Physiological feature detection methods, handheld devices and computer-readable storage media

Abstract

This invention discloses a physiological feature detection method, a handheld device, and a computer-readable storage medium. The method includes: extracting sensing data collected by a sensing component at a target position where the handheld device is held; determining a physiological feature signal based on the sensing data; and outputting the physiological feature signal. This solves the problem of low detection accuracy of physiological feature signals during the use of handheld devices, and improves the detection accuracy of physiological feature signals during handheld device use.

Description

Technical Field

[0001] This invention relates to the field of intelligent electronic device technology, and in particular to a physiological characteristic detection method, a handheld device, and a computer-readable storage medium. Background Technology

[0002] With the rapid development of virtual reality technology, virtual products are being used more and more widely. Handheld devices are usually an indispensable part of VR products. These devices are equipped with photosensors to detect physiological characteristic signals. Their working principle is that when an infrared emitting diode on the handheld device emits light towards the wrist, the light reflected back through the skin tissue is received by the photosensor and converted into an electrical signal. This electrical signal is then converted into a digital signal, thus enabling the detection of physiological characteristics during virtual gaming. However, as light passes through the skin tissue and reflects back to the photosensor, the light intensity decreases, leading to inaccurate detection of the physiological characteristic signals. Summary of the Invention

[0003] This application provides a physiological feature detection method, a handheld device, and a computer-readable storage medium, aiming to improve the detection accuracy of physiological feature signals during the use of the handheld device.

[0004] This application provides a physiological feature detection method applied to a handheld device, the handheld device being equipped with a sensing component, the physiological feature detection method comprising:

[0005] Extract sensor data collected by sensor components at the target location on the handheld device;

[0006] Based on the sensor data, physiological characteristic signals are determined;

[0007] Output the physiological characteristic signal.

[0008] Optionally, the target location includes a first target location and a second target location, and the step of extracting the sensing data collected by the sensing component at the target location of the handheld device in the gripping position includes:

[0009] Acquire first sensing data collected by the sensing component at the first target location, and second sensing data collected by the sensing component at the second target location;

[0010] The step of determining the physiological characteristic signal based on the sensed data includes:

[0011] The physiological characteristic signal is determined based on the first physiological characteristic signal determined from the first sensing data and the characteristic signal determined from the second sensing data.

[0012] Optionally, the step of determining the physiological characteristic signal based on the first physiological characteristic signal determined from the first sensing data and the characteristic signal determined from the second sensing data includes:

[0013] The first physiological characteristic signal is determined based on the first sensing data;

[0014] The grip force of the handheld device is determined based on the feature signal corresponding to the second sensing data;

[0015] When the gripping force is greater than the preset gripping force, the first physiological characteristic signal is determined to be the physiological characteristic signal.

[0016] Optionally, the feature signal includes a first feature signal for determining the grip force of the handheld device and a second feature signal for determining a second physiological feature signal; after the step of determining the grip force of the handheld device based on the feature signal corresponding to the second sensing data, the method further includes:

[0017] When the gripping force is less than or equal to the preset gripping force, the physiological characteristic signal is determined based on the changing trends of the first physiological characteristic signal and the second physiological characteristic signal.

[0018] Optionally, the step of determining the physiological characteristic signal based on the changing trends of the first physiological characteristic signal and the second physiological characteristic signal when the gripping force is less than or equal to the preset gripping force includes:

[0019] The first signal change trend is determined based on the first physiological characteristic signal, and the second signal change trend is determined based on the second physiological characteristic signal;

[0020] When the similarity between the trend of change of the first signal and the trend of change of the second signal is greater than a preset value, the first physiological feature signal is determined to be the physiological feature signal.

[0021] Optionally, the step of determining the physiological characteristic signal based on the first physiological characteristic signal determined from the first sensing data and the characteristic signal determined from the second sensing data includes:

[0022] The first physiological characteristic signal is determined based on the first sensing data;

[0023] A second physiological characteristic signal is determined based on the second sensing data, wherein the characteristic signal determined by the second sensing data is the second physiological characteristic signal;

[0024] The physiological characteristic signal is determined based on the changing trends of the first physiological characteristic signal and the second physiological characteristic signal.

