can be used together to diagnose the health degree of the human body.
At present, conventional plantar pressure detection needs to be carried out in a gait laboratory, a test subject is required to complete barefoot testing on a force measuring flat plate, but in the testing mode, bare feet of a patient are required to walk on a rigid flat plate in the testing process, if plantar pressure distribution of daily activities such as ascending and descending is tested, only a simulation mode can be adopted, plantar pressure distribution of the patient in different motion states in real-life daily activities cannot be obtained, motion states corresponding to detected plantar pressure data need to be recorded manually, and data acquisition efficiency is low.
Disclosure of Invention
The embodiment of the present disclosure provides a motion state detection system based on plantar pressure at least, can detect plantar pressure data of an object to be detected in different motion states in real life daily activities, and automatically mark its motion state for the detected plantar pressure data, which has higher applicability and improves the efficiency of marking corresponding motion states for plantar pressure data.
The embodiment of the present disclosure provides a motion state detection system based on sole pressure, including: the detection system comprises a data acquisition device, an analysis processing device and a storage device which are sequentially in communication connection, wherein the detection system is worn at the sole of an object to be detected;
the data acquisition device is used for detecting a land stepping event corresponding to the object to be detected and acquiring a plurality of groups of pressure data corresponding to the land stepping event, wherein each group of pressure data comprises pressure data at a plurality of preset sampling positions in the sole of the object to be detected;
the analysis processing device is used for determining the pace corresponding to the land stepping event; for each group of the multiple groups of pressure data, determining a Hilbert characteristic diagram corresponding to the group of the pressure data, and a landing time length corresponding to each pressure data in the group of the pressure data in the land stepping event;
inputting the Hilbert feature map, the pace and the landing duration into a pre-trained motion state classification model, and determining a motion state corresponding to each piece of pressure data;
and marking the corresponding motion state for the pressure data, and sending the motion state to the storage device for storage.
In an alternative embodiment, the data acquisition device comprises: the pressure sensor group, the analog switch and the analog-digital converter; the analysis processing device comprises a random access memory;
the pressure sensor group, the analog switch and the analog-to-digital converter are sequentially in communication connection; the analog-to-digital converter is connected to the random access memory;
the pressure sensor group comprises a plurality of pressure sensors, and the pressure sensors are arranged according to preset sole sampling characteristic points and are used for acquiring pressure data of each sole sampling characteristic point in the ground treading event;
the analog switch is used for controlling the rest of the pressure sensors to be in a turn-off state when controlling the pressure sensor to be in a working state for each of the plurality of pressure sensors; after the pressure sensors in the current working state finish the pressure data acquisition, switching to the next pressure sensor in the working state according to a preset acquisition sequence, and controlling the rest pressure sensors to be in the off state until all the pressure sensors finish the pressure data acquisition;
the analog-to-digital converter is used for converting the voltage signal corresponding to the pressure data acquired by the pressure sensor into a digital signal and sending the digital signal to the random access memory;
the random access memory is used for receiving and temporarily storing the pressure data acquired by each pressure sensor, and transmitting all the pressure data to the storage device for storage until all the pressure data acquired by all the pressure sensors are received.
In an optional embodiment, the analysis processing apparatus is further configured to:
acquiring a preset training data set and a preset test data set corresponding to the motion state classification model;
the preset training data set comprises the Hilbert feature map, the pace and the landing duration corresponding to a plurality of preset training pressure data, and a motion state label corresponding to each preset training pressure data;
the preset test data set comprises the Hilbert characteristic diagram, the pace and the landing duration corresponding to a plurality of preset test pressure data, and a motion state label corresponding to each preset test pressure data;
inputting the preset training data set into the motion state classification model, and training the motion state classification model;
inputting the preset test data set into the trained motion state classification model, and determining the classification accuracy corresponding to the motion state classification model according to the classification result corresponding to the motion state classification model and the motion state label corresponding to the preset test pressure data;
and when the classification accuracy is greater than a preset classification accuracy threshold, finishing training of the motion state classification model.
In an optional embodiment, the analysis processing apparatus is further configured to:
and supplementing the data quantity corresponding to each group of pressure data to a preset sampling quantity by utilizing an interpolation method aiming at each group of pressure data.
In an optional implementation, the data acquisition device is specifically configured to:
determining the duration corresponding to the land stepping event;
and acquiring multiple groups of pressure data within the duration corresponding to the land stepping event according to a preset data acquisition frequency.
In an optional embodiment, the analysis processing apparatus is further configured to:
according to the Hilbert characteristic diagram, drawing a plantar pressure distribution histogram and a plantar pressure distribution gray scale diagram corresponding to the object to be detected in the ground treading event;
and sending the sole pressure distribution histogram and the sole pressure distribution gray-scale map to the storage device for storage.
