Mouse running wheel, method and system for quantitatively analyzing running wheel exercise

Abstract

This invention discloses a mouse running wheel, a method for quantitative analysis of running wheel motion, and a system. The mouse running wheel includes a running wheel body for accommodating mice during running and a support frame for supporting the running wheel body. The axles at both ends of the running wheel body are connected to the support frame via short axles, allowing the running wheel body to rotate along its own axis on the support frame. It also includes a sensor mounting base disposed on the short axles, which is used to mount angular velocity sensors to sense the rotation of the short axles. The mouse running wheel with the above structure, using a short axle connected to the support frame to connect the running wheel body, replaces the traditional long axle design, leaving sufficient space inside the running wheel to avoid hindering the mouse's movement, allowing it to move freely and more realistically show its motion state.

Description

Technical Field

[0001] This invention belongs to the field of behavioral research technology, and in particular relates to a mouse running wheel, a method and system for quantitative analysis of running wheel movement. Background Technology

[0002] In the fields of life sciences and medical research, the study of mouse movement behavior is of great significance. For example, mouse movement data can provide researchers with key information in studying mouse physiological functions, drug responses, and movement-related disease models.

[0003] In mouse movement studies, the cage is the fundamental environment for mice's living and movement, and its structural design has a significant impact on mouse movement performance and data collection. Traditional cage structures are typically simple, and the installation method and structure of the running wheel, as the primary exercise facility for mice, have certain limitations. For example, traditional running wheels often use a central axis mounting method, which occupies a significant amount of space within the cage, restricting the mouse's range of motion and limiting its freedom of movement, potentially affecting its normal motor behavior. Simultaneously, friction between the traditional running wheel and the cage is a prominent issue. Due to the lack of effective restraint devices, the running wheel easily rubs against the cage during movement, increasing wheel wear and potentially affecting the smoothness of the mouse's movement, thus interfering with the accuracy of the collected movement data.

[0004] For example, a Chinese patent application discloses a system and method for acquiring mouse running wheel motion data, including a running cage, a support, an angular velocity sensor, a microcontroller system, a display, and an input device. The running cage is used for the mouse to run, and a central axis is provided in the running cage; the support is used to support the running cage; the angular velocity sensor is used to collect angle data during the mouse's running process, wherein the angular velocity sensor is fixed on the support, and the rotation axis of the angular velocity sensor is connected to the central axis. During the mouse's running, the rotation axis of the angular velocity sensor rotates together with the running cage and the central axis; the display is connected to the output terminal of the microcontroller system, the input device is connected to the input terminal of the microcontroller system, and the angular velocity sensor is connected to the input terminal of the microcontroller system. The microcontroller system is used to calculate motion data based on the angle data. The microcontroller system collects the output electrical signal value of the angular velocity sensor every set time interval, calculates the angle difference within the set time interval based on two adjacent electrical signals collected, and then calculates the motion data. This prior art uses a central axis design.

[0005] Furthermore, existing methods and systems for analyzing mouse movement also have some shortcomings. Currently, the collection and processing of mouse wheel running data often lacks a unified and efficient process and methodology. During data collection, data from different sensors may exhibit inconsistent formats and significant differences in data volume, posing considerable difficulties for subsequent data processing and analysis. For example, the data reading phase may require extensive manual data organization and conversion, which is inefficient and prone to errors. During data analysis, methods for handling outlier data are not sufficiently robust, potentially leading to inaccurate and unreliable data. Moreover, when determining key movement indicators such as the total number of laps completed by the mouse, existing methods often lack comprehensive consideration of sensor selection and data reliability, potentially resulting in significant errors in the final results.

[0006] For example, Chinese patent application CN202111243789.X discloses an animal signal acquisition system, including a running cage, a data processor, a resistance measuring device, a counting device, a timing device, and a terminal. This application uses the resistance measuring device, the counting device, and the timing device to acquire the rotational resistance of the running cage, the number of rotations of the running cage, and the rotation time of the running cage, respectively. The data processor is used to acquire the rotational rotation data and rotation time data from the counting device and the timing device, respectively. The terminal generates animal movement ability data based on the rotational rotation data, rotation time data, and the rotational resistance of the running cage. This prior art has the aforementioned problems. Summary of the Invention

[0007] The purpose of this invention is to provide a mouse running wheel, a method and system for quantitative analysis of running wheel movement, which can partially solve or alleviate at least one of the above-mentioned problems. The running wheel has a more reasonable structure, more accurate data collection, and meets the needs of precise data collection and analysis in mouse movement research, providing more reliable technical support for related research.