[0025] Optionally, the handheld device includes a left-handed device and a right-handed device, both of which are equipped with the sensing component. The step of acquiring the first sensing data collected by the sensing component at the first target location and the second sensing data collected by the sensing component at the second target location includes:

[0026] The first sub-sensing data collected by the sensing component at the first target position of the left-hand handheld device and the second sub-sensing data collected by the sensing component at the first target position of the right-hand handheld device are respectively acquired. Based on the first sub-sensing data and the second sub-sensing data, the first sensing data is determined.

[0027] The first sub-sensing data collected by the sensing component at the second target position of the left-hand handheld device and the second sub-sensing data collected by the sensing component at the second target position of the right-hand handheld device are respectively acquired. The second sensing data is determined based on the first sub-sensing data and the second sub-sensing data.

[0028] Optionally, the method further includes:

[0029] Acquire human factors data when the handheld device is held;

[0030] The gripping position of the handheld device is determined based on the human factors data.

[0031] In addition, to achieve the above objectives, the present invention also provides a handheld device, the handheld device comprising: a memory, a processor, and a physiological feature detection program stored in the memory and executable on the processor, wherein the physiological feature detection program, when executed by the processor, implements the steps of the above-described physiological feature detection method.

[0032] In addition, to achieve the above objectives, the present invention also provides a computer-readable storage medium storing a physiological feature detection program, which, when executed by a processor, implements the steps of the above-described physiological feature detection method.

[0033] This application provides a physiological feature detection method, handheld device, and computer-readable storage medium. Compared to related technologies where light emitted by an infrared emitting diode passes through skin tissue and is then reflected to a photosensitive sensor for analog-to-digital conversion to obtain physiological feature signals, this method addresses the issue of light attenuation during reflection, leading to inaccurate physiological feature signal detection. This application incorporates a sensing component within the handheld device. By directly holding the device, the system extracts sensing data from the sensing component at the target location where the device is held, and determines the physiological feature signal based on this extracted data, thereby improving the detection accuracy of the physiological feature signal. Attached Figure Description

[0034] Figure 1 This is a flowchart illustrating the first embodiment of the physiological characteristic detection method of the present invention;

[0035] Figure 2 This is a flowchart illustrating the third embodiment of the physiological characteristic detection method of the present invention;

[0036] Figure 3 This is a flowchart illustrating the fourth embodiment of the physiological characteristic detection method of the present invention;

[0037] Figure 4 This is a schematic diagram of the structure of a handheld device involved in an embodiment of the present invention.

[0038] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. The accompanying drawings are only one embodiment and not the entirety of the invention. Detailed Implementation

[0039] Virtual products are currently widely used in the gaming industry, such as fitness or first-person perspective sports. When users use these applications or immerse themselves in corresponding games, they experience noticeable fluctuations in their physiological characteristics. This biometric information is valuable data that can help product companies better serve their customers. For example, when physiological characteristics fluctuate too rapidly, the system can remind users to rest, or the background can proactively reduce the game's difficulty or intensity to prevent excessive user fatigue and further impact on the gaming experience. Similarly, this biometric information is also important in scenarios such as watching movies, participating in sporting events, and even working in virtual offices. Therefore, it is necessary to add physiological characteristic detection functionality.

[0040] Currently, the main physiological characteristic detection is achieved through photoelectric principles: when light emitted by an infrared emitting diode on a handheld device shines onto the skin, the light reflected back through the skin tissue is received by a photosensor and converted into an electrical signal, which is then converted into a digital signal via analog-to-digital conversion. However, light attenuates as it passes through the skin tissue and reflects back to the photosensor. Under normal conditions, the absorption of light by muscles, bones, veins, and other connecting tissues remains relatively constant. However, blood is different; due to the flow of blood in arteries, its absorption of light naturally varies. When light is converted into an electrical signal, the change in light absorption by arteries, compared to the relatively constant absorption by other tissues, results in a signal that can be divided into a direct current (DC) signal and an alternating current (AC) signal. Extracting the AC signal reveals the characteristics of blood flow, thereby extracting physiological characteristic signals and achieving physiological characteristic detection.

