Human body area detection method, intelligent mattress and storage medium

Abstract

The embodiment of the invention discloses a human body area detection method, an intelligent mattress and a storage medium, the human body area detection method is applied to the intelligent mattress, the intelligent mattress is provided with a photoelectric sensor array, and the method comprises the following steps: collecting a plurality of photoelectric signals through the photoelectric sensor array; target photoelectric signals in the multiple photoelectric signals are determined, the target photoelectric signals are signals corresponding to target human body parts of the user, and the target human body parts comprise at least two human body parts of the neck, the hands and the legs; according to the target position of the target photoelectric sensor in the intelligent mattress, the human body area of the user in the intelligent mattress is determined, and the target photoelectric sensor is a photoelectric sensor for collecting the target photoelectric signal in the photoelectric sensor array. The positions of the at least two human body parts of the user on the intelligent mattress can be identified based on the acquired photoelectric signals, so that the human body area of the user on the intelligent mattress is positioned, and the accuracy of human body area detection is improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of smart devices, and are related to, but not limited to, a human body region detection method, a smart mattress, and a storage medium. Background Art

[0002] In the field of smart devices, identifying the relative position of the user and the device is crucial for enhancing intelligent control and personalized interaction. For example, a smart mattress, by identifying the user's body position on the mattress, can precisely adjust mattress functions, such as massage intensity and support strength, providing a personalized and comfortable user experience.

[0003] Related technologies can identify a user's location through image recognition or capacitive sensors. However, image recognition carries the risk of privacy leakage and is susceptible to light, while capacitive sensors have low recognition accuracy and cannot meet the precise control requirements of smart mattresses, affecting the user experience. Summary of the Invention

[0004] In view of this, the human body region detection method, smart mattress, and storage medium provided in the embodiments of the present application can identify the positions of at least two body parts of the user on the smart mattress based on the acquired photoelectric signals, thereby locating the user's body region on the smart mattress and improving the accuracy of human body region detection. The human body region detection method, smart mattress, and storage medium provided in the embodiments of the present application are implemented as follows:

[0005] A first aspect of an embodiment of the present application discloses a human body region detection method, which is applied to a smart mattress, wherein the smart mattress is provided with a photoelectric sensor array. The method includes:

[0006] Collecting multiple photoelectric signals through the photoelectric sensor array;

[0007] Determining a target photoelectric signal from the multiple photoelectric signals, wherein the target photoelectric signal is a signal corresponding to a target body part of the user, and the target body part includes at least two body parts of the neck, hands, and legs;

[0008] The human body area of the user on the smart mattress is determined according to the target position of the target photoelectric sensor on the smart mattress, wherein the target photoelectric sensor is a photoelectric sensor in the photoelectric sensor array that collects the target photoelectric signal.

[0009] A second aspect of an embodiment of the present application discloses a smart mattress, wherein the smart mattress is provided with a photoelectric sensor array, including:

[0010] an acquisition unit, configured to acquire a plurality of photoelectric signals through the photoelectric sensor array;

[0011] a processing unit, configured to determine a target photoelectric signal from the plurality of photoelectric signals, wherein the target photoelectric signal is a signal corresponding to a target body part of the user, the target body part comprising at least two body parts of the user's neck, hands, and legs;

[0012] The processing unit is further configured to determine the body area of the user on the smart mattress according to the target position of the target photoelectric sensor on the smart mattress, wherein the target photoelectric sensor is a photoelectric sensor in the photoelectric sensor array that collects the target photoelectric signal.

[0013] A third aspect of the embodiments of the present application discloses a smart mattress, comprising:

[0014] a memory storing executable program code;

[0015] a processor coupled to the memory;

[0016] The processor calls the executable program code stored in the memory to execute all or part of the steps in any one of the human body area detection methods disclosed in the first aspect of the embodiment of the present application.

[0017] The fourth aspect of the embodiments of the present application discloses a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute all or part of the steps in any one of the human body area detection methods disclosed in the first aspect of the embodiments of the present application.

[0018] Compared with related technologies, the human body region detection method, smart mattress, and storage medium provided by the embodiments of the present application have at least the following beneficial effects:

[0019] First, the smart mattress collects multiple photoelectric signals through a photoelectric sensor array, minimizing the risk of privacy leaks. Next, a target photoelectric signal is identified from the multiple photoelectric signals. These target photoelectric signals correspond to target body parts of the user, such as at least two of the neck, hands, and legs, enabling localization of a portion of the user's body. Finally, based on the target position of the target photoelectric sensor on the smart mattress, i.e., the position of the user's target body part on the smart mattress, the user's body region on the smart mattress is determined. This improves the accuracy of body region detection and provides a reliable basis for adjusting the smart mattress's functions based on body regions. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings herein are incorporated into and constitute a part of the specification. These drawings illustrate embodiments consistent with the present application and, together with the specification, are used to illustrate the technical solutions of the present application.

[0021] Figure 1 A schematic diagram of an application scenario of the human body region detection method provided in an embodiment of the present application;

[0022] Figure 2 A schematic diagram of a photoelectric sensor array provided for a smart mattress according to an embodiment of the present application;

[0023] Figure 3 A schematic diagram of a flow chart of a human body region detection method provided in an embodiment of the present application;

[0024] Figure 4 A schematic diagram of a target photoelectric sensor for collecting target photoelectric signals in a smart mattress provided in an embodiment of the present application;

[0025] Figure 5 A schematic diagram of a process for determining a target photoelectric signal in a human body region detection method provided in an embodiment of the present application;

[0026] Figure 6 A schematic diagram of a process for detecting the human body area of a user on a smart mattress in a human body area detection method provided in an embodiment of the present application;

[0027] Figure 7 A schematic diagram of a process for adjusting the functions of a smart mattress in the human body region detection method provided in an embodiment of the present application;

[0028] Figure 8 A schematic diagram of a smart mattress provided in an embodiment of the present application having multiple functional partitions;

[0029] Figure 9 A schematic structural diagram of a smart mattress provided in an embodiment of the present application;

[0030] Figure 10 Another structural schematic diagram of the smart mattress provided in an embodiment of the present application. DETAILED DESCRIPTION

[0031] To make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the specific technical solutions of the present application will be further described in detail below in conjunction with the drawings in the embodiments of the present application. The following embodiments are used to illustrate the present application but are not intended to limit the scope of the present application.

[0032] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0033] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0034] It should be pointed out that the terms "first\second\third" involved in the embodiments of the present application are used to distinguish similar or different objects, and do not represent a specific ordering of the objects. It can be understood that "first\second\third" can be interchanged with a specific order or sequence where permitted, so that the embodiments of the present application described here can be implemented in an order other than that illustrated or described here.