[0008] To solve the aforementioned technical problems, the present invention specifically adopts the following technical solution:

[0009] A first aspect of the present invention is to provide a mouse running wheel, comprising a running wheel body for accommodating a mouse for running and a support for supporting the running wheel body; the axles at both ends of the running wheel body are respectively connected to the support via short shafts, so that the running wheel body can rotate on the support along its own axis; and further comprising a sensor mounting base disposed on the short shaft, the sensor mounting base being used to mount an angular velocity sensor to sense the rotation of the short shaft.

[0010] As an improvement, the running wheel body has connecting holes on the shafts at both ends. One end of the short shaft is fixed to the running wheel body using the connecting holes, and the other end is embedded in the U-shaped groove on the bracket and can rotate in the U-shaped groove to drive the running wheel body to rotate.

[0011] As an improvement, the short shaft is made of engineering plastic and is provided with a limiting ring. The limiting rings on the two short shafts clamp and fix the running wheel body.

[0012] As an improvement, the sensor mounting base is located at the end of the short shaft, and there are two sensor mounting bases respectively located on the two short shafts.

[0013] This invention also provides a method for quantitative analysis of running wheel motion, applied to the aforementioned mouse running wheel, comprising:

[0014] Target sensor screening steps:

[0015] Install the sensor to be screened on the sensor mounting base of the mouse running wheel, rotate the mouse running wheel according to the preset number of revolutions, and collect the data returned by the sensor to be screened;

[0016] Using the shortest sensor data as the baseline, other sensor data to be screened are cropped.

[0017] All sensor data to be screened are screened, and abnormal data found during the screening are corrected.

[0018] Calculate the number of mouse wheel rotations recorded by each sensor to be screened based on the data from each sensor to be screened;

[0019] Compare the number of mouse wheel rotations recorded by each sensor to be screened with the preset number of rotations, and select one or two angular velocity sensors corresponding to the sensor data with the smallest difference from the preset number of rotations as the target sensors;

[0020] Measurement steps:

[0021] The target sensor was installed on the mouse running wheel. The target sensor was used to detect the rotation of the mouse running wheel driven by the mouse's running motion and to collect the data transmitted back by the target sensor.

[0022] When there is only one target sensor, the number of rotations of the mouse running wheel is calculated based on the data returned by the target sensor;

[0023] When there are two target sensors, the data returned by the two target sensors are compared. If the difference is less than a threshold, the number of rotations of the mouse running wheel is calculated using the data returned by the two target sensors; otherwise, the data returned by the two target sensors is considered invalid.

[0024] As an improvement, the steps for cropping the sensor data to be screened include:

[0025] Calculate the difference in the number of sensor data to be screened compared with the baseline data;

[0026] The sensor data to be screened is divided into corresponding segments based on the difference in quantity.

[0027] One data point is randomly deleted from each data segment.

[0028] As an improvement, the method for calculating the number of rotations of the mouse running wheel based on sensor data is to use the following formula:

[0029]

[0030] Calculate the number of rotations of the mouse running wheel; where R n Let ωi be the number of rotations of the mouse running wheel recorded by the sensor numbered n, ωi be the angular velocity value of the i-th sensor, and N be the number of sensor data.

[0031] As an improvement, when there are two target sensors, the formula is used.