[0041] However, in the process of detecting physiological feature signals using photoelectric principles, the light attenuates during the process of light passing through skin tissue and then reflecting to the photosensitive sensor, leading to inaccurate detection of physiological feature signals. Therefore, to solve the problem of inaccurate physiological feature signal detection in related technologies, this application proposes a physiological feature detection method. This method involves incorporating a sensing component within a handheld device, which is directly held by the hand. By extracting sensing data from the sensing component at the target location where the handheld device is held, physiological feature signals are determined based on the extracted sensing data, thereby improving the detection accuracy of physiological feature signals.

[0042] Secondly, this application places the sensing component inside the handheld device, and the sealed design of the sensing component achieves the effect of waterproofing and dustproofing.

[0043] Furthermore, in the process of detecting physiological feature signals using photoelectric principles, the different skin colors of hands affect the reflected light, leading to errors in the detected physiological feature signals. This application employs a sensing component, specifically a high-sensitivity stress plate. When the handheld device is held, the physiological feature data is determined by the sensing data output from the sensing component at the gripped position, thus avoiding the influence of skin color and improving the detection accuracy of physiological feature signals.

[0044] Finally, the use of infrared emitting diodes to detect physiological characteristic signals results in light pollution. This application avoids the light pollution defects existing in the prior art by setting a sensing component in the handheld device to detect physiological characteristic signals.

[0045] To better understand the above technical solutions, exemplary embodiments of this disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of this disclosure to those skilled in the art.

[0046] like Figure 1As shown, in the first embodiment of this application, the handheld device control method is applied to a handheld device. The virtual product used in conjunction with the handheld device can be VR (Virtual Reality), MR (Mixed Reality), XR (Extended Reality), etc. The handheld device is internally equipped with a sensing component for detecting physiological characteristic signals. The sensing component is a high-sensitivity stress gauge, and a high-sensitivity strain gauge with a sensitivity of less than 2µm can be selected, for example, a stress gauge with a sensitivity of 1µm. Simultaneously, to improve signal quality, the data acquisition area (i.e., the location of the sensing component) needs to be as thin as possible, such as <0.5mm. Structural strength must also be considered; therefore, new material processes, such as carbon fiber, are used to thin the area where the pressure gauge is placed, enabling the detection of changes in physiological characteristic signals and grip force, thereby improving the detection accuracy of physiological characteristic signals.

[0047] Optionally, the placement of the sensing components can be determined according to actual conditions. For example, sensing components can be placed only in one or some localized locations, or they can be distributed throughout the entire handheld device. In the case of placing sensing components in one or some localized locations, the placement can be determined based on the specific circumstances. For example, it can be determined based on the gripping methods of people with different hand shapes. By acquiring ergonomic data (the relative position of the hand and the handheld device) under different gripping methods, the optimal gripping position can be extracted and set as the placement location of the sensing components. Alternatively, the placement can be determined based on different models of handheld devices. Or, the placement can be based on people of different age groups or genders. This allows the sensing components to meet the gripping needs of people in different application scenarios, and by determining the placement of the sensing components for different gripping methods, the detection accuracy of physiological characteristic signals can be improved.

[0048] Specifically, the physiological characteristic detection method of this application includes the following steps:

[0049] Step S110: Extract the sensing data collected by the sensing component at the target position of the handheld device.

[0050] Step S120: Determine physiological characteristic signals based on the sensed data.

[0051] Optionally, the sensor component's location can be defaulted to the gripped position. After determining the sensor component's location based on actual conditions, it is then positioned at that location, and sensor data is collected by the sensor component located at the target position of the handheld device. Optionally, human factors data when the handheld device is gripped can also be acquired, and the gripped position of the handheld device can be determined based on the human factors data. The human factors data refers to the relative position of the hand and the handheld device. A position matrix can be pre-marked on the handheld device model, and the gripped position of the handheld device can be determined based on the relative position of the hand and the handheld device when gripping it.

[0052] Optionally, the gripped position includes multiple location points, from which a target position can be selected. Sensing data collected by the sensing components at the target position is extracted, and physiological characteristic signals are determined based on the sensing data. The target position can be one or more, for example, the target position can be the position corresponding to the fingertip or fingertip when holding the handheld device.