[0035] In related technologies, determining the user's relative position on a smart mattress can provide a basis for regulating the mattress's functions. For example, in scenarios such as child care or patient care, cameras and other devices can be installed to monitor the user's position on the mattress, providing functions such as fall warnings or activity monitoring. However, in some scenarios, installing cameras may raise concerns about privacy leaks. While using capacitive sensors for position recognition can alleviate privacy issues to a certain extent, they are susceptible to changes in ambient humidity and temperature, resulting in reduced recognition accuracy. Furthermore, capacitive sensors can generally only detect whether a person is in contact with the human body surface, but cannot accurately identify the user's contact position, making it difficult to meet the requirements for precise regulation of smart mattresses.

[0036] In view of this, an embodiment of the present application provides a human body area detection method, a smart mattress, and a storage medium. The method can identify the positions of at least two parts of the user's body on the smart mattress based on the acquired photoelectric signals, thereby locating the user's body area on the smart mattress and improving the accuracy of human body area detection.

[0037] The following introduces an application scenario of the human body area detection method provided in an embodiment of the present application.

[0038] See also Figure 1 , Figure 1 A schematic diagram of an application scenario of the human body region detection method provided in an embodiment of the present application is shown as follows: Figure 1 As shown, the application scenario includes a smart mattress 10 and a user 20. When the user 20 uses the smart mattress 10, the smart mattress 10 can monitor and identify the human body area 30 of the user 20 on the smart mattress 10 through the photoelectric sensor array provided therein, so as to adjust the relevant functions of the smart mattress 10 based on the human body area 30 to provide the user with a personalized and comfortable usage experience.

[0039] In the embodiment of the present application, the photoelectric sensor array provided in the smart mattress 10 includes a plurality of photoelectric sensors 11. For example, Figure 1As shown, multiple photoelectric sensors 11 can be evenly arranged on the smart mattress 10 according to preset row and column spacing to form a regular matrix layout to ensure full coverage and uniform monitoring of the surface of the smart mattress 10.

[0040] In some possible embodiments, the photosensor array includes multiple zones, each corresponding to a different part of the user's body. The density of photosensors 11 in each zone varies. The zone corresponding to the target body part has a higher density of photosensors 11 than other zones. That is, the zone corresponding to the target body part has a higher number of sensors per unit area than other zones, ensuring more detailed monitoring and data collection of the target body part. The target body part may include at least two of the following: the neck, hands, and legs. The multiple zones may be arranged along the longer or shorter sides of the smart mattress 10, without limitation.

[0041] It should be noted that the multiple partitions included in the photoelectric sensor array are pre-divided and determined during the design. These partitions can be divided based on the common sleeping postures of different groups of people and ergonomic principles to ensure high-precision monitoring of key body parts, improve the stability and reliability of human body area detection, and will not change due to changes in the position of body parts of the user 20 who actually uses the smart mattress 10 during use (such as turning over, changing position, etc.).

[0042] For example, Figure 2 A schematic diagram of a photoelectric sensor array provided for a smart mattress provided in an embodiment of the present application. In one embodiment, the target body parts may include hands and legs, and the photoelectric sensor array includes five partitions, namely partition 12, partition 13, partition 14, partition 15, and partition 16. Partition 12 corresponds to the head area, partition 13 corresponds to the neck area, partition 14 corresponds to the back area, partition 15 corresponds to the hand area, and partition 16 corresponds to the leg area. Figure 2 As shown, the density of photosensors in the subareas of the photoelectric sensor array corresponding to the target body parts, namely, subarea 15 corresponding to the hands and subarea 16 corresponding to the legs, is higher than in other subareas. This improves the monitoring accuracy of the target body parts and the accuracy of data collection, thereby more accurately identifying the user's body area. Simultaneously, a high density of photosensors is avoided in all subareas, thereby reducing production costs and process complexity. Furthermore, a small number of sensors are still placed in other areas, allowing the target body part of user 20 to be identified even when the user 20 is in an unconventional posture, such as reclining or lying on their side, thereby improving the robustness of detection.

[0043] Among them, such as Figure 2The illustrated partitioning of the photosensor array is for illustrative purposes only and does not limit this application. In actual applications, fewer or more partitions can be configured based on factors such as the size of the smart mattress 10 and the target body area. Parameters such as sensor density and partition area in different areas can also be rationally optimized based on ergonomic research and multi-population experimental data. In this way, the photosensor array design of the smart mattress 10 can be better adapted to different user groups and usage scenarios, meeting diverse monitoring needs and enhancing the user experience.

[0044] In the embodiment of the present application, after a plurality of photoelectric signals are acquired by the photoelectric sensor array provided in the smart mattress 10 , the human body area 30 of the user 20 on the smart mattress 10 can be obtained based on the target photoelectric signal determined based on the plurality of photoelectric signals.

[0045] It should be noted that the human body region 30 may be Figure 1 The rectangular area shown is for the convenience of rapid detection, and can also be an irregularly shaped area including the outline of the human head and limbs for more detailed monitoring and functional adjustment, which is not limited here.

[0046] It is understandable that if Figure 1 The illustrated smart mattress 10 is rectangular in shape, which is merely an example and does not limit the smart mattress 10 in the embodiments of this application. The smart mattress 10 can also be designed in other shapes, such as circular, elliptical, or polygonal, to meet different usage requirements and spatial layouts. Similarly, the individual photosensors in the photosensor array can also include various types, such as photodiodes and phototransistors, depending on the actual application scenario and performance requirements, and are not limited here.

[0047] To facilitate understanding of how to improve the accuracy of human body region detection through the human body region detection method provided in this application, an implementation method of the human body region detection method provided in this application is introduced below.

[0048] See also Figure 3 , Figure 3 A flow chart of a human body region detection method provided in an embodiment of the present application is shown as follows: Figure 3 As shown, the method may include the following steps:

[0049] S301, collecting multiple photoelectric signals through a photoelectric sensor array.

[0050] In the embodiment of the present application, the photosensor array may include a plurality of photosensors.

[0051] In some possible embodiments, all photosensors in the photosensor array can collect photoelectric signals corresponding to each photoelectric sensor. That is, the number of photoelectric signals collected by the photosensor array is equal to the number of photosensors in the photosensor array. This allows the multiple photoelectric signals to fully cover the detection area of the smart mattress, ensuring that all parts of the user's body on the mattress are recognized, thereby improving the accuracy and reliability of body area detection.

[0052] In some possible embodiments, photoelectric signals can be collected by some of the photoelectric sensors in the photoelectric sensor array, that is, the number of multiple photoelectric signals collected can be smaller than the number of photoelectric sensors. In this way, the power consumption of the photoelectric sensor array can be reduced, and the mutual interference between the light sources of too many photoelectric sensors and the impact on the user's rest can be avoided. In addition, reducing the number of enabled photoelectric sensors can also help reduce the complexity of signal processing and improve the system's response speed and data processing efficiency.