In an optional embodiment, the system further comprises an interface expansion device;
the interface expanding device is used for providing an access interface for the mobile storage equipment and receiving a pressure data acquisition request sent by the mobile storage equipment to the storage device.
In an optional embodiment, the interface expansion apparatus further includes an identity authentication module;
the identity authentication module is used for confirming whether the pressure data acquisition request has corresponding data access authority or not when the interface expansion device receives the pressure data acquisition request;
when the pressure data acquisition request has corresponding data access right, allowing the pressure data corresponding to the pressure data acquisition request to be accessed in the storage device;
and when the pressure data acquisition request does not have the corresponding data access right, closing the access interface.
In an alternative embodiment, the detection system further comprises a sock-like housing;
the insole-shaped shell is fixed in the daily shoe of the object to be detected and used for accommodating the data acquisition device, the analysis processing device, the storage device and the interface expansion device.
In an optional embodiment, the detection system further comprises a fixing band;
the fixing band is arranged on the insole-shaped shell and used for fixing the insole-shaped shell and the foot of the object to be detected.
The motion state detection system based on plantar pressure that this disclosed embodiment provided includes: the detection system comprises a data acquisition device, an analysis processing device and a storage device which are sequentially in communication connection, wherein the detection system is worn at the sole of an object to be detected; the data acquisition device is used for detecting a ground treading event corresponding to the object to be detected and acquiring a plurality of groups of pressure data corresponding to the ground treading event, wherein each group of pressure data comprises pressure data at a plurality of preset sampling positions in the sole of the object to be detected; the analysis processing device is used for determining the pace corresponding to the land stepping event; aiming at each group of the multiple groups of pressure data, determining a Hilbert characteristic diagram corresponding to the group of pressure data, and the corresponding landing time length of each pressure data in the group of pressure data in the event of landing; inputting the Hilbert characteristic diagram, the pace and the landing duration into a pre-trained motion state classification model, and determining a motion state corresponding to each pressure data; and marking the corresponding motion state for the pressure data, and sending the motion state to a storage device for storage. The vola pressure data of the object to be detected in different motion states in real life daily activities can be detected, the motion state of the object to be detected can be automatically marked for the detected vola pressure data, the applicability is higher, and the efficiency of marking the corresponding motion state for the vola pressure data is improved.
In order to make the aforementioned objects, features and advantages of the present disclosure more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings required for use in the embodiments will be briefly described below, and the drawings herein incorporated in and forming a part of the specification illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the technical solutions of the present disclosure. It is appreciated that the following drawings depict only certain embodiments of the disclosure and are therefore not to be considered limiting of its scope, for those skilled in the art will be able to derive additional related drawings therefrom without the benefit of the inventive faculty.
Fig. 1 is a schematic structural diagram illustrating a motion state detection system based on plantar pressure according to an embodiment of the present disclosure;
FIG. 2 is a schematic diagram illustrating a first-order to third-order Hilbert feature provided by an embodiment of the present disclosure;
fig. 3 is a second schematic structural diagram of a motion state detection system based on plantar pressure according to an embodiment of the present disclosure;
fig. 4 shows a third schematic structural diagram of a motion state detection system based on plantar pressure according to an embodiment of the present disclosure.
Illustration of the drawings: 100-a motion state detection system; 110-a data acquisition device; 111-pressure sensor group; 112-analog switches; 113-an analog-to-digital converter; 120-an analytical processing device; 121-random access memory; 130-a storage device; 140-interface expansion means; 141-identity authentication module; 150-insole-like shell; 160-fixing belt.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present disclosure more clear, the technical solutions of the embodiments of the present disclosure will be described clearly and completely with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are only a part of the embodiments of the present disclosure, not all of the embodiments. The components of the embodiments of the present disclosure, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present disclosure, presented in the figures, is not intended to limit the scope of the claimed disclosure, but is merely representative of selected embodiments of the disclosure. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the disclosure without making creative efforts, shall fall within the protection scope of the disclosure.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
The term "and/or" herein merely describes an associative relationship, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality, for example, including at least one of A, B, C, and may mean including any one or more elements selected from the group consisting of A, B and C.
According to researches, at present, conventional plantar pressure detection needs to be carried out in a gait laboratory, a test subject is required to complete barefoot testing on a force measuring flat plate, but in the testing mode, bare feet of a patient are required to walk on a rigid flat plate in the testing process, if plantar pressure distribution of daily activities such as ascending and descending is tested, only a simulation mode can be adopted, plantar pressure distribution of the patient in different motion states in the daily activities of real life cannot be obtained, motion states corresponding to detected plantar pressure data need to be recorded manually, and data acquisition efficiency is low.