[0032] R = a * R₁² + b * R₂²

[0033] To calculate the number of rotations of the mouse running wheel; where R is the number of rotations, R12 is the number of rotations of the mouse running wheel recorded by the first target sensor in the measurement step, R22 is the number of rotations of the mouse running wheel recorded by the second target sensor in the measurement step, a is the weight of the first target sensor, b is the weight of the second target sensor, and a+b=1;

[0034] Using the formula:

[0035]

[0036] Calculate the weight of the first target sensor; where a is the weight of the first target sensor, R11 is the number of mouse wheel rotations recorded by the first target sensor in the target sensor screening step, R21 is the number of mouse wheel rotations recorded by the second target sensor in the target sensor screening step, and r is the preset number of rotations;

[0037] Using the formula:

[0038]

[0039] Calculate the weight of the second target sensor; where b is the weight of the second target sensor, R11 is the number of mouse wheel rotations recorded by the first target sensor, R21 is the number of mouse wheel rotations recorded by the second target sensor, and r is the preset number of rotations.

[0040] As an improvement, the steps of screening the sensor data to be screened and correcting the abnormal data found include:

[0041] For a specific sensor with filtering capabilities, if the sensor data value at a certain time point is greater than the angular velocity threshold and the standard deviation of the angular velocity is greater than the standard deviation threshold, the sensor data at that time point is determined to be abnormal data; the standard deviation of the angular velocity is calculated using the formula:

[0042]

[0043] Calculate the standard deviation of angular velocity; where σ ω Let ωi be the standard deviation of angular velocity, and ωi be the i-th angular velocity value. Let N be the average angular velocity of all sensor data, and N be the number of sensor data.

[0044] Delete the abnormal data and fill the empty spaces left by the deleted abnormal data with the average of the two normal data before and after the abnormal data.

[0045] The present invention also provides a quantitative analysis system for running wheel motion, comprising:

[0046] The target sensor screening module is used to collect data transmitted back by the sensor after the sensor to be screened is installed on the sensor mounting seat of the mouse running wheel and the mouse running wheel is rotated a preset number of times.

[0047] Using the shortest sensor data as the baseline, other sensor data to be screened are cropped.

[0048] All sensor data to be screened are screened, and abnormal data found during the screening are corrected.

[0049] Calculate the number of mouse wheel rotations recorded by each sensor to be screened based on the data from each sensor to be screened;

[0050] Compare the number of mouse wheel rotations recorded by each sensor to be screened with the preset number of rotations, and select one or two angular velocity sensors corresponding to the sensor data with the smallest difference from the preset number of rotations as the target sensors;

[0051] The measurement module is used to detect the rotation of the mouse running wheel driven by the mouse's running motion after the target sensor is installed on the mouse running wheel, and to collect the data transmitted back by the target sensor.

[0052] When there is only one target sensor, the number of rotations of the mouse running wheel is calculated based on the data returned by the target sensor;

[0053] When there are two target sensors, the data returned by the two target sensors are compared. If the difference is less than a threshold, the number of rotations of the mouse running wheel is calculated using the data returned by the two target sensors; otherwise, the data returned by the two target sensors is considered invalid.

[0054] Beneficial effects: The mouse running wheel with the above structure uses a short shaft to connect the running wheel body to the support, replacing the traditional long shaft design. This provides ample space inside the running wheel, avoiding obstruction of the mouse's movement and allowing it to move freely, more realistically showcasing its movement. Furthermore, it avoids errors caused by the mouse hanging on the long shaft during movement, or by the cage rotating due to inertia or the mouse's own movement.

[0055] Connecting holes are made on the axles at both ends of the running wheel body to precisely match the short shaft, ensuring even force distribution during rotation and stable operation. The other end of the short shaft is embedded in the U-shaped groove of the bracket. The U-shaped groove provides support and rotation space for the short shaft while restricting its radial movement, making the running wheel rotate flexibly and stably, and also facilitating disassembly, maintenance and replacement of parts.

[0056] The short shaft is made of engineering plastic, which has the characteristics of high strength, rigidity, wear resistance and self-lubrication. It can withstand various forces when the running wheel is running, extend its service life, reduce the coefficient of friction, reduce the weight of the running wheel, facilitate the movement of mice and reduce noise interference.

[0057] A limiting ring is provided on the short shaft. The two limiting rings of the short shaft clamp and fix the running wheel body, accurately limiting the axial position, preventing axial movement, ensuring operational accuracy, enhancing structural safety, and preventing the running wheel from falling off and injuring mice or damaging the equipment.