[0053] In some possible embodiments, the photoelectric sensor array includes multiple partitions, where different partitions correspond to different body parts of the user. The density of photosensors in the partition corresponding to the target body part is higher than that in other partitions. Multiple photoelectric signals are collected by the photoelectric sensor array, including:

[0054] Multiple photoelectric signals are collected by photoelectric sensors in corresponding partitions of the target body part.

[0055] It should be noted that by enabling the photoelectric sensors in the partitions corresponding to the target body parts to collect photoelectric signals, the accuracy of detecting the user's target body parts can be improved without turning on all sensors in the photoelectric sensor array, so as to more accurately identify and locate the user's body area, thereby providing data support for the personalized function adjustment of the smart mattress.

[0056] Understandably, if a user rests in an unconventional position on the smart mattress, such as reclining or rolling over, the photoelectric sensors in the corresponding zones may not be able to detect the user's target body area. Therefore, the corresponding zone sensors can still be activated to identify the user's key body parts. This design ensures flexible and robust detection, adapting to various usage scenarios and improving the user experience.

[0057] In some possible embodiments, after collecting multiple photoelectric signals by the photoelectric sensors corresponding to the zones of the target body part, the human body region detection method provided by the present application further includes:

[0058] Determining the number of target photoelectric signals among the multiple photoelectric signals collected by the photoelectric sensor of the partition corresponding to the target body part, wherein the target photoelectric signal is a signal corresponding to the target body part of the user;

[0059] When the number of target photoelectric signals is less than or equal to a preset signal number threshold, multiple photoelectric signals are collected by the photoelectric sensors of all partitions of the photoelectric sensor array.

[0060] It should be noted that the target photoelectric signal refers to the photoelectric signal corresponding to the user's target body part (such as the neck, hands and legs). Feature extraction, signal recognition and other methods can be used to determine which photoelectric signals from the multiple photoelectric signals collected by the photoelectric sensors in the partition corresponding to the target body part belong to the target photoelectric signal. If the number of target photoelectric signals is less than or equal to the preset signal number threshold, it can be understood that due to changes in the user's posture or other occlusion factors, the number of photoelectric signals currently collected in the partition corresponding to the target body part is small and insufficient to accurately locate the user's body part. By enabling the photoelectric sensors in all partitions of the photoelectric sensor array, comprehensive collection can be performed to obtain more comprehensive photoelectric signal data, thereby improving the accuracy and reliability of detection.

[0061] S302: Determine a target photoelectric signal among multiple photoelectric signals.

[0062] In the embodiment of the present application, the target photoelectric signal is a signal corresponding to the user's target body part, which includes at least two of the following: the neck, hands, and legs. The hands may include areas such as the palm and forearm, while the legs may include areas such as the thigh, calf, and foot. This helps increase the likelihood of identifying the target photoelectric signal corresponding to the target body part, ensuring that the smart mattress can more accurately identify and locate the user's body area.

[0063] Target body parts can be those with distinct features and easily detected by the photoelectric sensor. In smart mattress applications, the user's neck, hands, and legs are often less covered or not covered at all by clothing, making them more likely to come into direct contact with the mattress surface. This allows the photoelectric sensor to more effectively collect signals from these areas. Therefore, these body parts are preferred as target body parts to improve detection accuracy and reliability.

[0064] In some possible embodiments, other body parts, such as the back, shoulders, and waist, can be adjusted based on actual application scenarios and needs, and are not limited here. For example, if the application scenario requires higher detection accuracy or coverage of more body parts, these body parts can also be included in the range of target body parts, allowing the smart mattress to adapt to different user needs and usage scenarios, providing more comprehensive and accurate human body area detection.

[0065] In an embodiment of the present application, the photoelectric signal sensor may include a light source module and a detection module, wherein the light source module may include a light-emitting diode (LED) light source, an infrared light source, etc., for emitting a light signal of a specified frequency (such as green light, infrared light, etc.); the detection module may include a photoelectric sensor for receiving the reflected light signal obtained after the above-mentioned light signal is absorbed and reflected by an object, and converting it into a photoelectric signal that can be used for analysis and processing.

[0066] Among the multiple photoelectric signals collected by the photoelectric sensor array, there may be photoelectric signals reflected by the human body surface and photoelectric signals reflected by other objects (such as clothing, bedding, etc.). In order to ensure accurate identification of the target photoelectric signal, it is necessary to extract and analyze the features of the collected photoelectric signals. For example, the characteristic parameters such as intensity, frequency and phase of each photoelectric signal can be calculated and compared with the typical characteristics of human body signals. In addition, signal processing techniques such as filtering and correlation analysis can be used to remove background noise and interference signals and extract photoelectric signals that match the reflection characteristics of the human body.

[0067] In some possible embodiments, a target photoelectric signal among multiple photoelectric signals can be identified by analyzing the timing characteristics and spatial distribution characteristics of the photoelectric signals. For example, human physiological activities (such as heartbeat and breathing) can cause photoelectric signals to exhibit periodic variations. By analyzing the timing characteristics of the signals, photoelectric signals related to the human body can be identified. Furthermore, considering the position and posture of the human body on the mattress, such as the partitioning of the photoelectric sensor, the target photoelectric signals are often concentrated in specific partitions. Therefore, combining the spatial distribution characteristics can help more accurately locate the target photoelectric signal.

[0068] In some possible embodiments, photoelectric signals can be classified and identified using a pre-trained recognition model to distinguish between photoelectric signals collected by the human body and photoelectric signals collected by other objects, and to determine the body part corresponding to the photoelectric signals collected by each human body, thereby improving the accuracy and robustness of target photoelectric signal recognition.

[0069] It should be noted that the machine learning model can be a traditional machine learning model such as support vector machine (SVM) and random forest (RandomForest), or a deep learning model such as convolutional neural network (CNN) and long short-term memory network (LSTM), and is not limited here.

[0070] In some possible embodiments, the recognition model can be trained through the following steps: first, training data is obtained, which may include multiple photoelectric signals collected from different body parts of different users, and the obtained training data is labeled, recording the body part corresponding to each photoelectric signal, such as the neck, hand or leg, or marking it as a reflection source such as clothing or bedding. Then, the obtained training data is preprocessed, such as downsampling and noise reduction, to improve the quality of the data. Next, feature extraction is performed on the preprocessed training data to obtain features such as signal strength, frequency, phase or periodic changes. Finally, the initial model is trained based on the preprocessed training data. The initial model can be a deep learning model, such as CNN or LSTM. The model performance can be evaluated by setting a loss function, cross-validation, etc., and parameter optimization is performed based on the evaluation results.