Based on the research, the present disclosure provides a motion state detection system based on plantar pressure, including: the detection system comprises a data acquisition device, an analysis processing device and a storage device which are sequentially in communication connection, wherein the detection system is worn at the sole of an object to be detected; the data acquisition device is used for detecting a ground treading event corresponding to the object to be detected and acquiring a plurality of groups of pressure data corresponding to the ground treading event, wherein each group of pressure data comprises pressure data at a plurality of preset sampling positions in the sole of the object to be detected; the analysis processing device is used for determining the pace corresponding to the land stepping event; aiming at each group of the multiple groups of pressure data, determining a Hilbert characteristic diagram corresponding to the group of pressure data, and the corresponding landing time length of each pressure data in the group of pressure data in the event of landing; inputting the Hilbert characteristic diagram, the pace and the landing duration into a pre-trained motion state classification model, and determining a motion state corresponding to each pressure data; and marking the corresponding motion state for the pressure data, and sending the motion state to a storage device for storage. The vola pressure data of the object to be detected in different motion states in real life daily activities can be detected, the motion state of the object to be detected can be automatically marked for the detected vola pressure data, the applicability is higher, and the efficiency of marking the corresponding motion state for the vola pressure data is improved.
For the understanding of the present embodiment, a detailed description will be given to a motion state detection system based on plantar pressure disclosed in the embodiments of the present disclosure.
Referring to fig. 1, a schematic structural diagram of a motion state detection system 100 based on plantar pressure according to an embodiment of the present disclosure is shown.
As shown in fig. 1, the motion state detection system 100 includes: data acquisition device 110, analysis processing device 120 and storage device 130. The data acquisition device 110, the analysis processing device 120 and the storage device 130 are sequentially connected in communication.
In the practical application process, the motion state detection system 100 is worn on the sole of the object to be detected, can be integrated in daily shoes of the object to be detected, moves in daily life along with the object to be detected, and can acquire and store plantar pressure data of the object to be detected in real time while not interfering with the normal life of the object to be detected.
Specifically, the data acquisition device 110 is configured to detect a ground stepping event corresponding to the object to be detected, and acquire multiple sets of pressure data corresponding to the ground stepping event, where each set of pressure data includes pressure data at multiple preset sampling positions in a sole of the object to be detected.
The preset sampling position may be set according to factors such as foot type and walking habit of the object to be detected, and is not particularly limited herein.
As a possible implementation, the plantar pressure data collection process of the data collection device 110 may be: determining the duration corresponding to the land stepping event; and acquiring multiple groups of pressure data within the duration corresponding to the land stepping event according to a preset data acquisition frequency.
Here, when it is detected that the sole of the object to be detected applies pressure to the data acquisition device 110, an effective ground stepping event may be determined based on a threshold division manner, and in each ground stepping event, since a process is often required for the foot to completely land, for example, after the heel lands first, the sole falls, the foot completely lands for a period of time, the heel lifts, and then the forefoot lifts, the ground stepping event may be a foot movement process in a period from the time when the foot of the object to be detected contacts the ground to the time when the foot completely leaves the ground, the process may last for a period of time, and in this period of time, the data acquisition device 110 may acquire a plurality of sets of sole pressure data.
The data acquisition device 110 acquires plantar pressure data through constant data sampling frequency, the data acquisition device 110 is provided with a plurality of sampling characteristic points aiming at the plantar of an object to be detected, and pressure data of all the sampling characteristic points can be acquired in each acquisition process, so that the data acquisition device 110 acquires a plurality of pressure data of all the sampling characteristic points in each acquisition process as a group, and the group number of the pressure data is that a plurality of groups of pressure data are acquired through the constant data sampling frequency within the duration corresponding to a ground stepping event.
Illustratively, 48 sampling feature points are arranged on the sole of the subject to be detected, the data sampling frequency of the data acquisition device 110 is 50 times/second, in a certain ground stepping event, the duration from the heel landing to the sole off-ground of the subject to be detected is 1 second, then 50 sets of pressure data are acquired by the data acquisition device 110 in the ground stepping event, and each set of pressure data comprises 48 pressure data.
Further, the analysis processing device 120 is configured to determine a pace corresponding to the ground stepping event; aiming at each group of the multiple groups of pressure data, determining a Hilbert characteristic diagram corresponding to the group of pressure data, and the corresponding landing time length of each pressure data in the group of pressure data in the event of landing; inputting the Hilbert characteristic diagram, the pace and the landing duration into a pre-trained motion state classification model, and determining a motion state corresponding to each pressure data; and marking the corresponding motion state for the pressure data, and sending the pressure data to the storage device 130 for storage.