[0053] Optionally, sensor data within a single work cycle can be acquired, representing one pulse beat. Based on the sensor data within a single work cycle, the physiological characteristic signal corresponding to one pulse beat can be determined. Optionally, sensor data within multiple work cycles over a historical period can also be acquired. Based on the sensor data within multiple work cycles over a historical period, the pulse change trend over that historical period can be determined, thereby enabling the monitoring of the user's physiological characteristics.

[0054] Optionally, the sensed data can be voltage data. The sensed component can output voltage data according to the pulse beat. The acquisition circuit can collect the output voltage of the sensed component and convert the output voltage analog-to-digital signal into a physiological characteristic signal.

[0055] Optionally, physiological signals may be heart rate and / or blood pressure.

[0056] Optionally, the handheld device can communicate with terminal devices, such as mobile phones and computers. It can also communicate with a cloud server. Voltage data output from the sensing components at the held position of the handheld device can be acquired periodically or in real-time, and the acquired voltage data can be stored in the cloud server for subsequent use to retrieve the corresponding voltage data and calculate physiological characteristic signals. Optionally, the acquired physiological characteristic signals can also be uploaded to the cloud server in real-time for storage to monitor changes in physiological characteristic signals under different virtual scenarios.

[0057] Step S130: Output the physiological characteristic signal.

[0058] In this embodiment, after determining the physiological characteristic signal, the signal can be sent to an upper-layer application on the terminal device, thereby enabling real-time monitoring of the physiological characteristic signal during the user's use of the handheld device. Specifically, after the handheld device determines the physiological characteristic signal, it sends the signal to the terminal device, where an application is installed to display changes in the signal. Optionally, the signal can also be sent to a cloud server, which then forwards it to the terminal device, where changes are displayed in an application installed on the device.

[0059] Optionally, in addition to displaying changes in physiological characteristic signals, this application can also generate abnormal physiological characteristic signal alerts. These alerts can be displayed on the screen of the handheld device, sent to a terminal device connected to the handheld device, or delivered via voice or vibration. These alerts promptly remind users to pay attention to changes in their own bodies, thereby improving user safety.

[0060] According to the above technical solution, this embodiment sets up a sensing component in the handheld device. The hand directly holds the handheld device, and the sensing data collected by the sensing component at the target position of the handheld device is extracted. Physiological feature signals are determined based on the extracted sensing data. After determining the physiological feature signals, the physiological feature signals are output to the upper layer application. This not only improves the detection accuracy of physiological feature signals, but also avoids the harm to the user's body when using the handheld device for a long time, thus improving the user's physical safety.

[0061] Second embodiment.

[0062] Based on the first embodiment, in the second embodiment of this application, when the gripped position includes a first target position and a second target position, each target position is provided with a corresponding sensing component, namely, a first stress sheet disposed at the first target position and a second stress sheet disposed at the second target position. The physiological characteristic detection method of this application includes the following steps:

[0063] Step S111: Obtain the first sensing data collected by the sensing component at the first target location and the second sensing data collected by the sensing component at the second target location.

[0064] Optionally, the first sensing data may be voltage data, and the second sensing data may be voltage data and / or resistance data.

[0065] Step S121: Determine the physiological characteristic signal based on the first physiological characteristic signal determined by the first sensing data and the characteristic signal determined by the second sensing data;

[0066] Optionally, when the first sensed data is voltage data, the first physiological characteristic signal can be determined based on the voltage data.

[0067] Optionally, the feature signal includes two types: one for determining the grip force of the handheld device, and the other for determining a second physiological feature signal. The second physiological feature signal can be determined based on the second feature signal corresponding to the second sensing data, and the grip force can be determined based on the first feature signal corresponding to the second sensing data.

[0068] Optionally, the sensing component of this application further includes a third stress plate disposed at the second target position, the third stress plate being used to detect the gripping force at the second target position of the handheld device. The third stress plate is a testing tool used to measure the strain of an object. The third stress plate consists of an insulating substrate and a metal sensitive grid. When the tested component deforms under external force, the sensitive grid also deforms, thus causing a corresponding change in the resistance value of the sensitive grid. This minute change in resistance can be measured using a Wheatstone bridge, and the measured resistance change can be converted into an actual strain value using the stress plate coefficient specified by the third stress plate manufacturer. Therefore, the second sensing data output by the third stress plate at the second target position of the handheld device can be obtained, and the gripping force can be determined based on the characteristic signal corresponding to the second sensing data.