[0071] In some possible embodiments, the target human body parts include hands and legs, and the physiological parameters include blood pressure. After obtaining the physiological parameters corresponding to each living body photoelectric signal based on at least two living body photoelectric signals, the method further includes:

[0072] Clustering the photoelectric sensors that collect the at least two living body photoelectric signals according to their positions on the smart mattress to obtain two sensor groups, each sensor group including at least one photoelectric sensor;

[0073] Calculate the average blood pressure value corresponding to each sensor group;

[0074] The sensor group with the smallest average blood pressure value among the two sensor groups is determined as the sensor group corresponding to the user's hands to determine the target photoelectric signal, wherein the sensor group with the largest average blood pressure value is the sensor group corresponding to the user's legs.

[0075] It is understood that by clustering the photosensors that collect at least two living body photosensory signals, the two sensor groups obtained can reflect different body parts. For the same user, blood pressure in the legs is typically higher than blood pressure in the hands. Therefore, the difference in the average blood pressure values corresponding to each sensor group can be used to distinguish the sensor groups corresponding to the hands and legs, and thus determine the target photosensory signal.

[0076] S303: Determine the human body area of the user on the smart mattress according to the target position of the target photoelectric sensor on the smart mattress.

[0077] In the embodiments of the present application, the target photoelectric sensor is a photoelectric sensor in the photoelectric sensor array that collects a target photoelectric signal. The target photoelectric signal is a signal corresponding to the user's target body part. It can be understood that the target position of the target photoelectric sensor on the smart mattress is the projected position of the user's target body part on the smart mattress. Therefore, the user's body area on the smart mattress can be determined based on the target position of the target photoelectric sensor on the smart mattress.

[0078] See also Figure 4 , Figure 4 A schematic diagram of a target photoelectric sensor for collecting target photoelectric signals in a smart mattress provided in an embodiment of the present application. In one possible embodiment, the target body area of the user includes the hands and legs. Figure 4 As shown, in the photosensor array 40 provided on the smart mattress, target sensor 41 labeled 1 is the photosensor corresponding to the user's hand, and target sensor 42 labeled 2 is the photosensor corresponding to the user's hand. The multiple target sensors 41 corresponding to the user's hand and the multiple target sensors 42 corresponding to the user's leg can reflect the placement of the user's hands and legs on the smart mattress, thereby determining the user's body area.

[0079] It should be noted that if Figure 4 The schematic diagram shown is only an example. In actual applications, other photoelectric sensors in the photoelectric sensor array except the target photoelectric sensor can also detect photoelectric signals from other parts of the user's body or items such as clothes and bedding ( Figure 4 not shown).

[0080] In some possible embodiments, after obtaining the target position of the target photoelectric sensor on the smart mattress, various methods can be used, for example, to construct a rectangular area containing all target photoelectric sensors as the human body area, or to combine the human body contour structure and the position of the target body part on the smart mattress with a preset algorithm to determine the human body area with irregular boundaries to improve the accuracy of area detection. In this application, the selection can be made according to actual needs and is not limited here.

[0081] For example, the human body contour structure may include the positional relationship and size ratio of various parts of the human body. Through the position of the target body part and a preset algorithm, such as a path planning algorithm, the position points of various body parts are connected starting from the target body part to obtain the human body area of the user on the smart mattress.

[0082] The human body region detection method provided in embodiments of this application uses a photoelectric sensor array to collect photoelectric signals, identify a target photoelectric signal, and determine the user's body region on the smart mattress based on the position of the target photoelectric sensor. Based on the acquired photoelectric signals, the method can identify the positions of at least two body parts of the user on the smart mattress, thereby locating the user's body region on the smart mattress and improving the accuracy of human body region detection.

[0083] The following describes an implementation method for determining a target photoelectric signal in a human body region detection method provided in an embodiment of the present application.

[0084] See also Figure 5 , Figure 5 A schematic diagram of a process for determining a target photoelectric signal in a human body region detection method provided in an embodiment of the present application is shown as follows: Figure 5 As shown, the method may include the following steps:

[0085] S501, collecting multiple photoelectric signals through a photoelectric sensor array.

[0086] S502 : performing feature extraction on each photoelectric signal among the multiple photoelectric signals to obtain feature parameters corresponding to each photoelectric signal.

[0087] In some possible embodiments, the characteristic parameter includes at least one of signal strength, signal frequency, or signal phase.

[0088] It should be noted that the photoelectric signal collected by the photoelectric sensor is usually a waveform signal in the time domain, such as a photoplethysmography (PPG) signal. By extracting the features of the time domain photoelectric signal, characteristic parameters such as signal strength, signal frequency or signal phase can be obtained.

[0089] The signal strength can be the peak or average amplitude of each photoelectric signal, which can be obtained through signal amplitude analysis. The signal frequency can be the dominant frequency component of each photoelectric signal, which can be obtained by converting the time domain signal into the frequency domain signal using frequency analysis methods such as Fourier transform. The signal phase is the phase information of each photoelectric signal, which can be obtained using phase analysis methods such as Hilbert transform.

[0090] In some possible embodiments, before extracting features from each of the multiple photoelectric signals, the human body region detection method further includes:

[0091] In some possible embodiments, feature extraction is performed on each of the multiple photoelectric signals to obtain feature parameters corresponding to each photoelectric signal, including:

[0092] Preprocessing the multiple photoelectric signals to obtain multiple preprocessed photoelectric signals corresponding to the multiple photoelectric signals, wherein the preprocessing includes filtering the multiple photoelectric signals according to a preset cutoff frequency;

[0093] Feature extraction is performed on each of the plurality of preprocessed photoelectric signals to obtain feature parameters corresponding to each photoelectric signal.

[0094] Exemplarily, the preset cutoff frequency can be set to a value such as 0.1 Hz. Filtering the multiple photoelectric signals according to the preset cutoff frequency includes filtering out the frequency components below the preset cutoff frequency in the multiple photoelectric signals to avoid low-frequency signal components below 0.1 Hz generated by tissues such as bones and muscles, which may affect the recognition of subsequent target photoelectric signals.

[0095] In some possible embodiments, preprocessing may also include baseline correction of multiple photoelectric signals to ensure signal stability; normalization of the photoelectric signals to unify the signal amplitude to a specific range (such as 0-1) to enhance the comparability between different signals; or noise reduction processing to make the photoelectric signal after noise reduction higher in quality and improve the quality of subsequent feature extraction.

[0096] In some possible embodiments, the extracted characteristic parameters of the photoelectric signal may further include period, waveform shape, etc. These characteristic parameters may be obtained through signal processing techniques such as Fourier transform, wavelet transform, or waveform analysis.

[0097] It is understandable that the photoelectric signals collected by the photoelectric sensor in the user's body area can reflect the body's physiological activities, such as heartbeat and breathing. Therefore, its signals show periodic changes and have a certain degree of regularity and stability. For example, the heartbeat signal usually appears as a regular pulse waveform, while the breathing signal appears as a slower periodic fluctuation. In contrast, the photoelectric signals collected from items such as clothing and bedding generally lack this periodicity and may appear as relatively stable or irregular signals. These signals are usually caused by ambient light reflection or non-physiological slight movement and do not contain obvious periodic characteristics.