Here, the analysis processing device 120 determines the pace corresponding to the current stepping event by acquiring the time interval between two adjacent stepping events corresponding to the current stepping data and the interval distance between two adjacent stepping events.
The motion state corresponding to the pressure data may include: walking, running, ascending a slope, descending a slope and other motion states.
The Hilbert characteristic diagram can map the size of each pressure data into the gray level of each pixel point in the diagram, the pressure at each sampling characteristic point in each group of pressure data can be visually obtained according to the Hilbert characteristic diagram, the distribution condition of the plantar pressure of the object to be detected in the motion process under the group of pressure data can be analyzed according to the Hilbert characteristic diagram, and the motion state of the object to be detected in the process of collecting the group of pressure data can be analyzed and obtained by combining the pace and the landing duration.
As a possible implementation, the analysis processing apparatus is further configured to: and supplementing the data quantity corresponding to each group of pressure data to a preset sampling quantity by utilizing an interpolation method aiming at each group of pressure data.
Here, since the characteristic sampling points arranged on the data acquisition device 110 cannot completely cover all regions of the sole of the object to be detected, an interpolation method needs to be adopted for predicting the regions not covered by the characteristic sampling points for each set of pressure data, and the pressure data in the whole range of the sole of the object to be detected is supplemented as much as possible, so that the set of pressure data can accurately and comprehensively reflect the pressure distribution of the sole of the object to be detected.
Preferably, the value of the preset number of samples may be an integer power of 4, so as to facilitate the drawing of a subsequent hilbert feature map.
Optionally, the process of generating the hilbert feature map by the analysis processing device 120 may be to generate a 1-to-nth-order hilbert feature map from the time sequence signal according to a plurality of pressure data included in each set of pressure data, where a value of n may be determined according to a preset sampling number, for example, the number of data corresponding to the pressure data is supplemented to 64 by an interpolation method, that is, the number is 3 to the power of 4, and the hilbert feature map may be the 1-to-3-order hilbert feature map. Referring to fig. 2, fig. 2 is a schematic diagram illustrating a first-order to third-order hilbert feature diagram provided in an embodiment of the present application.
As a possible implementation, the analysis processing device 120 may train on the motion state classification model based on the following method: acquiring a preset training data set and a preset test data set corresponding to the motion state classification model; the preset training data set comprises the Hilbert feature map, the pace and the landing duration corresponding to a plurality of preset training pressure data, and a motion state label corresponding to each preset training pressure data; the preset test data set comprises the Hilbert characteristic diagram, the pace and the landing duration corresponding to a plurality of preset test pressure data, and a motion state label corresponding to each preset test pressure data; inputting the preset training data set into the motion state classification model, and training the motion state classification model; inputting the preset test data set into the trained motion state classification model, and determining the classification accuracy corresponding to the motion state classification model according to the classification result corresponding to the motion state classification model and the motion state label corresponding to the preset test pressure data; and when the classification accuracy is greater than a preset classification accuracy threshold, finishing training of the motion state classification model.
In the practical application process, at least 100 volunteers can be invited, and 100 steps of walking, running, ascending and descending are required to be respectively carried out by fixed actions, such as walking, running, ascending and descending, so that 10000 groups of pressure data with the walking, running, ascending and descending as data labels are obtained. The method comprises the steps of selecting 8000 groups as a training set, selecting 2000 groups as a test set, inputting the training set to a motion state classification model for training, testing the trained motion state classification model by using the test set after the motion state classification model is trained, observing whether a motion state classification result obtained by the motion state classification model is matched with a corresponding data label or not to obtain the classification accuracy of the motion state classification model, considering that the motion state classification model is trained when the classification accuracy is larger than a preset classification accuracy threshold, and continuing training after modifying the motion state classification model if the classification accuracy is smaller than the preset classification accuracy threshold until the classification accuracy is larger than the preset classification accuracy threshold.
Preferably, the preset classification accuracy threshold may be 95%.
As a possible implementation, the analysis processing device 120 may further be configured to: according to the Hilbert characteristic diagram, drawing a plantar pressure distribution histogram and a plantar pressure distribution gray scale diagram corresponding to the object to be detected in the ground treading event; and sending the plantar pressure distribution histogram and the plantar pressure distribution gray-scale map to the storage device 130 for storage.
Here, the analysis processing device 120 performs feature analysis on the hilbert feature map by using an image processing method, so as to extract a plantar pressure distribution histogram and a plantar pressure distribution gray scale map which reflect plantar pressure distribution of the object to be detected, the analysis processing device 120 sends the plantar pressure distribution histogram and the plantar pressure distribution gray scale map to the storage device 130 for storage, and medical staff can extract the plantar pressure distribution histogram and the plantar pressure distribution gray scale map as required to better analyze whether the gait of the object to be detected is abnormal.
