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CN-121987042-A - Electric sofa self-adaptive adjusting system and method based on user gesture recognition

CN121987042ACN 121987042 ACN121987042 ACN 121987042ACN-121987042-A

Abstract

The invention relates to the technical field of electric sofa self-adaptive adjusting systems and discloses an electric sofa self-adaptive adjusting system and method based on user gesture recognition, wherein the self-adaptive adjusting system comprises a gesture sensing module ‌, a control center module, an electric adjusting mechanism module, a learning and memory module, a health detection and fatigue early warning module, a multi-mode interaction control module and an environment sensing and scene linkage module, health monitoring and early warning can be carried out on a sedentary person through the system, and the using effect in the aspects of health management, environment adaptation and user experience is improved.

Inventors

  • LIU QINBO
  • LIU TAO
  • YIN ZHIYONG

Assignees

  • 南通黛奥比智能科技有限公司

Dates

Publication Date
20260508
Application Date
20260225

Claims (10)

  1. 1. The self-adaptive adjustment system for the electric sofa based on the user gesture recognition is characterized by comprising a gesture sensing module ‌, a control center module, an electric adjustment mechanism module, a learning and memory module, a health detection and fatigue early warning module, a multi-mode interaction control module and an environment sensing and scene linkage module; The gesture sensing module comprises an infrared camera, a pressure sensor array, an integrated heart rate blood pressure sensor and a millimeter wave radar, and is used for capturing three-dimensional gesture information such as sitting gesture, lying gesture and the like of a user in real time and identifying key skeleton nodes; The control center module is responsible for processing data from the gesture sensing module, integrating the output of the modules such as image acquisition, gesture recognition, three-dimensional reconstruction, skeleton registration and the like, generating a control instruction to execute a control algorithm and driving the electric adjustment mechanism module; The electric adjusting mechanism module is used for executing instructions of a control center, driving motors of the sofa backrest, the seat cushion and the leg support components, and realizing angle, height and stretching degree adjustment; The learning and memory module is used for storing personalized gesture preferences of users, memorizing and adjusting the common lying angle and the extension length, and optimizing and adjusting strategies through historical data; The health detection and fatigue early warning module is used for analyzing the posture change of a user, analyzing the curvature of the spine and the micro motion of limbs, and early warning sedentary fatigue or health risk by combining the data such as heart rate and respiratory frequency, ‌ The multi-mode interaction control module supports various interaction modes such as voice, gesture, mobile phone APP and the like, allows a user to manually fine tune or switch scene modes, ‌ The environment sensing and scene linkage module comprises a temperature and humidity sensor, a light sensor and a gesture sensor, and the environment state is identified through the temperature and humidity sensor and the light sensor, so that heating and ventilation parameters related to the sofa are automatically adjusted, and intelligent household equipment linkage is conducted.
  2. 2. The self-adaptive adjustment system of the electric sofa based on user gesture recognition of claim 1, wherein the infrared camera in the gesture sensing module adopts 940nm invisible light waves, so that interference to human eyes is avoided, and meanwhile, the self-adaptive adjustment system is provided with a 120-degree wide-angle lens to cover the full viewing angle of sitting postures of users.
  3. 3. The self-adaptive adjustment system for the electric sofa based on user gesture recognition of claim 1, wherein the pressure sensor array is in 256-point distributed layout, the pressure resolution of each point is 0.1N, the pressure distribution of buttocks and legs of a user can be accurately recognized, and the micro detection of millimeter wave radar is combined, so that the non-contact gesture recognition is realized.
  4. 4. The electric sofa self-adaptive adjusting system based on user gesture recognition of claim 1, wherein the control center module is internally provided with a multithread processing unit, the gesture recognition algorithm adopts a lightweight convolutional neural network, real-time performance is realized on embedded equipment, and delay is lower than 50ms.
  5. 5. The system of claim 1, wherein the learning and memorizing module is configured to automatically store the posture parameters as a preference mode and optimize the adjustment strategy to reduce mechanical wear when detecting that the user is in the same lying posture 3 consecutive times.
  6. 6. The self-adaptive adjustment system of an electric sofa based on user gesture recognition of claim 1, wherein the health detection and fatigue early warning module is used for adjusting the sofa angle to a 15-degree sitting posture mode by analyzing dynamic changes of the curvature of the spine and triggering vibration reminding and connecting with the electric adjustment mechanism module when the same gesture is kept for 30 minutes continuously.
  7. 7. The self-adaptive adjustment system for the electric sofa based on user gesture recognition of claim 1, wherein the temperature and humidity sensor in the environment sensing and scene linkage module adopts an MEMS technology, the power consumption is lower than 0.5mW, the temperature and humidity of the surface of the sofa can be monitored in real time, and when the temperature is detected to be more than 28 ℃, a ventilation mode is automatically started.
  8. 8. The self-adaptive adjustment system of the electric sofa based on user gesture recognition of claim 1, wherein the electric adjustment mechanism module is driven by a brushless motor and is provided with an absolute value encoder, the angle control precision reaches +/-0.1 degrees, stepless adjustment of 0-180 degrees is supported, and the noise is lower than 40dB.
  9. 9. The system of claim 1, wherein the temperature and humidity sensor ‌ is capable of monitoring the ambient temperature and humidity in real time, providing data support ‌ for heating and ventilation adjustment, the light sensor ‌ is capable of detecting the ambient illumination intensity and is used for linking some curtains or light adjustment in the intelligent home, the gesture sensor is MPU-6050, and the user sitting position angle is calculated through accelerometer and gyroscope data, and the supporting force is dynamically adjusted.
  10. 10. An electric sofa self-adaptive adjusting method based on user gesture recognition, characterized in that the device as claimed in any one of claims 1 to 9 is applied: s1, the system captures three-dimensional gesture information, pressure distribution and inching data ‌ ‌ of a user in real time through an infrared camera, a pressure sensor array, a millimeter wave radar and the like in a ‌ gesture sensing module ‌; the ‌ gesture sensing module feeds back the monitored data to the control center module, and the control center module ‌ processes the acquired data, including image analysis, gesture recognition and three-dimensional reconstruction, and generates a control instruction ‌; s3, an instruction generated by the control center module drives the electric adjusting mechanism module ‌ to drive a motor according to the instruction, and the angles, the heights and the stretching degrees ‌ of the sofa backrest, the cushion and the leg rest are adjusted; s4, a learning and memorizing module ‌ stores the user preference gesture, and optimizes the adjustment strategy through an incremental learning mechanism; S5, a health detection and fatigue early warning module ‌ analyzes the curvature and sign data of the spine, vibration reminding is triggered and adjusted to a 15-degree sitting posture mode after sitting for 30 minutes, an environment sensing and scene linkage module ‌ automatically adjusts ventilation through a temperature and humidity sensor, ventilation is started when the temperature is more than 28 ℃, and the environment illumination intensity can be detected and used for linking some curtains or light adjustment in a smart home; S6, the intelligent sofa user can manually fine tune or switch the scene mode ‌ through the multi-mode interaction control module by utilizing voice, gestures or the mobile phone APP.