[0098] Therefore, by extracting and analyzing the characteristic parameters of the photoelectric signal, such as the period and waveform shape, it is possible to effectively distinguish the photoelectric signals reflected by the human body from other photoelectric signals not reflected by the human body, thereby improving the accuracy and robustness of the target photoelectric signal recognition and ensuring that the smart mattress can accurately identify and locate the user's body area.

[0099] S503: Determine a target photoelectric signal according to a plurality of characteristic parameters and a preset characteristic parameter threshold.

[0100] In some possible embodiments, it can be determined that multiple characteristic parameters meet preset characteristic parameter thresholds, such as photoelectric signals corresponding to characteristic parameters greater than a set minimum threshold and / or less than a set maximum threshold, are target photoelectric signals.

[0101] It should be noted that the category of the budgeted characteristic parameter threshold corresponds to the category of the characteristic parameter. For example, in the case where the characteristic parameter is signal frequency, since the signal frequency generated by the human heartbeat and breathing is usually in the range of 0.5Hz to 3Hz, the preset characteristic parameter threshold may include an upper frequency threshold (such as 3Hz) and a lower frequency threshold (such as 0.5Hz). If the frequency of the photoelectric signal does not meet the preset characteristic parameter threshold, that is, it is lower than the lower frequency threshold or higher than the upper frequency threshold, it is likely to be a signal obtained by light interference in the environment or a signal collected from other objects.

[0102] It should be noted that the preset characteristic parameter threshold can be a statistical value derived from multiple sample data points, or a threshold determined based on basic information such as the user's height, weight, gender, or age. For example, the desired characteristic parameter threshold can be determined based on the mapping relationship between the user's basic information and the preset threshold. Normal ranges of heart and respiratory rates vary for users of different age groups, so the frequency characteristic parameter threshold can be adjusted based on the user's age. This personalized setting allows for more precise identification of target photoelectric signals, improving detection accuracy and adaptability.

[0103] In some possible embodiments, the characteristic parameters corresponding to each photoelectric signal may include multiple types, such as signal strength and signal frequency, and the preset characteristic parameter thresholds also need to include multiple types. Corresponding to these characteristic parameters, only when the characteristic parameters of the photoelectric signal all meet their respective preset characteristic parameter thresholds, the photoelectric signal is determined to be a target photoelectric signal.

[0104] In some possible embodiments, determining the target photoelectric signal according to multiple characteristic parameters and preset characteristic parameter thresholds includes:

[0105] Determining at least two living body photoelectric signals from the plurality of photoelectric signals based on the plurality of characteristic parameters and a preset characteristic parameter threshold, wherein the living body photoelectric signals are photoelectric signals collected on the surface of the human body by the photoelectric sensor array;

[0106] Obtaining, based on the at least two living body photoelectric signals, a physiological parameter corresponding to each living body photoelectric signal, wherein the physiological parameter includes at least one of a blood flow velocity and a blood pressure of the user;

[0107] The target photoelectric signal is determined according to the mapping relationship between the physiological parameters and the target body parts corresponding to each living body photoelectric signal, wherein the target body part mapping relationship includes a mapping relationship between a preset body part and a preset physiological parameter threshold.

[0108] It will be appreciated that, to improve the accuracy of body region detection, after determining at least two of the multiple photoelectric signals, it is possible to further determine whether each of the at least two photoelectric signals belongs to a target body region, and to which target body region. Therefore, based on the at least two photoelectric signals, the corresponding physiological parameters of each photoelectric signal can be obtained through techniques such as PPG processing. Due to differences in blood vessels and tissue structures in different parts of the human body, the physiological parameters measured at each location vary, allowing for differentiation of photoelectric signals from different body regions.

[0109] For example, blood flow velocity can be calculated by analyzing signal characteristics in the time or frequency domain, such as by calculating the slope change of the rising or falling edge of the photoelectric signal and the interval between the peaks of the photoelectric signal. Clustering can be performed based on the locations of the photosensors corresponding to multiple in vivo photoelectric signals to obtain multiple sensor groups. Based on the spatial and temporal characteristics corresponding to the multiple in vivo photoelectric signals in each sensor group, a common blood pressure prediction value is determined for each sensor group, thereby obtaining the blood pressure corresponding to each in vivo photoelectric signal.

[0110] In some possible embodiments, the smart mattress further includes a plurality of inflatable and deflable air bags to improve the accuracy of blood pressure testing, which will not be described in detail here.

[0111] In some possible embodiments, the physiological parameters may further include parameters such as the user's blood flow density and blood oxygen, so as to distinguish the body part to which the living photoelectric signal belongs.

[0112] In some possible embodiments, the target body part mapping relationship can be pre-set based on a large amount of data and statistical models, or generated based on basic user information (such as age, weight, gender, etc.), which is not limited here. Through the target body part mapping relationship, the physiological parameter threshold corresponding to each living body photoelectric signal can be determined based on the physiological parameter corresponding to the body part, thereby determining the target photoelectric signal.

[0113] In some possible embodiments, the photoelectric sensor array includes multiple partitions, where different partitions correspond to different body parts of the user. The target photoelectric signal is determined based on multiple characteristic parameters and preset characteristic parameter thresholds, including:

[0114] Determining at least two living body photoelectric signals from the plurality of photoelectric signals based on the plurality of characteristic parameters and a preset characteristic parameter threshold, wherein the living body photoelectric signals are photoelectric signals collected on the surface of the human body by the photoelectric sensor array;

[0115] The target photoelectric signal is determined according to the partition to which the photoelectric sensor corresponding to each living body photoelectric signal belongs.

[0116] It should be noted that the multiple partitions may include partitions of the target body part and partitions corresponding to other body parts. In this way, after obtaining multiple living photoelectric signals through examination, the living photoelectric signal in the partition corresponding to the target body part can be determined as the target photoelectric signal corresponding to the target body part, thereby improving the recognition efficiency and providing data support for the personalized function adjustment of the smart mattress.

[0117] S504: Determine the human body area of the user on the smart mattress according to the target position of the target photoelectric sensor on the smart mattress.

[0118] In the above technical solution, multiple photoelectric signals are collected through a photoelectric sensor array, and features of each photoelectric signal are extracted and analyzed, so that the target photoelectric signal can be identified and the target body part of the user can be accurately positioned.

[0119] The following describes an implementation method for determining the human body area of a user on a smart mattress in the human body area detection method provided in an embodiment of the present application.

[0120] See also Figure 6 , Figure 6 A schematic diagram of a process for detecting the human body area of a user on a smart mattress in a human body area detection method provided in an embodiment of the present application, such as Figure 6 As shown, the method may include the following steps:

[0121] S601, collecting multiple photoelectric signals through a photoelectric sensor array.