[0058] The sensor mounting bases are located at the ends of the short shafts, with two in total and located on the two short shafts respectively. This allows for the selection of one or two angular velocity sensors according to experimental needs, reducing data transmission interference errors and accurately acquiring real-time motion data of the running wheel.

[0059] The quantitative analysis method for running wheel motion with the above steps first involves installing and collecting data from the sensors to be screened, then cropping other data based on the shortest data to unify the data volume, then screening and correcting abnormal data, calculating the number of running wheel rotations recorded by each sensor based on the processed data, and finally comparing and selecting one or two sensors with the smallest difference from the preset number of rotations as the target sensors. This multi-step process ensures the accuracy and reliability of the selected sensors.

[0060] After installing the target sensors, different calculation strategies are adopted depending on the number of sensors. When there is a single target sensor, the number of revolutions is directly calculated based on its data, which is simple to operate. When there are two target sensors, the data of the two sensors are compared. If the difference is less than a threshold, the number of revolutions is calculated using a weighted average method, which combines the advantages of both methods. If the difference is large, the data is deemed invalid to avoid interference from erroneous data and to ensure that the measurement results accurately reflect the actual rotation of the running wheel to the greatest extent possible. Attached Figure Description

[0061] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. The elements or parts in the drawings are not necessarily drawn to scale. Obviously, the drawings described below are some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0062] Figure 1 This is a schematic diagram of the structure of Embodiment 1 of the present invention;

[0063] Figure 2 This is an exploded view of Embodiment 1 of the present invention;

[0064] Figure 3 This is an enlarged view of the short axis in Embodiment 1 of the present invention;

[0065] Figure 4 This is an enlarged view of the connection between the short shaft and the running wheel body in Embodiment 1 of the present invention;

[0066] Figure 5 This is a flowchart of Embodiment 2 of the present invention.

[0067] Summary of attached labeling and identification:

[0068] 1. Running wheel body, 2. Bracket, 3. Short shaft, 4. Sensor mounting base, 5. Limiting ring, 6. U-shaped groove. Detailed Implementation

[0069] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0070] In this document, suffixes such as "module," "part," or "unit" used to denote elements are used only for the purpose of illustrative purposes and have no specific meaning in themselves. Therefore, "module," "part," or "unit" may be used interchangeably.

[0071] In this document, the terms "upper," "lower," "inner," "outer," "front," "rear," "one end," and "the other end," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the present invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the present invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0072] In this document, unless otherwise explicitly specified and limited, the terms "installed," "equipped with," "connected," etc., should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection, a direct connection, or an indirect connection through an intermediate medium; it can be a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0073] In this document, "and / or" includes any and all combinations of one or more of the listed related items.

[0074] In this article, "multiple" means two or more, that is, it includes two, three, four, five, etc.

[0075] Example 1: As Figure 1 , Figure 2 As shown, this embodiment provides a mouse running wheel, including a running wheel body 1 for accommodating a mouse for running and a support 2 for supporting the running wheel body 1; the axles at both ends of the running wheel body 1 are respectively connected to the support 2 via short shafts 3, so that the running wheel body 1 can rotate on the support 2 along its own axis; it also includes a sensor mounting base 4 disposed on the short shaft 3, the sensor mounting base 4 being used to mount an angular velocity sensor to sense the rotation of the short shaft 3.

[0076] In traditional long-axis designs, the long axis occupies a certain amount of space inside the running wheel, potentially hindering the mouse's movement. Furthermore, the mouse may become suspended on the long axis during movement, interfering with the collection of motion data. In this embodiment, the axles at both ends of the running wheel are connected to the support 2 via short shafts 3. This connection method allows the running wheel body 1 to rotate along its own axis on the support 2. The design supported by the short shafts 3 avoids this problem, ensuring that the mouse moves freely without obstruction, thus more accurately reflecting the mouse's movement status and facilitating the acquisition of more accurate experimental data. Moreover, it essentially avoids the situation where the mouse becomes suspended on the long axis.

[0077] like Figure 3As shown, more specifically, the running wheel body 1 has connecting holes on the shafts at both ends. One end of the short shaft 3 is fixed to the running wheel body 1 through the connecting hole, and the other end is embedded in the U-shaped groove 6 on the bracket 2 and can rotate in the U-shaped groove 6 to drive the running wheel body 1 to rotate.

[0078] Connecting holes are provided at both ends of the running wheel body 1, providing a specific interface for the connection between the short shaft 3 and the running wheel body 1. The short shaft 3 is fixed by the connecting holes, ensuring that the short shaft 3 and the axis of the running wheel body 1 are aligned, so that the running wheel is subjected to uniform force when rotating, and the running wheel is stable when the mouse moves.

[0079] The connection hole between the short shaft 3 and the running wheel body 1 can be an interference fit, that is, the outer diameter of the short shaft 3 is slightly larger than the inner diameter of the connection hole. During installation, a certain pressure needs to be applied to press the short shaft 3 into the connection hole, and the friction between the two is used to prevent the short shaft 3 from loosening. Alternatively, a key connection can be used, with keyways machined on the short shaft 3 and the connection hole, and a key installed to transmit torque, so that the short shaft 3 drives the running wheel body 1 to rotate synchronously, effectively avoiding relative slippage.

[0080] The other end of the short shaft 3 is embedded in the U-shaped groove 6 on the bracket 2. The U-shaped groove 6 provides support and rotation space for the short shaft 3. The short shaft 3 can rotate freely within the U-shaped groove 6, and the shape and size of the U-shaped groove 6 can restrict the radial movement of the short shaft 3, ensuring that the short shaft 3 always rotates within a specified range. When the mouse moves on the running wheel body 1, the force generated causes the running wheel body 1 to rotate, which drives the short shaft 3 fixed to it to rotate within the U-shaped groove 6. The design of the U-shaped groove 6 ensures both flexible rotation of the running wheel and maintains its stability.

[0081] The above assembly method facilitates the disassembly and replacement of the running wheel body 1 or the short shaft 3, and allows for quick repair and replacement if any parts are damaged. It also ensures that the running wheel rotates smoothly during mouse movement, reducing friction and energy loss, providing a smooth exercise experience for the mouse, and is conducive to the accurate collection of motion data by the sensor to meet the needs of experimental research.

[0082] like Figure 4 As shown, in some embodiments, the short shaft 3 is made of engineering plastic and is provided with a limiting ring 5, and the limiting rings 5 ​​on the two short shafts 3 clamp and fix the running wheel body 1.

[0083] Engineering plastics possess high strength and rigidity, capable of withstanding various forces generated during the operation of the running wheel, including the forces exerted on the wheel by the mouse during movement and the centrifugal force generated by the wheel's own rotation. This ensures that the short shaft 3 will not easily deform or be damaged during long-term use, maintaining the stability of the running wheel structure. Secondly, engineering plastics have good wear resistance. Friction occurs at the contact points between the short shaft 3 and the running wheel body 1 and the support 2. This wear-resistant property extends the service life of the short shaft 3, reducing the decrease in precision and the frequency of component replacement due to wear. Furthermore, engineering plastics also have good self-lubricating properties, which can reduce the coefficient of friction when the short shaft 3 rotates, making the running wheel rotate more smoothly and reducing energy loss. This not only helps the mouse move more easily but also reduces noise generated by friction, avoiding interference with the experimental environment. In addition, engineering plastics are lighter than materials such as metals, helping to reduce the overall weight of the running wheel, facilitating movement and installation. It also reduces the inertia that the mouse needs to overcome during movement, making the mouse's movement more natural.

[0084] A limiting ring 5 is installed on the short shaft 3, and the limiting rings 5 ​​on the two short shafts 3 clamp and fix the running wheel body 1. The limiting ring 5 can precisely limit the axial position of the running wheel body 1 on the short shaft 3, preventing axial movement of the running wheel during rotation. If the running wheel moves axially, it will cause uneven friction between the running wheel and the support 2, accelerate component wear, and may also affect the mouse's exercise experience, or even lead to inaccurate experimental data. By clamping and fixing it with the limiting ring 5, the running wheel can be ensured to rotate in a stable position at all times, improving the running wheel's operational accuracy. From a safety perspective, the limiting ring 5 can enhance the safety of the running wheel structure, preventing the running wheel from falling off the short shaft 3 when rotating at high speed or subjected to external impact, avoiding injury to the mice or damage to the experimental equipment.