The motion state detection system based on plantar pressure that this disclosed embodiment provided includes: the detection system comprises a data acquisition device, an analysis processing device and a storage device which are sequentially in communication connection, wherein the detection system is worn at the sole of an object to be detected; the data acquisition device is used for detecting a ground treading event corresponding to the object to be detected and acquiring a plurality of groups of pressure data corresponding to the ground treading event, wherein each group of pressure data comprises pressure data at a plurality of preset sampling positions in the sole of the object to be detected; the analysis processing device is used for determining the pace corresponding to the land stepping event; aiming at each group of the multiple groups of pressure data, determining a Hilbert characteristic diagram corresponding to the group of pressure data, and the corresponding landing time length of each pressure data in the group of pressure data in the event of landing; inputting the Hilbert characteristic diagram, the pace and the landing duration into a pre-trained motion state classification model, and determining a motion state corresponding to each pressure data; and marking the corresponding motion state for the pressure data, and sending the motion state to a storage device for storage. The vola pressure data of the object to be detected in different motion states in real life daily activities can be detected, the motion state of the object to be detected can be automatically marked for the detected vola pressure data, the applicability is higher, and the efficiency of marking the corresponding motion state for the vola pressure data is improved.
Referring to fig. 3, a second structural schematic view of a motion state detection system 100 based on plantar pressure according to an embodiment of the present disclosure is shown.
As shown in fig. 3, the motion state detection system 100 includes: data acquisition device 110, analysis processing device 120 and storage device 130. Wherein, data acquisition device 110 includes: a pressure sensor group 111, an analog switch 112, and an analog-to-digital converter 113. The analysis processing device 120 includes a random access memory 121.
Here, the pressure sensor group 111, the analog switch 112, and the analog-to-digital converter 113 are sequentially connected in communication; the analog-to-digital converter 113 is connected to a random access memory 121.
Specifically, the pressure sensor group 111 includes a plurality of pressure sensors, and the pressure sensors are arranged according to preset sole sampling feature points and are used for acquiring pressure data of each sole sampling feature point in a ground treading event.
Further, the analog switch 112 is configured to, for each of the plurality of pressure sensors, control the remaining pressure sensors to be in an off state when the pressure sensor is controlled to be in an operating state; after the pressure sensors in the current working state finish the pressure data acquisition, switching to the next pressure sensor in the working state according to a preset acquisition sequence, and controlling the rest pressure sensors in the turn-off state until all the pressure sensors finish the pressure data acquisition.
Here, in the working process of the motion state detection system 100, pressure data acquisition may be performed on the sole of an object to be detected by using a one-by-one sampling manner for a plurality of pressure sensors included in the pressure sensor group 111, a preset acquisition sequence may be acquisition line by line or acquisition column by column, and in an actual application process, selection may be performed according to actual needs, where no specific limitation is imposed, when the current pressure sensor performs data acquisition, the analog switch 112 controls all the remaining pressure sensors to be in an off state to prevent interference of the other pressure sensors on the currently working pressure sensor, after the current pressure sensor completes data acquisition, the next pressure sensor is controlled by the analog switch 112 to work according to the preset acquisition sequence, and all the remaining pressure sensors are in an off state.
As a possible embodiment, two analog switches 112 may be provided for performing precise positioning control on the row and the column of the pressure sensor group 111 where the pressure sensor that needs to be controlled to be turned off is located.
Further, the analog-to-digital converter 113 is configured to convert a voltage signal corresponding to the pressure data collected by the pressure sensor into a digital signal, and send the digital signal to the random access memory 121.
Here, the analog-to-digital converter 113 and the random access memory 121 are connected by a communication bus.
Further, the random access memory 121 is configured to receive and temporarily store the pressure data acquired by each pressure sensor, and transmit all the pressure data to the storage device 130 for storage until the pressure data acquired by all the pressure sensors is received.
Here, because the pressure sensors in the pressure sensor group 111 acquire pressure data one by one, in order to ensure the integrity of the pressure data acquired and stored in the storage device 130 each time, in the process of acquiring the pressure data one by one in the pressure sensor group 111, the random access memory 121 temporarily stores the acquired pressure data, after all the pressure sensors in the pressure sensor group 111 have been acquired, a corresponding motion state is marked for each pressure data in the acquired pressure data group, and then the pressure data group obtained in the acquisition process is sent to the storage device 130 as a whole for storage.
Thus, the processing load of the analysis processing device 120 can be reduced.
Referring to fig. 4, a third schematic structural diagram of a motion state detection system 100 based on plantar pressure according to an embodiment of the present disclosure is shown.