[0122] S602: Determine a target photoelectric signal among multiple photoelectric signals.

[0123] S603: Obtain basic information of the user.

[0124] In some possible embodiments, the basic information of the user includes at least one of height or weight.

[0125] It is understandable that the user's body proportions and general outline can be obtained through the user's basic information, providing a reference basis for subsequent human body area determination.

[0126] In some possible embodiments, the user's basic information may be pre-stored in the memory of the smart mattress, or may be input by the user through an input device or mobile device application while the user is using the mattress. For example, when the user's mobile device, such as a mobile phone or smart wearable device, approaches the smart mattress, the user's basic information may be obtained through Bluetooth, NFC, or other methods.

[0127] In some possible embodiments, multiple different users may have used the smart mattress, and basic information of the users may be obtained, including:

[0128] According to the target photoelectric signal, the identity information of the current user is determined from the stored user database, and the pre-stored basic information of the current user is obtained.

[0129] It should be noted that the user database can store the basic information of multiple users. By matching the characteristic parameters of the target photoelectric signal with the records in the database, the current user can be quickly and accurately identified. For example, when each user uses a smart mattress, the photoelectric signal characteristics of their resting state can be recorded and associated with the user's basic information.

[0130] S604: Determine the human body area based on the basic information and the target position of the target photoelectric sensor on the smart mattress.

[0131] In some possible embodiments, determining a human body region based on basic information and a target position of a target photoelectric sensor on a smart mattress includes:

[0132] Obtaining the user's body feature information based on the basic information and the preset body proportion coefficient, wherein the body feature information includes target sizes of different parts of the user;

[0133] The human body area is determined based on the target position and human body feature information.

[0134] It should be noted that the preset human body proportion coefficient can be a coefficient obtained based on ergonomics to indicate the relative size and proportional relationship between different parts of the user's body. By presetting the human body proportion coefficient, the approximate size and position of each part of the user's body can be obtained, thereby more accurately determining the human body area. Exemplarily, the preset human body proportion coefficient includes the head proportion coefficient, which is the ratio of the head length to the body height, usually about 1 / 8; the shoulder proportion coefficient: the ratio of the shoulder width to the body height, usually about 1 / 4; the arm proportion coefficient, which is the ratio of the arm length to the body height, usually about 1 / 3; and the leg proportion coefficient, which is the ratio of the leg length to the body height, usually about 1 / 3. The above-mentioned proportion coefficients can also be adjusted according to parameters such as the user's weight to estimate the target size of each part of the user's body.

[0135] In some possible embodiments, a path planning algorithm or other geometric modeling methods, such as the A* algorithm or the Dijkstra algorithm, can be used to start from the target body part and connect the position points of each body part according to the target size of different parts to obtain the human body area of the user on the smart mattress.

[0136] The basic information and target position are input into the prediction model to obtain the user's body area on the smart mattress, wherein the prediction model is trained based on multiple sample basic information, sample positions and sample body areas.

[0137] It should be noted that the prediction model is built using a machine learning algorithm and trained on a large amount of sample data. This training data can include basic information such as height, weight, and age of different users, as well as the corresponding target photoelectric sensor locations and actual body regions. The prediction model uses this training data to learn the mapping between basic user information, sensor locations, and body regions. By inputting the current user's basic information and the target photoelectric sensor locations into the prediction model, the user's regional distribution on the smart mattress can be predicted, providing a precise basis for adjusting the smart mattress's functions to meet personalized needs.

[0138] In the above technical solution, multiple photoelectric signals are collected through a photoelectric sensor array, and combined with the user's basic information and the position of the target photoelectric sensor, accurate detection of the human body area is achieved, thereby improving the accuracy and robustness of detection.

[0139] See also Figure 7 , Figure 7 A flow chart of adjusting the function of a smart mattress in the human body region detection method provided in an embodiment of the present application is shown as follows: Figure 7 As shown, the method may include the following steps:

[0140] S701, collecting multiple photoelectric signals through a photoelectric sensor array.

[0141] S702: Determine a target photoelectric signal among multiple photoelectric signals.

[0142] S703: Determine the human body area of the user on the smart mattress according to the target position of the target photoelectric sensor on the smart mattress.

[0143] S704: Determine a target functional partition among multiple functional partitions according to the human body region.

[0144] In some possible embodiments, the smart mattress is further provided with a plurality of functional zones, each of which includes a corresponding functional module. The target functional zone at least partially overlaps with the human body area.

[0145] It should be noted that multiple functional partitions can realize different functions such as massage, heating, ventilation, and support, and the functional modules are structural components that provide the above functions.

[0146] For example, if the functional area is a massage area, the functional module can be equipped with a massage motor and rollers, and different massage effects can be achieved by adjusting the motor speed and the roller movement mode. If the functional area is a heating area, the functional module can be installed with a heating wire to adjust the heating temperature according to user needs to provide a warm and comfortable sleeping environment. If the functional area is a ventilation area, the functional module can be equipped with ventilation holes and fans to promote air circulation, keep the mattress dry, and improve user comfort. If the functional area is a support area, the functional module can include a hydraulic rod or airbag with adjustable hardness to adjust the support strength.

[0147] In some possible embodiments, a polygon collision detection algorithm or a region intersection calculation method may be used to determine a target functional partition having at least a partial overlap with a human body region among multiple functional partitions.

[0148] For example, the human body region can be represented as a polygon. A polygon collision detection algorithm is used to check whether the human body region polygon intersects with the polygons of each functional partition. If an intersection exists, it indicates that the functional partition overlaps with the human body region and is thus identified as the target functional partition. This allows for rapid identification of functional partitions related to the human body region, providing a basis for subsequent functional adjustments.

[0149] See also Figure 8 , Figure 8 A schematic diagram of a smart mattress provided in an embodiment of the present application having multiple functional partitions, such as Figure 8 As shown, the smart mattress includes functional zones 1 to 16, among which functional zones 1 to 8 and functional zones 10 and 11 overlap with the human body area and are target functional zones. In this way, it is only necessary to adjust the working parameters of these target functional zones to achieve precise functional adjustment of the user's human body area, while reducing overall energy consumption and improving the energy-saving effect of the smart mattress.

[0150] S705: Adjust the working parameters of the functional modules corresponding to the target functional partition.

[0151] In some possible embodiments, the operating parameters of the functional modules include on or off. In this way, the operating parameters of the functional modules of each functional partition can be flexibly controlled based on the user's body area.

[0152] For example, the functional modules corresponding to the target functional area are controlled to be turned on to provide the required massage, heating, ventilation, or support functions, while the functional modules corresponding to the remaining functional areas are controlled to be turned off to save energy and reduce unnecessary interference. In this way, the smart mattress can meet the user's personalized needs, provide a more intelligent and comfortable user experience, and reduce overall energy consumption.

