Hyperactivity behavior monitoring method, apparatus, system, vest, storage medium and electronic device

Abstract

The invention provides a hyperactivity behavior monitoring method, device and system, a vest, a storage medium and electronic equipment, and relates to the technical field of intelligent monitoring. The method comprises the following steps: receiving multi-channel time series behavior data of a user, wherein the multi-channel time series behavior data comprises user behavior data collected by sensors placed at a plurality of body parts of the user; and inputting the multi-channel time sequence behavior data into a deep neural network, and intervening a user based on a recognition result of the multi-action behavior pattern output by the deep neural network. The multi-channel time sequence behavior data of the user is collected through the sensors placed at the multiple body parts of the user, the stability is higher than that of data collected at a single part, and the recognition result is more accurate; the multi-channel time sequence behavior data is processed and recognized through the deep neural network, and compared with judgment through simple threshold comparison, a more reliable and more accurate recognition result can be obtained.

Description

(19) State Intellectual Property Office (12) Invention Patent Application (10) Application Publication Number (43) Application Publication Date (21) Application Number 202411992749.9 (22) Application Date 2024.12.31 (71) Applicant: Hong Kong Textile and Apparel Research & Development Centre Limited Address: Room R906, Chen Bao Xueying Building, Hong Kong Polytechnic University, Kowloon, Hong Kong, China (72) Inventors: Fu Hongqiu Minyu (74) Patent Agency: Longtian Intellectual Property Agency Limited 72003 Patent Attorney: Shi Haixia (51) Int.Cl. A61B 5 / 16 (2006.01) A61B 5 / 11 (2006.01) A61B 5 / 00 (2006.01) (54) Invention Title: Multi-Motion Behavior Monitoring Method, Apparatus, System, Vest, Storage Medium, and Device (57) Abstract: This disclosure provides a multi-motion behavior monitoring method, apparatus, system, vest, storage medium, and electronic device, relating to the field of intelligent monitoring technology. The method includes: receiving multi-channel time-series behavior data of a user, wherein the multi-channel time-series behavior data includes user behavior data collected by sensors placed on multiple parts of the user's body; inputting the multi-channel time-series behavior data into a deep neural network; and intervening in the user based on the recognition results of the multi-motion behavior patterns output by the deep neural network. Collecting multi-channel time-series behavior data of a user by sensors placed on multiple parts of the user's body is more stable and the recognition results are more accurate than data collected from a single part; and processing and recognizing multi-channel time-series behavior data through a deep neural network, compared with judgment by simple threshold comparison, can obtain more reliable and accurate recognition results. Claims 2 pages, Description 9 pages, Drawings 4 pages, CN 122296885 A 2026.06.30 CN 1 22 29 68 85 A 1. A method for monitoring multi-movement behavior, characterized in that it includes: receiving multi-channel time-series behavior data of a user, wherein the multi-channel time-series behavior data includes user behavior data collected by sensors placed on multiple body parts of the user; inputting the multi-channel time-series behavior data into a deep neural network, and receiving the recognition result of the multi-movement behavior pattern output by the deep neural network; intervening in the user based on the recognition result of the multi-movement behavior pattern. 2. The method according to claim 1, wherein the sensors include an accelerometer and a gyroscope; and / or the deep neural network includes a convolutional neural network (CNN). 3. The method according to claim 1, wherein the method further includes: displaying the multi-channel time-series behavior data and the recognition result in a visualization interface. 4. The method according to any one of claims 1 to 3, wherein the multi-movement behavior pattern...Intervention on the user based on the recognition result includes: when the recognition result indicates that the user exhibits hyperactivity, alerting the user by placing a vibrator on a predetermined body part of the user. 5. A vest, characterized in that it comprises: a vest body; a first sensor receiving portion located at the shoulder portion of the vest body for receiving and fixing a first sensor; a second sensor receiving portion located at the back portion of the vest body for receiving and fixing a second sensor. 6. The vest according to claim 5, characterized in that it further comprises: a first sensor detachably located at the first sensor receiving portion; a second sensor detachably located at the second sensor receiving portion; a vibrator receiving portion located at a predetermined body part of the vest body for receiving and fixing a vibrator; the vibrator being detachably located at the vibrator receiving portion. 7. The vest according to claim 5, characterized in that it further comprises: a first sensor detachably located at the first sensor receiving portion; a second sensor detachably located at the second sensor receiving portion; a vibrator for placement on a predetermined body part of the user. 8. The vest according to claim 6 or 7, further comprising: a communication module, configured to send multi-channel time-series behavioral data of the user collected by the first sensor and the second sensor to a control unit; and configured to receive control commands from the control unit and send them to the vibrator. 9. The vest according to claim 6 or 7, wherein the first sensor and the second sensor comprise a 6-axis inertial measurement unit, the inertial measurement unit comprising an accelerometer and a gyroscope. 10. The vest according to claim 6, further comprising: a connection channel for accommodating a connecting cable for electrically connecting the first sensor, the second sensor, and the vibrator to the communication module via the connecting cable. 11. A user multi-movement behavior monitoring device, characterized in that it comprises: a behavior data receiving unit, configured to receive multi-channel time-series behavior data of the user, the multi-channel time-series behavior data including user behavior data collected by sensors placed on multiple body parts of the user; a user behavior recognition unit, configured to input the multi-channel time-series behavior data into a deep neural network, and receive the recognition result of the multi-movement behavior pattern output by the deep neural network; and a user control unit, configured to control the user based on the recognition result of the multi-movement behavior pattern. 12. A user multi-movement behavior monitoring system, characterized in that it comprises a vest as described in any one of claims 5 to 10, and a monitoring device as described in claim 11. 13. An electronic device, characterized in that it comprises: a processor; and a memory for storing executable instructions of the processor;Wherein, the processor is configured to execute the method of any one of claims 1 to 4 by executing the executable instructions. 14. A computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the method of any one of claims 1 to 4. Claims 2 / 2 Page 3 CN 122296885 A Hyperactivity Monitoring Method, Apparatus, System, Vest, Storage Medium and Device Technical Field