As shown in fig. 4, the motion state detection system 100 includes: data acquisition device 110, analysis processing device 120 and storage device 130. Wherein, data acquisition device 110 includes: a pressure sensor group 111, an analog switch 112, and an analog-to-digital converter 113. The analysis processing device 120 includes a random access memory 121. The motion state detection system 100 further includes: an interface widening device 140, a sock-like shell 150, and a securing strap 160. The interface expansion apparatus 140 includes: an identity authentication module 141.
Specifically, the interface expanding unit 140 is configured to provide an access interface for the mobile storage device, and receive a pressure data obtaining request sent by the mobile storage device to the storage unit 130. The identity authentication module 141 is configured to, when the interface expansion apparatus 140 receives the pressure data acquisition request, determine whether the pressure data acquisition request has a corresponding data access right; when the pressure data acquisition request has the corresponding data access right, allowing the pressure data corresponding to the pressure data acquisition request to be accessed in the storage device 130; and when the pressure data acquisition request does not have the corresponding data access right, closing the access interface.
Here, in the practical application process, the medical staff may use the mobile storage device to access the plantar pressure data stored in the storage device 130 through the insertion interface expanding device 140, when the mobile storage device is accessed to the interface expanding device 140, the identity authentication module 141 is preferably used to verify the access permission of the plantar pressure data of the patient needing to be accessed, and when and only when the medical staff has the access permission, the medical staff provides an access interface from the mobile storage device to the storage device 130, and the medical staff can normally access the plantar pressure data; if the healthcare worker does not have access permission for the patient, the access interface is closed.
Further, the insole-shaped shell 150 is fixed in the daily shoe of the object to be detected, and is used for accommodating the data acquisition device 110, the analysis processing device 120, the storage device 130 and the interface expansion device 140. The fixing band 160 is provided on the insole-like casing 150 for fixing the insole-like casing 150 to the foot of the subject to be detected.
Here, in order to synchronously perform the plantar pressure detection process in daily life and track the plantar pressure condition of the object to be detected for a long time, the exercise state detection system 100 is further provided with an insole-shaped shell 150, and the data acquisition device 110, the analysis processing device 120, the storage device 130 and the interface expansion device 140 are integrated into daily shoes of the object to be detected.
Therefore, the adaptation process is not needed in the process of detecting the plantar pressure of the object to be detected, the daily life of the object to be detected is not affected, and the applicability is good.
The motion state detection system based on plantar pressure that this disclosed embodiment provided includes: the detection system comprises a data acquisition device, an analysis processing device and a storage device which are sequentially in communication connection, wherein the detection system is worn at the sole of an object to be detected; the data acquisition device is used for detecting a ground treading event corresponding to the object to be detected and acquiring a plurality of groups of pressure data corresponding to the ground treading event, wherein each group of pressure data comprises pressure data at a plurality of preset sampling positions in the sole of the object to be detected; the analysis processing device is used for determining the pace corresponding to the land stepping event; aiming at each group of the multiple groups of pressure data, determining a Hilbert characteristic diagram corresponding to the group of pressure data, and the corresponding landing time length of each pressure data in the group of pressure data in the event of landing; inputting the Hilbert characteristic diagram, the pace and the landing duration into a pre-trained motion state classification model, and determining a motion state corresponding to each pressure data; and marking the corresponding motion state for the pressure data, and sending the motion state to a storage device for storage. The vola pressure data of the object to be detected in different motion states in real life daily activities can be detected, the motion state of the object to be detected can be automatically marked for the detected vola pressure data, the applicability is higher, and the efficiency of marking the corresponding motion state for the vola pressure data is improved.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the system and the apparatus described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. In the several embodiments provided in the present disclosure, it should be understood that the disclosed system, apparatus, and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present disclosure may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solution of the present disclosure may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present disclosure. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a magnetic disk, or an optical disk.
Finally, it should be noted that: the above-mentioned embodiments are merely specific embodiments of the present disclosure, which are used for illustrating the technical solutions of the present disclosure and not for limiting the same, and the scope of the present disclosure is not limited thereto, and although the present disclosure is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive of the technical solutions described in the foregoing embodiments or equivalent technical features thereof within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present disclosure, and should be construed as being included therein. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
Claims (10)
1. A motion state detection system based on plantar pressure is characterized by comprising: the system comprises a data acquisition device, an analysis processing device and a storage device which are sequentially in communication connection, wherein the system is worn at the sole of an object to be detected;
the data acquisition device is used for detecting a land stepping event corresponding to the object to be detected and acquiring a plurality of groups of pressure data corresponding to the land stepping event, wherein each group of pressure data comprises pressure data at a plurality of preset sampling positions in the sole of the object to be detected;
the analysis processing device is used for determining the pace corresponding to the land stepping event; for each group of the multiple groups of pressure data, determining a Hilbert characteristic diagram corresponding to the group of the pressure data, and a landing time length corresponding to each pressure data in the group of the pressure data in the land stepping event;
inputting the Hilbert feature map, the pace and the landing duration into a pre-trained motion state classification model, and determining a motion state corresponding to each piece of pressure data;
and marking the corresponding motion state for the pressure data, and sending the motion state to the storage device for storage.