[0069] Optionally, the second sensing data includes a resistance signal and / or a voltage signal. The resistance signal output from the third stress plate at the second target position of the handheld device is acquired, and the output resistance signal is converted from analog to digital to obtain the grip force. The voltage signal output from the third stress plate at the second target position of the handheld device is acquired, and the output voltage signal is converted from analog to digital to obtain the second physiological characteristic signal. Therefore, when the second sensing data is voltage data, the second physiological characteristic signal is determined based on the characteristic signal corresponding to the voltage data; and when the second sensing data is resistance data, the grip force is determined based on the characteristic signal corresponding to the resistance data.

[0070] Optionally, the resistance signal collected by the third stress plate at the second target position of the handheld device over a historical period can be acquired. This resistance signal is then converted into grip force to obtain the grip force over the historical period. A grip force characteristic value is calculated based on this historical grip force, and the grip force collected by the third stress plate at the second target position of the handheld device is determined based on the grip force characteristic value. Optionally, determining the grip force collected by the third stress plate at the second target position of the handheld device based on the grip force characteristic value includes: determining the grip force characteristic value as the grip force collected by the sensing component at the gripped position of the handheld device. Optionally, the grip force characteristic value includes, but is not limited to: average grip force, median grip force, maximum grip force, minimum grip force, etc. For example, the average grip force can be determined as the grip force collected by the third stress plate at the second target position of the handheld device. Alternatively, the median grip force can be determined as the grip force collected by the third stress plate at the second target position of the handheld device, etc. This improves the accuracy of the collected grip force.

[0071] Optionally, a first physiological characteristic signal can be determined based on the first sensing data, and the gripping force of the handheld device can be determined based on the characteristic signal corresponding to the second sensing data. When the gripping force is greater than the preset gripping force, the first physiological characteristic signal is determined as the physiological characteristic signal.

[0072] Optionally, after acquiring the grip force, it is necessary to first determine the magnitude of the grip force. If the grip force is large, it indicates that the detected physiological characteristic signal is less likely to have errors. Therefore, when the grip force is greater than a preset grip force, the first physiological characteristic signal is determined as the physiological characteristic signal and output, that is, the first physiological characteristic signal of the fingertip position is output. The preset grip force can be set according to actual conditions.

[0073] Optionally, if the grip force is small, it indicates a higher probability of error in the detected physiological feature signal. In this case, when the grip force is less than or equal to a preset grip force, it indicates that the grip force is very small, and the physiological feature signal can be determined based on the changing trends of the first and second physiological feature signals. Optionally, when the grip force is less than or equal to the preset grip force, a first signal changing trend is determined based on the first physiological feature signal, and a second signal changing trend is determined based on the second physiological feature signal; when the similarity between the first and second signal changing trends is greater than a preset value, the first physiological feature signal is determined as the physiological feature signal, and the first physiological feature signal is output. The preset value can be set according to actual conditions. If the similarity between the first and second signal changing trends is less than the preset value, the sensing data collected by the sensing component at the target position of the handheld device is re-extracted. By monitoring the changing trends of the physiological feature signals at the first and second target positions, the detection accuracy of the physiological feature signal is improved.

[0074] Optionally, some game scenarios can support simultaneous operation of left and right handheld devices, and using dual handheld devices can improve the gaming experience. When the handheld devices include a left-handed device and a right-handed device, each of the left-handed device and the right-handed device is equipped with corresponding sensing components. The first electro-induction signal includes a first sub-electro-induction signal of the first target position of the left-handed device and a second sub-electro-induction signal of the first target position of the right-handed device. The second electro-induction signal includes a third sub-electro-induction signal of the second target position of the left-handed device and a fourth sub-electro-induction signal of the second target position of the right-handed device. Specifically, the first sub-electro-induction signal of the first target position of the left-handed device and the second sub-electro-induction signal of the first target position of the right-handed device can be acquired, as can the third sub-electro-induction signal of the second target position of the left-handed device and the fourth sub-electro-induction signal of the second target position of the right-handed device. Based on the first sub-electro-induction signal and the second sub-electro-induction signal, the first sensing data is determined; based on the third sub-electro-induction signal and the fourth sub-electro-induction signal, the second sensing data is determined.

[0075] Optionally, the average value of the first inductive signal can be determined based on the first sub-inductive signal at the first target position of the left-handed device and the second sub-inductive signal at the first target position of the right-handed device, and this average value can be used as the first inductive data. Alternatively, the average value of the second inductive signal can be determined based on the third sub-inductive signal at the second target position of the left-handed device and the fourth sub-inductive signal at the second target position of the right-handed device, and this average value can be used as the second inductive data. This makes the acquired first and second inductive data more accurate.

[0076] Step S130: Output the physiological characteristic signal.

[0077] Optionally, the first target location in this application can be the fingertip, and the second target location can be the fingertip. Sensor data collected by the sensing components at the fingertip and fingertip positions of the handheld device can be extracted separately. A first physiological characteristic signal is determined based on the first sensor data collected by the first stress piece at the fingertip, and a second physiological characteristic signal is determined based on the second sensor data collected by the second stress piece at the fingertip. Furthermore, the physiological characteristic signal is determined based on the first and second sensor data at the fingertip and fingertip. Because the sensor data is collected from both the fingertip and fingertip positions, and the physiological characteristic signal is determined based on this data, interference from sensor data from other positions is avoided, making the determined physiological characteristic signal more accurate.

[0078] According to the above technical solution, this embodiment uses highly sensitive stress plates at the first and second target positions to focus on monitoring the physiological characteristic signals at the fingertips and simultaneously monitor the pressure value at the fingertips as a basis for judging the strength of hand grip, thereby improving the accuracy of the output first physiological characteristic signal.

[0079] Third embodiment.

[0080] Reference Figure 2 Based on the first and second embodiments, the third embodiment of this application includes the following steps:

[0081] Step S111: Obtain the first sensing data collected by the sensing component at the first target location and the second sensing data collected by the sensing component at the second target location;

[0082] Step S1211: Determine the first physiological characteristic signal based on the first sensing data;

[0083] Step S1212: Determine the grip force of the handheld device based on the feature signal corresponding to the second sensing data;

[0084] Step S210: Determine whether the gripping force is greater than the preset gripping force.

[0085] Step S1213: When the gripping force is greater than the preset gripping force, the first physiological characteristic signal is determined to be the physiological characteristic signal.

[0086] Step S220: When the gripping force is less than or equal to the preset gripping force, determine the first signal change trend based on the first physiological characteristic signal, and determine the second signal change trend based on the second physiological characteristic signal;

[0087] Step S230: When the similarity between the change trend of the first signal and the change trend of the second signal is greater than a preset value, the first physiological feature signal is determined to be the physiological feature signal.

[0088] After steps S1213 and S230, step S130 is executed to output the physiological characteristic signal.

[0089] Fourth embodiment.

[0090] Reference Figure 3 Based on the first to third embodiments, the fourth embodiment of this application includes the following steps:

[0091] Step S111: Obtain the first sensing data collected by the sensing component at the first target location and the second sensing data collected by the sensing component at the second target location;

[0092] Step S2211: Determine the first physiological characteristic signal based on the first sensing data;

[0093] Step S2212: Determine the second physiological characteristic signal based on the second sensing data, wherein the characteristic signal determined by the second sensing data is the second physiological characteristic signal;

[0094] Step S2213: Determine the physiological characteristic signal based on the changing trends of the first physiological characteristic signal and the second physiological characteristic signal.

[0095] Optionally, a first signal change trend is determined based on the first physiological feature signal, and a second signal change trend is determined based on the second physiological feature signal. When the similarity between the first signal change trend and the second signal change trend is greater than a preset value, the first physiological feature signal is determined to be the physiological feature signal, and the first physiological feature signal is output. The preset value can be set according to actual conditions. If the similarity between the first signal change trend and the second signal change trend is less than the preset value, the sensing data collected by the sensing component at the target position of the handheld device is re-extracted. By monitoring the change trends of the physiological feature signals at the first target position and the second target position, the detection accuracy of the physiological feature signals is improved.

[0096] Step S130: Output the physiological characteristic signal.

[0097] The embodiments of the present invention provide an embodiment of a physiological feature detection method. It should be noted that although the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than that shown here.

[0098] like Figure 4 As shown, Figure 4 This is a schematic diagram of the hardware operating environment of the handheld device of the present invention.

[0099] like Figure 4As shown, the handheld device may include: a processor 1001, such as a CPU; a memory 1005; a user interface 1003; a network interface 1004; and a communication bus 1002. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen and an input unit such as a keyboard. Optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be high-speed RAM or non-volatile memory, such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0100] Those skilled in the art will understand that Figure 4 The handheld device structure shown does not constitute a limitation on the handheld device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0101] like Figure 4 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a network communication module, a user interface module, and a physiological characteristic detection program. The operating system is a program that manages and controls the hardware and software resources of the handheld device, the physiological characteristic detection program, and the operation of other software or programs.

[0102] exist Figure 4 In the handheld device shown, the user interface 1003 is mainly used to connect to the terminal and communicate with the terminal; the network interface 1004 is mainly used to communicate with the backend server; and the processor 1001 can be used to call the physiological feature detection program stored in the memory 1005.

[0103] In this embodiment, the handheld device includes: a memory 1005, a processor 1001, and a physiological feature detection program stored in the memory and executable on the processor, wherein:

[0104] When processor 1001 calls the physiological characteristic detection program stored in memory 1005, it performs the following operations:

[0105] Extract sensor data collected by sensor components at the target location on the handheld device;

[0106] Based on the sensor data, physiological characteristic signals are determined;

[0107] Output the physiological characteristic signal.

[0108] When processor 1001 calls the physiological characteristic detection program stored in memory 1005, it performs the following operations:

[0109] Acquire first sensing data collected by the sensing component at the first target location, and second sensing data collected by the sensing component at the second target location;

[0110] The physiological characteristic signal is determined based on the first physiological characteristic signal determined from the first sensing data and the characteristic signal determined from the second sensing data.

[0111] When processor 1001 calls the physiological characteristic detection program stored in memory 1005, it performs the following operations:

[0112] The first physiological characteristic signal is determined based on the first sensing data;

[0113] The grip force of the handheld device is determined based on the feature signal corresponding to the second sensing data;

[0114] When the gripping force is greater than the preset gripping force, the first physiological characteristic signal is determined to be the physiological characteristic signal.

[0115] When processor 1001 calls the physiological characteristic detection program stored in memory 1005, it performs the following operations:

[0116] When the gripping force is less than or equal to the preset gripping force, the physiological characteristic signal is determined based on the changing trends of the first physiological characteristic signal and the second physiological characteristic signal.

[0117] When processor 1001 calls the physiological characteristic detection program stored in memory 1005, it performs the following operations:

[0118] The first signal change trend is determined based on the first physiological characteristic signal, and the second signal change trend is determined based on the second physiological characteristic signal;

[0119] When the similarity between the trend of change of the first signal and the trend of change of the second signal is greater than a preset value, the first physiological feature signal is determined to be the physiological feature signal.

[0120] When processor 1001 calls the physiological characteristic detection program stored in memory 1005, it performs the following operations:

[0121] The first physiological characteristic signal is determined based on the first sensing data;

[0122] A second physiological characteristic signal is determined based on the second sensing data, wherein the characteristic signal determined by the second sensing data is the second physiological characteristic signal;

[0123] The physiological characteristic signal is determined based on the changing trends of the first physiological characteristic signal and the second physiological characteristic signal.

[0124] When processor 1001 calls the physiological characteristic detection program stored in memory 1005, it performs the following operations:

[0125] The first sub-sensing data collected by the sensing component at the first target position of the left-hand handheld device and the second sub-sensing data collected by the sensing component at the first target position of the right-hand handheld device are respectively acquired. Based on the first sub-sensing data and the second sub-sensing data, the first sensing data is determined.

[0126] The first sub-sensing data collected by the sensing component at the second target position of the left-hand handheld device and the second sub-sensing data collected by the sensing component at the second target position of the right-hand handheld device are respectively acquired. The second sensing data is determined based on the first sub-sensing data and the second sub-sensing data.

[0127] When processor 1001 calls the physiological characteristic detection program stored in memory 1005, it performs the following operations:

[0128] Acquire human factors data when the handheld device is held;

[0129] The gripping position of the handheld device is determined based on the human factors data.

[0130] Based on the same inventive concept, this application also provides a computer-readable storage medium storing a physiological feature detection program. When the physiological feature detection program is executed by a processor, it implements the various steps of the physiological feature detection method described above and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0131] Since the computer-readable storage medium provided in the embodiments of this application is a computer-readable storage medium used to implement the methods of the embodiments of this application, those skilled in the art can understand the specific structure and variations of the computer-readable storage medium based on the methods described in the embodiments of this application, and therefore will not be repeated here. All computer-readable storage media used in the methods of the embodiments of this application fall within the scope of protection of this application.

[0132] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0133] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0134] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, television, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0135] The above are merely preferred embodiments of the present invention and do not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for detecting physiological characteristics, characterized in that, Applied to a handheld device, the handheld device being equipped with a sensing component, the physiological characteristic detection method includes: Extract sensor data collected by sensor components at the target location on the handheld device; Based on the sensor data, physiological characteristic signals are determined; Output the physiological characteristic signal; The target location includes a first target location and a second target location, and the step of extracting the sensing data collected by the sensing component at the target location of the handheld device includes: Acquire first sensing data collected by the sensing component at the first target location, and second sensing data collected by the sensing component at the second target location; The step of determining the physiological characteristic signal based on the sensed data includes: The first physiological characteristic signal is determined based on the first sensing data; The grip force of the handheld device is determined based on the feature signal corresponding to the second sensing data; The feature signals include a first feature signal for determining the grip force of the handheld device, and a second feature signal for determining a second physiological feature signal; When the gripping force is less than or equal to a preset gripping force, the physiological characteristic signal is determined based on the changing trends of the first physiological characteristic signal and the second physiological characteristic signal.

2. The physiological characteristic detection method as described in claim 1, characterized in that, After the step of determining the grip force of the handheld device based on the feature signal corresponding to the second sensing data, the method further includes: When the gripping force is greater than the preset gripping force, the first physiological characteristic signal is determined to be the physiological characteristic signal.

3. The physiological characteristic detection method as described in claim 1, characterized in that, The step of determining the physiological characteristic signal based on the changing trends of the first physiological characteristic signal and the second physiological characteristic signal includes: The first signal change trend is determined based on the first physiological characteristic signal, and the second signal change trend is determined based on the second physiological characteristic signal; When the similarity between the trend of change of the first signal and the trend of change of the second signal is greater than a preset value, the first physiological feature signal is determined to be the physiological feature signal.

4. The physiological characteristic detection method as described in claim 1, characterized in that, The handheld device includes a left-handed device and a right-handed device, both of which are equipped with the sensing component. The step of acquiring the first sensing data collected by the sensing component at the first target location and the second sensing data collected by the sensing component at the second target location includes: The first sub-sensing data collected by the sensing component at the first target position of the left-hand handheld device and the second sub-sensing data collected by the sensing component at the first target position of the right-hand handheld device are respectively acquired. Based on the first sub-sensing data and the second sub-sensing data, the first sensing data is determined. The first sub-sensing data collected by the sensing component at the second target position of the left-hand handheld device and the second sub-sensing data collected by the sensing component at the second target position of the right-hand handheld device are respectively acquired. The second sensing data is determined based on the first sub-sensing data and the second sub-sensing data.

5. The physiological characteristic detection method as described in claim 1, characterized in that, The physiological characteristic detection method also includes: Acquire human factors data when the handheld device is held; The gripping position of the handheld device is determined based on the human factors data.

6. A handheld device, characterized in that, The handheld device includes: a processor, a memory, and a physiological feature detection program stored in the memory and executable on the processor, wherein the physiological feature detection program, when executed by the processor, implements the steps of the physiological feature detection method as described in any one of claims 1-5.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a physiological feature detection program, which, when executed by a processor, implements the steps of the physiological feature detection method according to any one of claims 1-5.

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