[0001] This disclosure relates to the field of intelligent monitoring technology, and more particularly to a user hyperactivity monitoring method, apparatus, system, vest, storage medium and electronic device. Background Art

[0002] Currently, the technologies used to improve the classroom behavior of children with ADHD (Attention-Deficit / Hyperactivity Disorder) mainly include drug treatment, psychosocial intervention, self-monitoring technology and digital intervention. Drugs can usually effectively control ADHD symptoms, but may have side effects and are expensive. Psychosocial interventions usually focus on reducing disruptive and confrontational behaviors, which become ineffective shortly after the treatment period ends, and are difficult to manage in real time in the classroom. Self-monitoring technology helps individuals improve their self-regulation by allowing them to observe and record their own behavior. This technology requires sufficient self-discipline and can affect learning efficiency in a classroom environment. Early digital interventions with fixed vibration schedules can lead to habituation, and students may ignore the vibrations. Some existing technologies use personalized SMS reminders for alerts. However, these are primarily used to assist in executive functions such as planning, organization, and time management, and are not suitable for a classroom environment. Furthermore, reminders that require reading can easily distract attention.

[0003] It should be noted that the information disclosed in the above background section is only for enhancing the understanding of the background of this disclosure and may therefore include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0004] The object of this disclosure is to provide a method, apparatus, system, vest, storage medium, and electronic device for monitoring hyperactivity, at least to some extent overcoming the problem of inaccurate behavior monitoring in related technologies.

[0005] Other features and advantages of this disclosure will become apparent from the following detailed description or may be learned in part by practice of this disclosure.

[0006] According to one aspect of this disclosure, a method for monitoring ADHD behavior is provided, comprising: receiving multi-channel time-series behavioral data of a user, the multi-channel time-series behavioral data including user behavioral data collected by sensors placed on multiple body parts of the user; inputting the multi-channel time-series behavioral data into a deep neural network; receiving the recognition result of ADHD behavior patterns output by the deep neural network; and based on the recognition result of the ADHD behavior patterns...The user is intervened.

[0007] In one embodiment, the first sensor and the second sensor include an accelerometer and a gyroscope.

[0008] In one embodiment, the deep neural network includes a convolutional neural network (CNN).

[0009] In one embodiment, it further includes: displaying the multi-channel time-series behavioral data and the recognition result in a visualization interface.

[0010] In one embodiment, intervening in the user based on the recognition result of the hyperactive behavior pattern includes: when the recognition result indicates that the user has hyperactive behavior, alerting the user by placing a vibrator on a predetermined part of the user's body.