2. The system of claim 1, wherein the data acquisition device comprises: the pressure sensor group, the analog switch and the analog-digital converter; the analysis processing device comprises a random access memory;
the pressure sensor group, the analog switch and the analog-to-digital converter are sequentially in communication connection; the analog-to-digital converter is connected to the random access memory;
the pressure sensor group comprises a plurality of pressure sensors, and the pressure sensors are arranged according to preset sole sampling characteristic points and are used for acquiring pressure data of each sole sampling characteristic point in the ground treading event;
the analog switch is used for controlling the rest of the pressure sensors to be in a turn-off state when controlling the pressure sensor to be in a working state for each of the plurality of pressure sensors; after the pressure sensors in the current working state finish the pressure data acquisition, switching to the next pressure sensor in the working state according to a preset acquisition sequence, and controlling the rest pressure sensors to be in the off state until all the pressure sensors finish the pressure data acquisition;
the analog-to-digital converter is used for converting the voltage signal corresponding to the pressure data acquired by the pressure sensor into a digital signal and sending the digital signal to the random access memory;
the random access memory is used for receiving and temporarily storing the pressure data acquired by each pressure sensor, and transmitting all the pressure data to the storage device for storage until all the pressure data acquired by all the pressure sensors are received.
3. The system of claim 1, wherein the analysis processing device is further configured to:
acquiring a preset training data set and a preset test data set corresponding to the motion state classification model;
the preset training data set comprises the Hilbert feature map, the pace and the landing duration corresponding to a plurality of preset training pressure data, and a motion state label corresponding to each preset training pressure data;
the preset test data set comprises the Hilbert characteristic diagram, the pace and the landing duration corresponding to a plurality of preset test pressure data, and a motion state label corresponding to each preset test pressure data;
inputting the preset training data set into the motion state classification model, and training the motion state classification model;
inputting the preset test data set into the trained motion state classification model, and determining the classification accuracy corresponding to the motion state classification model according to the classification result corresponding to the motion state classification model and the motion state label corresponding to the preset test pressure data;
and when the classification accuracy is greater than a preset classification accuracy threshold, finishing training of the motion state classification model.
4. The system of claim 1, wherein the analysis processing device is further configured to:
and supplementing the data quantity corresponding to each group of pressure data to a preset sampling quantity by utilizing an interpolation method aiming at each group of pressure data.
5. The system of claim 1, wherein the data acquisition device is specifically configured to:
determining the duration corresponding to the land stepping event;
and acquiring multiple groups of pressure data within the duration corresponding to the land stepping event according to a preset data acquisition frequency.
6. The system of claim 1, wherein the analysis processing device is further configured to:
according to the Hilbert characteristic diagram, drawing a plantar pressure distribution histogram and a plantar pressure distribution gray scale diagram corresponding to the object to be detected in the ground treading event;
and sending the sole pressure distribution histogram and the sole pressure distribution gray-scale map to the storage device for storage.
7. The system of claim 1, further comprising an interface expansion device;
the interface expanding device is used for providing an access interface for the mobile storage equipment and receiving a pressure data acquisition request sent by the mobile storage equipment to the storage device.
8. The system of claim 7, wherein the interface expansion device further comprises an identity authentication module;
the identity authentication module is used for confirming whether the pressure data acquisition request has corresponding data access authority or not when the interface expansion device receives the pressure data acquisition request;
when the pressure data acquisition request has corresponding data access right, allowing the pressure data corresponding to the pressure data acquisition request to be accessed in the storage device;
and when the pressure data acquisition request does not have the corresponding data access right, closing the access interface.
9. The system of claim 7, wherein the detection system further comprises a sock-like shell;
the insole-shaped shell is fixed in the daily shoe of the object to be detected and used for accommodating the data acquisition device, the analysis processing device, the storage device and the interface expansion device.