Description

Electric sofa self-adaptive adjusting system and method based on user gesture recognition Technical Field The invention belongs to the technical field of electric sofa self-adaptive adjusting systems, and particularly relates to an electric sofa self-adaptive adjusting system and method based on user gesture recognition. Background The traditional electric sofa adjusting system depends on a single sensor, such as a pressure switch, an angle sensor or a preset fixed mode, and cannot adapt to the dynamic gesture change of a user. In the prior art, although partial products are provided with a simple pressure detection function, only two basic states of sitting and lying can be identified, key health indexes such as spinal curvature, limb micro-movement and the like can not be captured, and the fatigue early warning function of sedentary is lost. In addition, the traditional system adjustment mode is mainly based on manual buttons, multi-mode interaction support is lacked, users need to operate frequently to achieve ideal gestures, and experience is poor. In the aspect of health management, the existing electric sofa is not generally integrated with a physiological parameter monitoring module, the fatigue degree of a user cannot be estimated through data such as heart rate, respiratory rate and the like, and the linkage adjusting function cannot be actively interfered. In terms of environmental adaptability, most products only have basic temperature control functions, lack linkage capability with an intelligent household system, and cannot automatically adjust curtains or lamplight according to illumination intensity. In the technical aspect, the traditional gesture recognition adopts a contact sensor, so that the contact sensor is easy to generate a pressing sense and is easy to wear after long-term use. The control algorithm is mostly based on a rule engine, and cannot optimize and adjust strategies through user behavior data, so that mechanical parts are frequently started and stopped, and the service life of equipment is shortened. Along with the increase of intelligent home and health monitoring demands, an adaptive adjusting system integrating gesture sensing, health early warning and environment linkage is needed in the electric sofa market. Therefore, we propose an electric sofa self-adaptive adjusting system and method based on user gesture recognition. Disclosure of Invention The application aims to improve the use effects of health management, environmental adaptation and user experience for health monitoring and early warning of sedentary people, and provides an electric sofa self-adaptive adjusting system and method based on user gesture recognition. The technical scheme adopted by the invention is as follows: An electric sofa self-adaptive adjusting system based on user gesture recognition comprises a gesture sensing module ‌, a control center module, an electric adjusting mechanism module, a learning and memory module, a health detection and fatigue early warning module, a multi-mode interaction control module and an environment sensing and scene linkage module; The gesture sensing module comprises an infrared camera, a pressure sensor array, an integrated heart rate blood pressure sensor and a millimeter wave radar, and is used for capturing three-dimensional gesture information such as sitting gesture, lying gesture and the like of a user in real time and identifying key skeleton nodes; The control center module is responsible for processing data from the gesture sensing module, integrating the output of the modules such as image acquisition, gesture recognition, three-dimensional reconstruction, skeleton registration and the like, generating a control instruction to execute a control algorithm and driving the electric adjustment mechanism module; The electric adjusting mechanism module is used for executing instructions of a control center, driving motors of the sofa backrest, the seat cushion and the leg support components, and realizing angle, height and stretching degree adjustment; The learning and memory module is used for storing personalized gesture preferences of users, memorizing and adjusting the common lying angle and the extension length, and optimizing and adjusting strategies through historical data; The health detection and fatigue early warning module is used for analyzing the posture change of a user, analyzing the curvature of the spine and the micro motion of limbs, and early warning sedentary fatigue or health risk by combining the data such as heart rate and respiratory frequency, ‌ The multi-mode interaction control module supports various interaction modes such as voice, gesture, mobile phone APP and the like, allows a user to manually fine tune or switch scene modes, ‌ The environment sensing and scene linkage module comprises a temperature and humidity sensor, a light sensor and a gesture sensor, and the environment state is identified through the temperature and humi