[0011] According to another aspect of the present disclosure, a vest is provided, including: a vest body; a first sensor receiving portion located at the shoulder portion of the vest body for receiving and fixing the first sensor; and a second sensor receiving portion located at the back portion of the vest body for receiving and fixing the second sensor.

[0012] In one embodiment, the vest further includes: a first sensor detachably located in the first sensor receiving portion; a second sensor detachably located in the second sensor receiving portion; a vibrator receiving portion located at a predetermined body part of the vest body for receiving a fixed vibrator; and a vibrator detachably located in the vibrator receiving portion.

[0013] In one embodiment, the vest further includes: a first sensor detachably located in the first sensor receiving portion; a second sensor detachably located in the second sensor receiving portion; and a vibrator for placement on a predetermined body part of the user.

[0014] In one embodiment, the vest further includes: a communication module for sending multi-channel time-series behavioral data of the user collected by the first sensor and the second sensor to a control unit; and for receiving control commands from the control unit and sending them to the vibrator.

[0015] In one embodiment, the first sensor and the second sensor include a 6-axis inertial measurement unit, which includes an accelerometer and a gyroscope.

[0016] In one embodiment, the vest further includes: a connection channel for receiving a connection cable for electrically connecting the first sensor, the second sensor, and the vibrator to the communication module via the connection cable.

[0017] According to another aspect of this disclosure, a user multi-movement behavior monitoring device is provided, comprising: a behavior data receiving unit, configured to receive multi-channel time-series behavior data of the user, the multi-channel time-series behavior data including user behavior data collected by sensors placed on multiple body parts of the user; and a user behavior recognition unit, configured to input the multi-channel time-series behavior data into a deep neural network, and receive the multi-movement behavior data output by the deep neural network.The recognition result of the pattern; a user control unit, used to control the user based on the recognition result of the hyperactivity pattern.

[0018] According to another aspect of the present disclosure, a user hyperactivity monitoring system is provided, including the vest as described above, and the user hyperactivity behavior monitoring device as described above.

[0019] According to yet another aspect of the present disclosure, an electronic device is provided, including: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the above-described method by executing the executable instructions.

[0020] According to yet another aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the above-described method.

[0021] The multi-movement behavior monitoring method, device, system, vest, storage medium, and electronic device provided in the embodiments of this disclosure collect multi-channel time-series behavior data of users by placing sensors on multiple parts of the user's body. This data has higher stability and more accurate recognition results than data collected from a single body part. Furthermore, processing and recognizing multi-channel time-series behavior data through deep neural networks yields more reliable and accurate recognition results compared to simple threshold comparisons.

[0022] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this disclosure. Brief Description of the Drawings

[0023] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. Obviously, the drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0024] Figure 1 shows a schematic diagram of a smart vest in one embodiment of the present disclosure;

[0025] Figure 2 shows a flowchart of a multi-movement behavior monitoring method in one embodiment of the present disclosure;

[0026] Figure 3 shows a network structure diagram of a CNN in one embodiment of the present disclosure;

[0027] Figure 4 shows a structural schematic diagram of a multi-movement behavior monitoring system in one embodiment of the present disclosure;

[0028] Figure 5 shows a flowchart of a multi-movement behavior monitoring method in another embodiment of the present disclosure;

[0029] Figure 6 shows a structural diagram of the hardware of a smart vest in one embodiment of the present disclosure;

[0030] Figure 7 shows a structural schematic diagram of a multi-movement behavior monitoring device in one embodiment of the present disclosure; and

[0031] Figure 8 shows a structural block diagram of a computer device in one embodiment of the present disclosure. Detailed Description

[0032] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments can be implemented in various forms.The embodiments described herein are intended to be implemented in a manner that is not intended to limit the scope of the examples set forth herein; rather, these embodiments are provided to make the disclosure more comprehensive and complete and to fully convey the concept of exemplary embodiments to those skilled in the art. The features, structures, or characteristics described may be combined in one or more embodiments in any suitable manner.

[0033] Furthermore, the accompanying drawings are merely illustrative illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0034] The solution provided in this application provides an intelligent vest that, in conjunction with a deep neural network, can identify and intervene in the user's hyperactive behaviors in real time.

[0035] For ease of understanding, several terms involved in this application will be explained below.

[0036] Artificial Intelligence (AI) is the theory, methods, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results. In other words, AI is a comprehensive technology in computer science that attempts to understand the essence of intelligence and produce a new kind of intelligent machine that can react in a way similar to human intelligence. AI studies the design principles and implementation methods of various intelligent machines, enabling them to have the functions of perception, reasoning, and decision-making.