[0153] In some possible embodiments, adjusting the operating parameters of the functional modules corresponding to the target functional partition includes:

[0154] Determine the user's sleep quality based on the target photoelectric signal;

[0155] According to the mapping relationship between sleep quality and target working parameters, the working parameters of the functional modules corresponding to the target functional partitions are adjusted, wherein the target working parameter mapping relationship includes a mapping relationship between preset sleep quality and preset working parameters.

[0156] It should be noted that in addition to turning on or off, working parameters can also include intensity adjustment, time setting or mode selection. The user's heart rate, respiratory rate, blood oxygen and other physiological parameters can be obtained through the target photoelectric signal. These physiological parameters can be used to evaluate the user's sleep state and quality. For example, a stable heart rate and a uniform breathing rate usually indicate that the user is in a deep sleep stage, while an accelerated heart rate and an irregular breathing rate may indicate that the user is in a light sleep or interrupted sleep state. Based on the sleep quality evaluated by these physiological parameters, the smart mattress can automatically adjust the working parameters of the target functional partition according to the target working parameter mapping relationship, such as adjusting the massage intensity, support strength or heating temperature, to optimize the user's sleeping environment and improve sleep quality. This automated adjustment method can significantly enhance the user experience.

[0157] In some possible embodiments, since the target photoelectric signal includes photoelectric signals of different target body parts, in order to ensure the accuracy of sleep quality assessment, the various photoelectric signals contained in the target photoelectric signal can be fused to obtain a fused photoelectric signal, or the photoelectric signal corresponding to a specified body part (such as a hand) can be selected to determine the user's sleep quality, which is not limited here.

[0158] In the above technical solution, the functions of the smart mattress can be adjusted according to the detected human body areas, thereby achieving accurate identification of human body areas and personalized function adjustment, thereby improving the user experience of using the smart mattress.

[0159] It should be understood that, although the steps in the above-mentioned flowcharts are shown in sequence according to the instructions of the arrows, these steps are not necessarily performed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be performed in other orders. Moreover, at least a portion of the steps in the above-mentioned flowcharts may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily to be performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0160] Based on the foregoing embodiments, an embodiment of the present application provides a smart mattress, which includes the modules included therein and the units included in each module, which can be implemented by a processor; of course, it can also be implemented by a specific logic circuit; in the implementation process, the processor can be a central processing unit (CPU), a microprocessor (MPU), a digital signal processor (DSP) or a field programmable gate array (FPGA), etc.

[0161] See also Figure 9 , Figure 9 A schematic diagram of the structure of the smart mattress provided in the embodiment of the present application is shown as follows: Figure 9 As shown, the smart mattress is provided with a photoelectric sensor array, and the smart mattress includes a collection unit 901 and a processing unit 902, wherein:

[0162] The acquisition unit 901 is configured to acquire a plurality of photoelectric signals through a photoelectric sensor array.

[0163] The processing unit 902 is used to determine a target photoelectric signal among multiple photoelectric signals, wherein the target photoelectric signal is a signal corresponding to a target body part of the user, and the target body part includes at least two body parts of the neck, hands and legs.

[0164] The processing unit 902 is further configured to determine the user's body area on the smart mattress according to the target position of the target photoelectric sensor on the smart mattress, wherein the target photoelectric sensor is a photoelectric sensor in the photoelectric sensor array that collects the target photoelectric signal.

[0165] In some possible embodiments, the processing unit 902 is further used to perform feature extraction on each photoelectric signal among the multiple photoelectric signals to obtain characteristic parameters corresponding to each photoelectric signal, where the characteristic parameters include at least one of signal intensity, signal frequency, or signal phase; and determine the target photoelectric signal based on the multiple characteristic parameters and a preset characteristic parameter threshold.

[0166] In some possible embodiments, the processing unit 902 is further used to determine at least two living photoelectric signals from multiple photoelectric signals based on multiple characteristic parameters and preset characteristic parameter thresholds, wherein the living photoelectric signals are photoelectric signals collected by the photoelectric sensor array on the surface of the human body; obtain physiological parameters corresponding to each living photoelectric signal based on the at least two living photoelectric signals, wherein the physiological parameters include at least one of the user's blood flow velocity and blood pressure; and determine a target photoelectric signal based on a mapping relationship between the physiological parameters corresponding to each living photoelectric signal and a target body part, wherein the target body part mapping relationship includes a mapping relationship between a preset body part and a preset physiological parameter threshold.

[0167] In some possible embodiments, the processing unit 902 is further configured to obtain basic information of the user, wherein the basic information includes at least one of height or weight; and determine a human body region based on the basic information and a target position of the target photoelectric sensor on the smart mattress.

[0168] In some possible embodiments, the processing unit 902 is further used to obtain the user's body feature information based on the basic information and a preset body proportion coefficient, wherein the body feature information includes the target size of different parts of the user; and determine the body area based on the target position and the body feature information.

[0169] In some possible embodiments, the smart mattress is further provided with multiple functional partitions, each functional partition includes a corresponding functional module, and the smart mattress further includes an adjustment unit for determining a target functional partition among the multiple functional partitions based on a human body area, wherein the target functional partition has at least a partial overlapping area with the human body area; and adjusting the working parameters of the functional module corresponding to the target functional partition.

[0170] In some possible embodiments, the adjustment unit is further used to determine the user's sleep quality based on the target photoelectric signal; and adjust the working parameters of the functional modules corresponding to the target functional partitions based on the mapping relationship between the sleep quality and the target working parameters, wherein the target working parameter mapping relationship includes a mapping relationship between a preset sleep quality and a preset working parameter.

[0171] The description of the above smart mattress embodiment is similar to the description of the above method embodiment and has similar beneficial effects as the method embodiment. For technical details not disclosed in the smart mattress embodiment of this application, please refer to the description of the method embodiment of this application for understanding.

[0172] It should be noted that in the embodiments of this application Figure 9The module division of the smart mattress shown is schematic and represents only one logical functional division. Different division methods may be used in actual implementation. Furthermore, the functional units in the various embodiments of this application may be integrated into a single processing unit, exist as separate physical units, or be integrated into a single unit. These integrated units may be implemented in hardware or software functional units. A combination of software and hardware may also be used.

[0173] It should be noted that, in the embodiment of the present application, if the above method is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the relevant technology can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling an electronic device to execute all or part of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a U disk, a mobile hard disk, a read-only memory (ROM), a magnetic disk or an optical disk. In this way, the embodiment of the present application is not limited to any specific combination of hardware and software.

[0174] The embodiment of the present application provides a smart mattress, the internal structure of which can be shown as follows: Figure 10 As shown. The smart mattress includes a processor, memory, and a network interface connected via a system bus. The processor of the smart mattress is used to provide computing and control capabilities. The memory of the smart mattress includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the smart mattress is used to store data. The network interface of the smart mattress is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the above method is implemented.

[0175] An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the method provided in the above embodiment are implemented.

[0176] An embodiment of the present application provides a computer program product containing instructions, which, when executed on a computer, enables the computer to execute the steps of the method provided in the above method embodiment.

[0177] Those skilled in the art will understand that Figure 10The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present application, and does not constitute a limitation on the smart mattress to which the solution of the present application is applied. A specific smart mattress may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0178] In one embodiment, the smart mattress provided by the present application can be implemented in the form of a computer program. Figure 10 The memory of the smart mattress can store the various program modules that make up the smart mattress. The computer program composed of each program module enables the processor to execute the steps of the method of each embodiment of the present application described in this specification.

[0179] It should be noted that the description of the above storage medium and device embodiments is similar to the description of the above method embodiments and has similar beneficial effects as the method embodiments. For technical details not disclosed in the storage medium, storage medium, and device embodiments of this application, please refer to the description of the method embodiments of this application for understanding.

[0180] It should be understood that "one embodiment" or "an embodiment" or "some embodiments" mentioned throughout the specification means that the specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in an embodiment" or "in some embodiments" appearing throughout the specification do not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application. The above-mentioned serial numbers of the embodiments of the present application are for description only and do not represent the advantages and disadvantages of the embodiments. The above description of the various embodiments tends to emphasize the differences between the various embodiments. The same or similar aspects can be referenced to each other. For the sake of brevity, they will not be repeated here.

[0181] The term "and / or" in this article is only a description of the association relationship between associated objects, indicating that there can be three relationships. For example, object A and / or object B can mean: object A exists alone, object A and object B exist at the same time, and object B exists alone.

[0182] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0183] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The embodiments described above are merely illustrative. For example, the division of the modules is merely a logical function division. In actual implementation, there may be other division methods, such as: multiple modules or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or modules can be electrical, mechanical or other forms.

[0184] The modules described above as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules; they may be located in one place or distributed across multiple network units; some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment.

[0185] In addition, all functional modules in the embodiments of the present application can be integrated into one processing unit, or each module can be a separate unit, or two or more modules can be integrated into one unit; the above-mentioned integrated modules can be implemented in the form of hardware or in the form of hardware plus software functional units.

[0186] Those skilled in the art will understand that all or part of the steps of implementing the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM), magnetic disks or optical disks, and other media that can store program codes.

[0187] Alternatively, if the above-mentioned integrated unit of the present application is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application, or the part that contributes to the relevant technology, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling an electronic device to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROMs, magnetic disks or optical disks.

[0188] The methods disclosed in the several method embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments.

[0189] The features disclosed in the several product embodiments provided in this application can be arbitrarily combined without conflict to obtain new product embodiments.

[0190] The features disclosed in the several method or device embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments or device embodiments.

[0191] The above is merely an embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for detecting a human body region, characterized in that: Applied to a smart mattress, the smart mattress is provided with a photoelectric sensor array, and the method includes: Collecting multiple photoelectric signals through the photoelectric sensor array; Determining a target photoelectric signal from the multiple photoelectric signals, wherein the target photoelectric signal is a signal corresponding to a target body part of the user, and the target body part includes at least two body parts of the neck, hands, and legs; The human body area of the user on the smart mattress is determined according to the target position of the target photoelectric sensor on the smart mattress, wherein the target photoelectric sensor is a photoelectric sensor in the photoelectric sensor array that collects the target photoelectric signal.

2. The method according to claim 1, characterized in that Determining a target photoelectric signal among the multiple photoelectric signals includes: Performing feature extraction on each of the multiple photoelectric signals to obtain a feature parameter corresponding to each photoelectric signal, wherein the feature parameter includes at least one of signal strength, signal frequency, or signal phase; The target photoelectric signal is determined according to a plurality of characteristic parameters and a preset characteristic parameter threshold.

3. The method according to claim 2, characterized in that The step of determining the target photoelectric signal according to the plurality of characteristic parameters and a preset characteristic parameter threshold comprises: Determining at least two living body photoelectric signals from the multiple photoelectric signals based on the multiple characteristic parameters and the preset characteristic parameter threshold, wherein the living body photoelectric signals are photoelectric signals collected by the photosensor array on the surface of the human body; Obtaining, based on the at least two living body photoelectric signals, a physiological parameter corresponding to each living body photoelectric signal, wherein the physiological parameter includes at least one of a blood flow velocity and a blood pressure of the user; The target photoelectric signal is determined according to a mapping relationship between a physiological parameter corresponding to each living body photoelectric signal and a target body part, wherein the target body part mapping relationship includes a mapping relationship between a preset body part and a preset physiological parameter threshold.

4. The method according to claim 1, wherein The determining the human body area of the user on the smart mattress according to the target position of the target photoelectric sensor on the smart mattress includes: Acquiring basic information of the user, wherein the basic information includes at least one of height or weight; The human body area is determined according to the basic information and the target position of the target photoelectric sensor on the smart mattress.

5. The method according to claim 4, characterized in that The determining the human body area according to the basic information and the target position of the target photoelectric sensor on the smart mattress includes: Obtaining body feature information of the user according to the basic information and a preset body proportion coefficient, wherein the body feature information includes target sizes of different parts of the user; The human body region is determined according to the target position and the human body feature information.

6. The method according to claim 1, characterized in that The smart mattress is further provided with a plurality of functional partitions, each functional partition including a corresponding functional module. After determining the human body area of the user on the smart mattress according to the target position of the target photoelectric sensor on the smart mattress, the method further includes: Determining a target functional zone among the multiple functional zones according to the human body zone, wherein the target functional zone and the human body zone have at least a partially overlapping area; Adjust the operating parameters of the functional modules corresponding to the target functional partitions.

7. The method according to claim 6, characterized in that The adjusting the working parameters of the functional modules corresponding to the target functional partitions includes: determining the sleep quality of the user according to the target photoelectric signal; According to the mapping relationship between the sleep quality and the target working parameter, the working parameters of the functional modules corresponding to the target functional partition are adjusted, wherein the target working parameter mapping relationship includes a mapping relationship between preset sleep quality and preset working parameters.

8. A smart mattress, characterized in that: The smart mattress is provided with a photoelectric sensor array, including: an acquisition unit, configured to acquire a plurality of photoelectric signals through the photoelectric sensor array; a processing unit, configured to determine a target photoelectric signal from the plurality of photoelectric signals, wherein the target photoelectric signal is a signal corresponding to a target body part of the user, the target body part comprising at least two body parts of the user's neck, hands, and legs; The processing unit is further configured to determine the body area of the user on the smart mattress according to the target position of the target photoelectric sensor on the smart mattress, wherein the target photoelectric sensor is a photoelectric sensor in the photoelectric sensor array that collects the target photoelectric signal.

9. A smart mattress, characterized in that: include: a memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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