10. The system of claim 9, wherein the detection system further comprises a securing strap;
the fixing band is arranged on the insole-shaped shell and used for fixing the insole-shaped shell and the foot of the object to be detected.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210052518.4A CN114176571A (en) | 2022-01-18 | 2022-01-18 | Motion state detection system based on sole pressure |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210052518.4A CN114176571A (en) | 2022-01-18 | 2022-01-18 | Motion state detection system based on sole pressure |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114176571A true CN114176571A (en) | 2022-03-15 |
Family
ID=80606909
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210052518.4A Pending CN114176571A (en) | 2022-01-18 | 2022-01-18 | Motion state detection system based on sole pressure |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114176571A (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105266801A (en) * | 2015-09-10 | 2016-01-27 | 太原理工大学 | J wave detection and classification method based on probabilistic neural network (PNN) |
| CN110420029A (en) * | 2019-08-03 | 2019-11-08 | 苏州自如医疗器械有限公司 | A kind of walking step state wireless detecting system based on Multi-sensor Fusion |
| CN111582108A (en) * | 2020-04-28 | 2020-08-25 | 河北工业大学 | Gait recognition and intention perception method |
| CN112842342A (en) * | 2021-01-25 | 2021-05-28 | 北京航空航天大学 | Electrocardiogram and magnetic signal classification method combining Hilbert curve and integrated learning |
| US20210365342A1 (en) * | 2020-05-25 | 2021-11-25 | Wuhan University | Method and system for power equipment diagnosis based on windowed feature and hilbert visualization |
-
2022
- 2022-01-18 CN CN202210052518.4A patent/CN114176571A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105266801A (en) * | 2015-09-10 | 2016-01-27 | 太原理工大学 | J wave detection and classification method based on probabilistic neural network (PNN) |
| CN110420029A (en) * | 2019-08-03 | 2019-11-08 | 苏州自如医疗器械有限公司 | A kind of walking step state wireless detecting system based on Multi-sensor Fusion |
| CN111582108A (en) * | 2020-04-28 | 2020-08-25 | 河北工业大学 | Gait recognition and intention perception method |
| US20210365342A1 (en) * | 2020-05-25 | 2021-11-25 | Wuhan University | Method and system for power equipment diagnosis based on windowed feature and hilbert visualization |
| CN112842342A (en) * | 2021-01-25 | 2021-05-28 | 北京航空航天大学 | Electrocardiogram and magnetic signal classification method combining Hilbert curve and integrated learning |
Non-Patent Citations (1)
| Title |
|---|
| NASRULLOH R.B.S.L.等: "Hilbert Vector Convolutional Neural Network: 2D Neural Network on 1D Data", LECTURE NOTES IN COMPUTER SCIENCE, vol. 11727, pages 458 - 470 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Owings et al. | Measuring step kinematic variability on an instrumented treadmill: how many steps are enough? | |
| US9179862B2 (en) | Method and system for assessing locomotive bio-rhythms | |
| Sawers et al. | Beam walking can detect differences in walking balance proficiency across a range of sensorimotor abilities | |
| EP2470076B1 (en) | Characterizing a physical capability by motion analysis | |
| US11837365B1 (en) | Assessing fitness by entropy and bispectral analysis | |
| WO2014118767A1 (en) | Classifying types of locomotion | |
| Macleod et al. | Development and validation of a low-cost, portable and wireless gait assessment tool | |
| KR102313044B1 (en) | Method and system for evaluating elderly health status using characteristics of walk-in-place | |
| Panagiota et al. | Assessment of human gait speed and energy expenditure using a single triaxial accelerometer | |
| US20210145367A1 (en) | Systems, devices, and methods for determining injury risk and athletic readiness | |
| KR20190120922A (en) | Method and system for walking ability prediction using stepping characteristics information | |
| Majumder et al. | A wireless IoT system towards gait detection in stroke patients | |
| KR20190120923A (en) | Method and system for walking ability prediction using foot characteristics information | |
| CN114176571A (en) | Motion state detection system based on sole pressure | |
| McCalmont et al. | eZiGait: toward an AI gait analysis and sssistant system | |
| Loose et al. | A Public Dataset of Overground and Treadmill Walking in Healthy Individuals Captured by Wearable IMU and sEMG Sensors. | |
| Wiles et al. | NONAN GaitPrint: An IMU gait database of healthy young adults | |
| Weidensager et al. | Estimating vertical ground reaction forces from plantar pressure using interpretable high-dimensional approximation | |
| Márquez et al. | A Mobile, Smart Gait Assessment System for Asymmetry Detection Using Machine Learning-Based Classification | |
| D’Arco et al. | U2FSM: Unsupervised Square Finite State Machine for Gait Events Estimation from Instrumented Insoles | |
| KR102404850B1 (en) | Method and system for frailty prediction using gait parameters | |
| EP3915473B1 (en) | Information processing program, information processing method, and information processing system | |
| Catalfamo et al. | Comparison of kinematic and pressure measurement reference methods used in gait event detection | |
| Skovbjerg et al. | Monitoring physical behavior in rehabilitation: a development and validation study of a machine learning-based algorithm for thigh mounted accelerometers | |
| Razak et al. | Wireless Integrated Gait Analysis System for Heel-Strike and Toe-Off Measurement |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |