Hyperactivity behavior monitoring method and device, system, vest, storage medium and equipment

Abstract

This disclosure provides a method, device, system, vest, storage medium, and electronic device for monitoring multi-movement behavior, relating to the field of intelligent monitoring technology. The method includes: receiving multi-channel time-series behavioral data of a user, the multi-channel time-series behavioral data including user behavior data collected by sensors placed on multiple parts of the user's body; inputting the multi-channel time-series behavioral data into a deep neural network; and intervening in the user based on the recognition results of the multi-movement behavior patterns output by the deep neural network. Collecting multi-channel time-series behavioral data of the user by sensors placed on multiple parts of the user's body 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 judgment based on simple threshold comparisons, can obtain more reliable and accurate recognition results.

Description

Technical Field

[0001] This disclosure relates to the field of intelligent monitoring technology, and in particular to a method, device, system, vest, storage medium and electronic device for monitoring user multi-movement behavior. Background Technology

[0002] Currently, technologies used to improve classroom behavior in children with ADHD (Attention-Deficit / Hyperactivity Disorder) primarily include medication, psychosocial interventions, self-monitoring techniques, and digital interventions. Medication is often effective in controlling ADHD symptoms, but can have side effects and is expensive. Psychosocial interventions typically focus on reducing disruptive and confrontational behaviors, which often fail shortly after treatment ends, and are difficult to manage in real-time in the classroom. Self-monitoring techniques help individuals improve self-regulation by allowing them to observe and record their own behavior. This technology requires sufficient self-discipline and can impact learning efficiency in the classroom environment. Early digital interventions with fixed vibration schedules can lead to habituation, and students may ignore the vibrations. Some existing technologies use personalized text message reminders. However, these are primarily used to assist executive functions such as planning, organization, and time management and are not well-suited to the classroom environment. Furthermore, reminders requiring reading can easily distract attention.

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

[0004] The purpose of this disclosure is to provide a method, device, system, vest, storage medium, and electronic device for monitoring multiple behaviors, which at least to some extent overcomes 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 intervening in the user based on the recognition result of the ADHD behavior patterns.

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

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

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

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

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

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

[0013] In one embodiment, the vest further includes: a first sensor detachably located in a first sensor housing; a second sensor detachably located in a second sensor housing; 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 wire to electrically connect the first sensor, the second sensor, and the vibrator to the communication module via the connection wire.

[0017] According to another aspect of this disclosure, a user multi-movement behavior monitoring device is provided, comprising: a behavior data receiving unit for receiving 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 for 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; and a user control unit for controlling the user based on the recognition result of the multi-movement behavior pattern.

[0018] According to another aspect of this 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 another aspect of this disclosure, an electronic device is provided, comprising: 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 another aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method described above.

[0021] The multi-activity monitoring methods, devices, systems, vests, storage media, and electronic devices provided in the embodiments of this disclosure collect multi-channel time-series behavioral data of users by placing sensors on multiple parts of the user's body. This data is more stable and the identification results are more accurate than data collected from a single body part. Furthermore, by processing and identifying multi-channel time-series behavioral data through deep neural networks, more reliable and accurate identification results can be obtained 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 are not intended to limit this disclosure. Attached Figure Description

[0023] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0024] Figure 1 A schematic diagram of a smart vest according to one embodiment of the present disclosure is shown;

[0025] Figure 2This diagram illustrates a flowchart of a multi-action monitoring method according to one embodiment of the present disclosure;

[0026] Figure 3 This diagram illustrates the network structure of a CNN in one embodiment of the present disclosure.

[0027] Figure 4 This diagram illustrates the structure of a multi-behavior monitoring system according to one embodiment of the present disclosure;

[0028] Figure 5 A flowchart of a multi-activity monitoring method in another embodiment of this disclosure is shown;

[0029] Figure 6 A structural diagram of the hardware of a smart vest in one embodiment of this disclosure is shown;

[0030] Figure 7 This diagram illustrates the structure of a multi-activity monitoring device according to one embodiment of the present disclosure; and

[0031] Figure 8 A structural block diagram of a computer device according to one embodiment of the present disclosure is shown. Detailed Implementation

[0032] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0033] Furthermore, the accompanying drawings are merely illustrative of this 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, 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 offers a smart vest that, combined with a deep neural network, can identify and intervene in users' hyperactive behaviors in real time.

[0035] To facilitate understanding, the following is an explanation of several terms used in this application.

[0036] Artificial intelligence (AI) is the theory, methods, technology, and application systems that use 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 achieve optimal results. In other words, AI is a comprehensive technology within 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 possess the functions of perception, reasoning, and decision-making.

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

[0038] Machine learning (ML) is a multidisciplinary field involving probability theory, statistics, approximation theory, convex analysis, and algorithm complexity theory. 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 techniques such as artificial neural networks, belief networks, reinforcement learning, transfer learning, inductive learning, and instructional learning.

[0039] The solutions provided in this application involve technologies such as multi-movement behavior monitoring in artificial intelligence, which are specifically illustrated through the following embodiments:

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

[0041] Figure 1 A schematic diagram of a smart vest according to one embodiment of this disclosure is shown. Figure 1 As shown, the smart vest 100 includes: a vest body 11; a first sensor receiving part 12 located at the shoulder portion of the vest body for receiving and fixing the first sensor; and a second sensor receiving part 13 located at the back portion of the vest body for receiving and fixing the second sensor.

[0042] In one embodiment, the smart vest also includes a vibrator receiving portion 14 located at a predetermined body part of the vest body for accommodating a fixed vibrator. For example, it may be located in a user-sensitive area 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 for sending 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 for receiving control commands from the control unit and sending them to the vibrator 17. In one embodiment, the first and second sensors include accelerometers. In another embodiment, the first and second sensors include an accelerometer and a gyroscope. In yet another 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 capability 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, the sensors integrated into the vest are less likely to be removed or adjusted by children, thus ensuring more consistent use.

[0047] The vest's hardware components, including sensors and other parts, 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 receiving a connection wire to electrically connect a first sensor, a second sensor, and a 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 the control unit. For example, they can communicate via Bluetooth or Wi-Fi.

[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] Figure 2 A flowchart of a multi-activity monitoring method according to an embodiment of this disclosure is shown. The method provided in this embodiment can be executed by any electronic device with computing power.

[0052] like Figure 2 As shown, in step S202, multi-channel time-series behavioral data of the user is received. This multi-channel time-series behavioral data 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 shoulders and back. In one embodiment, the sensors include an accelerometer for monitoring the user's motion and frequency. In one embodiment, the sensors include a gyroscope for measuring the user's angular velocity. In one embodiment, the sensors include a 6-axis inertial measurement unit (IMU) that includes both an accelerometer and a gyroscope, simultaneously measuring the user's motion and posture.

[0053] S204, input multi-channel time-series behavioral data into a deep neural network, and receive the recognition results of multi-movement behavioral patterns 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, based on the identification results of this hyperactive behavior pattern, intervene in the user.

[0055] In one embodiment, when the identification result indicates that the user is exhibiting ADHD behavior, a vibrator is used to alert the user.

[0056] In the above embodiments, multi-channel time-series behavioral data of users are collected by sensors placed on multiple parts of the user's body. This data is more stable and the recognition results are more accurate than data collected from a single body part. Furthermore, processing and recognizing multi-channel time-series behavioral data through deep neural networks can yield more reliable and accurate recognition results compared to simple threshold comparisons.

[0057] Furthermore, data is collected by sensors placed on the shoulders and back, which are more robust than those placed on the wrists, waist, and other parts of the body, 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 disclosed herein provides a non-pharmacological intervention for children with ADHD, avoiding potential drug-related side effects.

[0060] Figure 3 A network structure diagram of a CNN is shown in one embodiment of this disclosure. Figure 3 As shown, 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 1D 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 hyperactive behavior.

[0061] Figure 4 This diagram illustrates the structure of a ADHD behavior monitoring system according to one embodiment of the present disclosure. Figure 4 As shown, the monitoring system includes a smart vest 41 and a control unit 42. The smart vest 41 can employ... Figure 1 The vest shown is a smart vest. The smart vest 41 transmits multi-channel time-series behavioral data of the user, collected by a first sensor and a second sensor, to a control unit 42 via wired or wireless means; it also receives control commands from the control unit 42 and sends them to a vibrator. The control unit 42 is used to receive the user's multi-channel time-series behavioral data, which includes user behavior data collected by sensors placed on multiple parts of the user's body; input the multi-channel time-series behavioral data into a deep neural network; receive the recognition results of the multi-movement behavior patterns output by the deep neural network; and send 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 result 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 database 44 for storage, and the parent terminal 45 can view the relevant data through the cloud; the doctor 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 classroom performance by intervening in the hyperactivity of children with ADHD in the classroom environment in real time, while assisting teachers in classroom management. Furthermore, parents can remotely obtain real-time information about their child's classroom status through the system. Additionally, parents can use a smart vest at home to help reduce hyperactivity while doing homework.

[0065] Furthermore, in one embodiment, the smart vest can be applied to differentiate between children with ADHD and those without ADHD by analyzing behavioral patterns, which can aid 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 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 tailoring interventions to individual needs. Beyond the classroom, the vest can be used in various settings, such as special education schools, counseling centers, and the home. It can also be adapted for behavioral monitoring and intervention in other conditions, such as autism and anxiety disorders, providing a broader range of applications for this disclosure.

[0067] Figure 5 A flowchart of a multi-activity monitoring method in another embodiment of this disclosure is shown.

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

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

[0070] S506, the control unit inputs multi-channel time-series behavioral data into the CNN network and receives the recognition results of multi-movement behavioral 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 a vibration command from the control unit, vibrates, intervenes, 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 status.

[0074] In addition, classroom performance can be scored based on children's classroom behavior data and the effects of intervention, thus realizing a classroom performance scoring mechanism.

[0075] The above embodiments provide a smart vest for improving classroom behavior in school-aged children with ADHD. It uses wearable sensing and deep learning to detect hyperactive behaviors in ADHD children in the classroom and provides vibration intervention to improve their performance, thus achieving automated classroom behavior intervention for ADHD children. 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 deep learning model performs real-time analysis, identification, and feedback on the collected behavior data, identifying hyperactive behaviors in the classroom in real time; through the vibration intervention mechanism, when hyperactive behavior is detected, effective vibration intervention is immediately provided to remind children 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 children's acceptance.

[0076] Figure 6 This diagram illustrates the hardware structure of a smart vest according to one embodiment of the present disclosure. Figure 6 As shown, 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 A schematic diagram of the structure of a multi-activity monitoring device according to one embodiment of this disclosure is shown. Figure 7 As shown, 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 embodiments of this disclosure, the smart vest includes two 6-axis inertial measurement units (IMUs). The 6-axis IMUs measure accelerometer and gyroscope data, enabling comprehensive and accurate tracking of complex movements and postures. The sensors are strategically positioned on the shoulders and back. The vest uses elastic bands, for example, to hold the sensors in place, ensuring they remain in the correct position. This consistent placement is important for accurate monitoring and intervention, ensuring reliable data collection throughout use. Embodiments of this disclosure employ deep learning algorithms, particularly 1D convolutional neural networks (CNNs), to analyze time-series behavioral data collected from the sensors, extract meaningful features, and identify patterns indicating hyperactive behavior. The use of 1D CNNs 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 be limiting. 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 products. Therefore, various aspects of the present invention can be specifically implemented in the following forms: entirely hardware implementations, entirely software implementations (including firmware, microcode, etc.), or implementations combining hardware and software aspects, collectively referred to herein as “circuits,” “modules,” or “systems.”

[0081] The following reference Figure 8 To describe an electronic device 800 according to this embodiment of the present invention. Figure 8 The electronic device 800 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0082] like Figure 8 As shown, 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, at least one storage unit 820, and a bus 830 connecting different system components (including storage unit 820 and processing unit 810).

[0083] The storage unit stores program code that can be executed by the processing unit 810, causing the processing unit 810 to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 810 can perform actions such as... Figure 2S202, as shown, receives 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, inputs the multi-channel time-series behavioral data into a deep neural network, and receives the recognition result of the multi-movement behavioral pattern output by the deep neural network; S206, controls the user based on the recognition result of the multi-movement behavioral pattern.

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

[0085] The storage unit 820 may also include a program / utility 8204 having a set (at least one) of program modules 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] Bus 830 can represent 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] Electronic device 800 can also communicate with one or more external devices 700 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with the electronic device 800, and / or with any device that enables the electronic device 800 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 850. Electronic device 800 can be connected to a monitor / screen via graphics card 870 to display multi-channel time-series behavioral data and the recognition results in a visualization window. Furthermore, electronic device 800 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 860. As shown, network adapter 860 communicates with other modules of electronic device 800 via bus 830. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with 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.

[0088] From 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, external 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 methods according to the embodiments of this disclosure.

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

[0090] A program product for implementing the above-described method according to embodiments of the present invention is described. This product may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may 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, the readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0091] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) 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 devices, magnetic storage devices, or any suitable combination thereof.

[0092] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various 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, capable of sending, propagating, or transmitting programs for use by or in conjunction 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 this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute 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 cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type 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 the 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 in children with ADHD in the classroom, and these behaviors are effectively intervened through a vibration intervention mechanism. This design combines sensing technology with an intervention mechanism, 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, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[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 a step may be broken down into multiple steps.

[0098] From 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, external 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 methods according to the embodiments of this disclosure.

[0099] Other embodiments of this disclosure will readily occur 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 examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the appended claims.

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.

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