[0037] Artificial intelligence technology is a comprehensive discipline involving a wide range of fields, including both hardware and software technologies. Basic AI technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operating / interactive systems, mechatronics, and other technologies. AI software technologies mainly include computer vision technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0038] Machine Learning (ML) is a multidisciplinary field involving probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory, and many other disciplines. It specifically studies how computers can simulate or implement human learning behavior to acquire new knowledge or skills and reorganize existing knowledge structures to continuously improve their performance. Machine learning is the core of artificial intelligence and the fundamental way to endow computers with intelligence; its applications span all areas of artificial intelligence. Machine learning and deep learning typically include artificial neural networks, belief networks, reinforcement learning, transfer learning, inductive learning, and formulaic learning.Teaching and learning technologies.

[0039] The solutions provided in this application involve technologies such as multi-movement behavior monitoring in artificial intelligence, which are specifically described in the following embodiment on page 3 / 9 of the specification, CN 122296885 A:

[0040] The steps of the multi-movement behavior monitoring method in this example embodiment will be described in more detail below with reference to the accompanying drawings and embodiments.

[0041] FIG1 shows a schematic diagram of a smart vest in one embodiment of the present disclosure. As shown in FIG1, the smart vest 100 includes: a vest body 11; a first sensor receiving part 12, located at the shoulder part of the vest body, for receiving and fixing a first sensor; and a second sensor receiving part 13, located at the back part of the vest body, for receiving and fixing a second sensor.

[0042] In one embodiment, the smart vest also includes a vibrator receiving part 14, located at a predetermined body part of the vest body, for receiving and fixing a vibrator. For example, located at a user-sensitive location such as the waist or abdomen.

[0043] In one embodiment, the smart vest further includes: a first sensor 15, detachably located in the first sensor housing 12; a second sensor 16, detachably located in the second sensor housing 13; and a vibrator 17, detachably located in the vibrator housing 14.

[0044] In one embodiment, the smart vest further includes a communication module 18, used to send multi-channel time-series behavioral data of the user collected by the first sensor 15 and the second sensor 16 to a control unit; and used to receive control commands from the control unit and send them to the vibrator 17. In one embodiment, the first and second sensors include accelerometers. In one embodiment, the first and second sensors include an accelerometer and a gyroscope. In one embodiment, the first and second sensors include a 6-axis inertial measurement unit, which includes an accelerometer and a gyroscope.

[0045] In one embodiment, the vest is a textile vest.

[0046] In the above embodiments, the smart vest comprehensively monitors the user's body movements through sensors placed on the shoulders and back. This broader monitoring function can more accurately detect hyperactive behaviors involving the entire body. This is an improvement over wrist-worn devices, which, while capable of detecting activity, rely primarily on hand and wrist movements. Furthermore, compared to wrist-worn devices, sensors integrated into the vest are less likely to be removed or adjusted by children, ensuring more consistent use.

[0047] The vest's hardware components, including sensors and other components, are designed for easy disassembly. This allows the textile vest to be washed, maintaining hygiene and usability for extended periods. Easy disassembly and reassembly ensure the vest retains its functionality and facilitates daily use. Compared to continuous medication or extensive treatment, the smart vest is cost-effective and less expensive.

[0048] In one embodiment, the smart vest includes a connection channel 19 for accommodating a connection wire to electrically connect the first sensor, the second sensor, and the vibrator to a communication module.

[0049] In one embodiment, the first sensor, the second sensor, and the vibrator have wireless communication capabilities and can communicate with a control unit. For example, via Bluetooth or WIFI.

[0050] In one embodiment, the first sensor can be positioned according to the user's dominant hand, located on the shoulder of the dominant hand. This allows for better measurement and monitoring.

[0051] FIG2 shows a flowchart of a multi-movement behavior monitoring method according to an embodiment of the present disclosure. The method provided by the embodiments of the present disclosure can be executed by any electronic device with computing power.

[0052] As shown in FIG2, S202, multi-channel time-series behavior data of the user is received, which includes user behavior data collected by sensors placed on multiple body parts of the user. In one embodiment, the sensors are placed on the user's shoulder and back. In one embodiment, the sensor includes an accelerometer for monitoring the user's motion and frequency. In one embodiment, the sensor includes a gyroscope for measuring the user's angular velocity. In one embodiment, the sensor includes a 6-axis inertial measurement unit, which includes an accelerometer and a gyroscope, and simultaneously measures the user's motion and posture. (Page 4 / 9 of the specification, CN 122296885 A)

[0053] S204, inputting multi-channel time-series behavioral data into a deep neural network, and receiving the recognition result of the hyperactive behavior pattern output by the deep neural network. In one embodiment, the deep neural network includes a convolutional neural network (CNN). In one embodiment, a 1D CNN network is used.

[0054] S206, intervening with the user based on the recognition result of the hyperactive behavior pattern.

[0055] In one embodiment, when the recognition result indicates that the user exhibits ADHD behavior, a vibrator is used to remind the user.

[0056] In the above embodiments, collecting multi-channel time-series behavioral data of the user by placing sensors on multiple body parts is more stable and yields more accurate recognition results than data collected from a single body part; and processing and recognizing multi-channel time-series behavioral data through a deep neural network, compared to judging by simple threshold comparison, can obtain more reliable and accurate recognition results.

[0057] Furthermore, data is collected by sensors placed on the shoulders and back, which are more robust than sensors placed on the wrists, waist, etc., resulting in more accurate recognition results.

[0058] In one embodiment, multi-channel time-series behavioral data and recognition results are displayed in a visualization interface, which shows user behavior data composed of accelerometer and gyroscope data and recognition results of multi-movement behavior patterns.

[0059] The technical solution of this disclosure provides a non-drug intervention for children with ADHD, avoiding potential side effects related to drugs.

[0060] Figure 3 shows a CNN network structure diagram in one embodiment of this disclosure. As shown in Figure 3, the CNN network model includes an input layer 31, convolutional layers 32 and 34, pooling layers 33 and 35, a fully connected layer 36, and an output layer 37. The input layer represents the input data, i.e., time-series behavioral data; the output layer represents the output recognition result. In one embodiment, the time-series behavioral data collected by the first and second sensors can be concatenated into a 1-dimensional vector and input into the CNN network. The output recognition result can include hyperactive behavior and non-hyperactive behavior (yes / no), and can also include different levels of hyperactive behavior, such as 3 or 5 levels of hyperactivity.

[0061] Figure 4 shows a schematic diagram of the structure of an ADHD behavior monitoring system in one embodiment of this disclosure. As shown in Figure 4, the monitoring system includes a smart vest 41 and a control unit 42. The smart vest 41 can be the vest shown in Figure 1. The smart vest 41 transmits multi-channel time-series behavioral data of the user collected by the first and second sensors to the control unit 42 via wired or wireless means; receives control commands from the control unit 42 and sends them to the vibrator. The control unit 42 is used to receive the user's multi-channel time-series behavioral data, wherein the multi-channel time-series behavioral data includes user behavior data collected by sensors placed on multiple parts of the user's body; inputs the multi-channel time-series behavioral data into a deep neural network, receives the recognition results of the multi-movement behavior patterns output by the deep neural network; and sends control commands to the smart vest 41 based on the recognition results of the multi-movement behavior patterns to control the user.

[0062] In one embodiment, the control unit 42 sends the recognition results to the teacher's terminal 43 (e.g., the teacher's mobile phone) to remind the teacher to pay attention to the student's behavior.

[0063] In one embodiment, the control unit 42 sends the received data and recognition results to the cloud 44 database for storage, and the parent's terminal 45 can view the relevant data through the cloud; the doctor's terminal 46 can analyze and process the collected relevant data with authorization.

[0064] In the above embodiments, the monitoring system is used for classroom behavior management of children with ADHD. It improves children's classroom performance by intervening in their hyperactivity in the classroom environment in real time and assisting teachers in classroom management. Furthermore, parents can remotely monitor their child's classroom status through the system. Additionally, parents can use the smart vest at home to help their child reduce hyperactivity while doing homework. (Instruction manual, pages 5 / 9, 8 CN 122296885 A

[0065] ) Further, in one embodiment, the smart vest can be applied to differentiate between ADHD and [other conditions] by analyzing behavioral patterns.For non-ADHD children, this aids in early diagnosis.

[0066] Furthermore, the behavioral data collected through the smart vest can support treatment plans by providing therapists with real-time data, enabling them to develop more personalized and effective treatment plans based on each child's unique behavioral patterns and treatment responses. In addition, the smart vest can be combined with personalized learning schedules and other executive function strategies to improve academic performance and optimize the learning experience for children with ADHD by adjusting interventions according to individual needs. Outside the classroom, the vest can also be used in various environments, such as special education schools, counseling centers, and home environments. It can be adapted for behavioral monitoring and intervention for other conditions (such as autism and anxiety disorders), providing a wider range of applications for this disclosure.

[0067] Figure 5 shows a flowchart of a method for monitoring ADHD behavior in another embodiment of this disclosure.

[0068] As shown in Figure 5, S502, multi-channel time-series behavioral data of the child in the classroom is obtained through sensors on the shoulders and back of the smart vest worn by the child. The sensors, for example, employ 6-axis inertial sensors (i.e., 6-axis inertial measurement units). The 6-axis inertial sensor detects not only the user's movements and frequency but also the user's posture.

[0069] S504, the control unit receives the child's multi-channel time-series behavioral data. The control unit receives data collected by sensors, for example, wirelessly or via wired means.

[0070] S506, the control unit inputs the multi-channel time-series behavioral data into a CNN network and receives the recognition results of the hyperactive behavior patterns output by the CNN network. In one embodiment, a 1D CNN network is used.

[0071] S508, the control unit determines that the child has hyperactive behavior based on the recognition results and sends a vibration command to the vibrator.

[0072] S510, the vibrator receives the vibration command from the control unit, vibrates, provides intervention, and reminds the child to improve their behavior.

[0073] S512, the control unit sends a reminder message to the teacher, reminding the teacher to pay attention to the child's state.

[0074] In addition, classroom performance can be scored based on the child's classroom behavior data and the effect after intervention, realizing a classroom performance scoring mechanism.

[0075] In the above embodiments, a smart vest is provided to improve the classroom behavior of school-aged children with ADHD. Wearable sensing and deep learning are used to detect hyperactive behaviors in the classroom and provide vibration intervention to improve their performance, thus achieving automated classroom behavior intervention for children with ADHD. Two 6-axis inertial sensors are used to provide multi-channel and real-time classroom behavior data collection and analysis to ensure data reliability and timeliness. Through the smart vest and deep learning model, automated and real-time behavior intervention is achieved, reducing reliance on manual intervention. The collected behavior data is analyzed, identified, and fed back in real time through the deep learning model, identifying hyperactive behaviors in the classroom in real time; vibration intervention...A pre-mechanism is in place to immediately provide effective vibration intervention when hyperactive behavior is detected, reminding the child to improve their behavior. Furthermore, the behavior data collection device is integrated into a comfortable textile vest to provide a good wearing experience and increase the child's acceptance.

[0076] Figure 6 shows a structural diagram of the hardware of a smart vest in one embodiment of the present disclosure. As shown in Figure 6, the hardware of the smart vest includes a control unit 61, which is implemented, for example, by a microprocessor; a first sensor 62, a second sensor 63, a vibrator 64, a wireless communication module 65, and a power module 66. The wireless communication module 65 is used to communicate with a backend server; the power module 66 is used to provide power to the various components. In one embodiment, a memory 67 is also included for storing collected data and recognition results.

[0077] Figure 7 shows a structural schematic diagram of a hyperactivity monitoring device in one embodiment of the present disclosure. As shown in Figure 7, the user ADHD behavior monitoring device 700 includes: a behavior data receiving unit 71, used to receive multi-channel time-series behavior data of the user, the multi-channel time-series behavior data including user behavior data collected by sensors placed on multiple body parts of the user; a user behavior recognition unit 72, used to input the multi-channel time-series behavior data into a deep neural network and receive the recognition result of the hyperactivity behavior pattern output by the deep neural network; and a user control unit 73, used to control the user based on the recognition result of the hyperactivity behavior pattern.

[0078] In an embodiment of this disclosure, the smart vest includes two 6-axis inertial measurement units. The 6-axis inertial sensors can measure accelerometer and gyroscope data, thereby comprehensively and accurately tracking complex movements and postures. The sensors are strategically placed on the shoulders and back. The vest uses elastic bands, for example, to fix the sensors in place, ensuring that they remain in the correct position. This consistent placement is important for accurate monitoring and intervention, and it can ensure reliable data collection throughout use. This disclosure employs deep learning algorithms, particularly 1D convolutional neural networks, to analyze time-series behavioral data collected from sensors, extract meaningful features, and identify patterns indicating hyperactive behavior. Using 1D CNN improves the accuracy and reliability of behavior detection, ensuring timely and appropriate intervention.

[0079] It should be noted that the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention and are not intended to limit the scope. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may, for example, be executed synchronously or asynchronously in multiple modules.

[0080] Those skilled in the art will understand that various aspects of the present invention can be implemented as systems, methods, or...Program product. Therefore, various aspects of the present invention can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software aspects, which can be collectively referred to herein as a "circuit", "module" or "system".

[0081] An electronic device 800 according to such an embodiment of the present invention will now be described with reference to FIG8. The electronic device 800 shown in FIG8 is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0082] As shown in FIG8, the electronic device 800 is manifested in the form of a general-purpose computing device. The components of the electronic device 800 may include, but are not limited to: at least one processing unit 810 described above, at least one storage unit 820 described above, and a bus 830 connecting different system components (including storage unit 820 and processing unit 810).

[0083] Wherein, the storage unit stores program code, which can be executed by the processing unit 810, causing the processing unit 810 to perform the steps of the various exemplary embodiments of the present invention described in the "Exemplary Methods" section above. For example, the processing unit 810 can execute S202 as shown in FIG2, receiving multi-channel time-series behavioral data of the user, the multi-channel time-series behavioral data including user behavioral data collected by sensors placed on multiple body parts of the user; S204, inputting the multi-channel time-series behavioral data into a deep neural network, and receiving the recognition result of the multi-movement behavioral pattern output by the deep neural network; S206, controlling the user based on the recognition result of the multi-movement behavioral pattern.

[0084] The storage unit 820 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 8201 and / or a cache storage unit 8202, and may further include a read-only memory unit (ROM) 8203.

[0085] The storage unit 820 may also include a program / utility 8204 having a set (at least one) program module 8205, such program module 8205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0086] The bus 830 can be one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0087] The electronic device 800 can also communicate with one or more external devices 700 (e.g., a keyboard, pointing device, Bluetooth device, etc.), and can also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or with devices that enable interaction between the user and the electronic device 600, as described on pages 7 / 9 of the specification.122296885 A The electronic device 800 can communicate with any device (e.g., router, modem, etc.) that can communicate with one or more other computing devices. Such communication can be performed through input / output (I / O) interface 850. The electronic device 800 can be connected to a display / screen via graphics card 870 to display multi-channel time series behavioral data and the recognition results in a visualization window. Furthermore, the electronic device 800 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN) and / or public network, such as the Internet) via network adapter 860. As shown, network adapter 860 communicates with other modules of the electronic device 800 via bus 830. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the electronic device 800, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0088] Through the above description of the embodiments, those skilled in the art will readily understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, portable hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the method according to the embodiments of this disclosure.

[0089] In an exemplary embodiment of this disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the methods described above in this specification is stored. In some possible embodiments, various aspects of the present invention can also be implemented in the form of a program product, which includes program code, which, when the program product is run on a terminal device, is used to cause the terminal device to perform the steps of the various exemplary embodiments of the present invention described in the "Exemplary Methods" section above in this specification.

[0090] A program product for implementing the above method according to an embodiment of the present invention has been described, which may employ a portable compact disk read-only memory (CD-ROM) and include program code, and can run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, a readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0091] The program product can take any combination of one or more readable media. A readable medium can be a readable signal medium or a readable storage medium. A readable storage medium can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or other types of media.A semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0092] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, wherein readable program code is carried. Such propagated data signal may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device.

[0093] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0094] Program code for performing the operations of the present invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user's computing device through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0095] In embodiments of this disclosure, data collected by multiple 6-axis inertial sensors is analyzed using a deep learning neural network model to identify hyperactive behaviors of ADHD children in the classroom, and these behaviors are effectively intervened through a vibration intervention mechanism. This design combines sensing technology with intervention mechanisms, providing an innovative solution for improving classroom behavior in children with ADHD.

[0096] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, one module or unit described above...The features and functions of a block or unit can be further divided into multiple modules or units to be embodied.

[0097] Furthermore, although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps, etc.

[0098] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, mobile hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, mobile terminal, or network device, etc.) to execute the method according to the embodiments of this disclosure.

[0099] Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the appended claims. Specification 9 / 9 pages 12 CN 122296885 A Figure 1 Figure 2 Specification Drawings 1 / 4 pages 13 CN 122296885 A Figure 3 Figure 4 Specification Drawings 2 / 4 pages 14 CN 122296885 A Figure 5 Figure 6 Specification Drawings 3 / 4 pages 15 CN 122296885 A Figure 7 Figure 8 Specification Drawings 4 / 4 pages 16 CN 122296885 A Abstract The present disclosure provides a hyperactivity behavior monitoring method, an apparatus, a system, a vest, a storage medium, and an electronic device, and relates to the technical field of intelligent monitoring. The method comprises:receiving multi-channel time-series behavior data of a user, where the multi-channel time-series behavior data includes user behavior data collected by sensors arranged at a plurality of body parts of the user; inputting the multi-channel time-series behavior data into a deep neural network, and intervening the user according to a recognition result of a hyperactivity behavior pattern output by the deep neural network. Collecting the user’s multi-channel time-series behavior data via sensors deployed on multiple body parts of the user achieves higher data stability and more accurate recognition results compared with data collected from a single body part. Furthermore, processing and recognizing the multi-channel time-series behavior data through the deep neural network can yield more reliable and precise recognition results, in contrast to judgment based on simple threshold comparison.

Claims

1. A method for monitoring multi-movement behavior, characterized in that, include: Receive multi-channel time-series behavioral data of the user, the multi-channel time-series behavioral data including user behavioral data collected by sensors placed on multiple parts of the user's body; The multi-channel time series behavioral data is input into a deep neural network, and the recognition results of the multi-movement behavioral patterns output by the deep neural network are received. Intervention is carried out on the user based on the identification results of the aforementioned hyperactive behavior patterns.

2. The method according to claim 1, characterized in that, The sensors include accelerometers and gyroscopes; and / or The deep neural network includes a convolutional neural network (CNN).

3. The method according to claim 1, characterized in that, The method further includes: The multi-channel time series behavioral data and the recognition results are displayed in a visualization interface.

4. The method according to any one of claims 1 to 3, characterized in that, The intervention on the user based on the recognition results of the hyperactive behavior pattern includes: When the identification result indicates that the user is exhibiting hyperactivity, the user is alerted by a vibrator placed on a predetermined part of the user's body.

5. A vest, characterized in that, include: Vest body; The first sensor receiving part is located at the shoulder part of the vest body and is used to receive and fix the first sensor. The second sensor receiving part is located on the back of the vest body and is used to receive and fix the second sensor.

6. The vest according to claim 5, characterized in that, Also includes: The first sensor is detachably located in the first sensor housing. The second sensor is detachably located in the second sensor housing. A vibrator receiving section, located at a predetermined body part of the vest body, is used to house and fix a vibrator; The vibrator is detachably located in the vibrator housing.

7. The vest according to claim 5, characterized in that, Also includes: The first sensor is detachably located in the first sensor housing. The second sensor is detachably located in the second sensor housing. A vibrator for placement on a predetermined part of the user's body.

8. The vest according to claim 6 or 7, characterized in that, Also includes: The communication module is used to send the user's multi-channel time-series behavioral data collected by the first sensor and the second sensor to the control unit; Used to receive control commands from the control unit and send them to the vibrator.

9. The vest according to claim 6 or 7, characterized in that, The first sensor and the second sensor include a 6-axis inertial measurement unit, which includes an accelerometer and a gyroscope.

10. The vest according to claim 6, characterized in that, Also includes: A connection channel is provided to accommodate a connection cable for electrically connecting the first sensor, the second sensor, and the vibrator to the communication module via the connection cable.

11. A user multi-activity monitoring device, characterized in that, include: A behavior data receiving unit is used to receive multi-channel time-series behavior data of the user, the multi-channel time-series behavior data including user behavior data collected by sensors placed on multiple body parts of the user; The user behavior recognition unit is used to input the multi-channel time series behavior data into a deep neural network and receive the recognition results of the multi-movement behavior patterns output by the deep neural network. The user control unit is used to control the user based on the recognition results of the multi-activity behavior pattern.

12. A user multi-activity monitoring system, characterized in that, It includes the vest as described in any one of claims 5 to 10, and the monitoring device as described in claim 11.

13. An electronic device, characterized in that, include: processor; as well as Memory for storing the executable instructions of the processor; The processor is configured to execute the method of any one of claims 1 to 4 by executing the executable instructions.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method described in any one of claims 1 to 4.

Images