[0085] In addition, the sensor mounting base 4 is located at the end of the short shaft 3, and there are two sensor mounting bases 4 respectively located on the two short shafts 3, which makes it convenient for researchers to selectively install one or two angular velocity sensors for experiments.

[0086] Because the short shaft 3 rotates synchronously with the running wheel body 1, the sensor installed at the end of the short shaft 3 can accurately acquire real-time motion data of the running wheel, such as angular velocity. Compared to installation in other positions, this position can minimize interference and errors during data transmission, ensuring that the data collected by the sensor truly reflects the motion state of the running wheel, and providing an accurate basis for subsequent experimental analysis.

[0087] In some experimental scenarios, researchers only need to obtain basic motion data of the running wheel, in which case they can choose to install one angular velocity sensor on one of the mounting bases 4. However, in experiments requiring higher accuracy and reliability of the experimental data, researchers can choose to install one angular velocity sensor on each of the two mounting bases 4. This dual-sensor configuration allows for cross-verification of the collected data, effectively avoiding data deviations caused by environmental interference or the malfunction of a single sensor. For example, if some accidental electromagnetic interference in the environment affects one sensor, the data from the other sensor can serve as a reference, ensuring the reliability of the experimental data.

[0088] Example 2: Figure 5 As shown, this embodiment provides a quantitative analysis method for running wheel motion, applied to the mouse running wheel described in Embodiment 1. The specific steps include:

[0089] S1 Target Sensor Screening Steps:

[0090] S11 installs the sensor to be screened on the sensor mounting base of the mouse running wheel, rotates the mouse running wheel according to a preset number of revolutions, and collects the data returned by the sensor to be screened.

[0091] This step simulates the motion of the running wheel in a real-world usage scenario. By collecting data from the sensors to be screened, it provides the initial data foundation for subsequent screening. The preset number of laps is set to ensure that each sensor is tested under the same motion conditions, making the collected data comparable.

[0092] S12 uses the shortest sensor data to be screened as the baseline data and then trims the other sensor data to be screened.

[0093] In actual data acquisition, due to various factors, the data collected by different sensors may have inconsistent lengths. Directly analyzing data of these varying lengths can lead to biased results. By cropping the data, the length of all sensors is made uniform, standardizing the data specifications and facilitating fair comparison and analysis later.

[0094] Preferably, if the difference in data length (i.e., data volume) between the various sensors is less than 0.002%, the data from the sensor with the shortest data length is used as the baseline data. Later data points from the other sensors are then truncated to align with the data volume of the sensor with the shortest data length. For example, suppose in a mouse wheel-running experiment, there are three sensors A, B, and C to be screened, collecting 30005, 30006, and 30004 data points respectively. Here, the data from sensor C, which has the smallest data volume, is used as the baseline data. The last data point collected from sensor A is truncated, and similarly, the last two data points collected from sensor B are truncated.

[0095] Furthermore, if the difference in data length (i.e., data volume) between various sensors is greater than 0.002%, the data of the sensor with the shortest data length to be screened is used as the benchmark data, and a random pruning mechanism is used to prune the data of other sensors.

[0096] More specifically, the steps for cropping the sensor data to be screened include:

[0097] S121 calculates the difference in the number of sensor data to be screened compared with the baseline data.

[0098] In a mouse wheel-running experiment, there are three sensors, A, B, and C, to be screened, with 30,005, 30,008, and 29,900 data points collected, respectively. Here, the data from sensor C, which has the fewest data points, is used as the baseline data.

[0099] The difference between the data from sensor A and the reference data is: 30005 - 29990 = 15.

[0100] The difference between the data from sensor B and the reference data is: 30008 - 29990 = 18.

[0101] S122 divides the sensor data to be screened into corresponding segments based on the difference in quantity.

[0102] For sensor A, its 30005 data points are divided into 15 segments, each segment containing approximately 30005 ÷ 15 ≈ 2000.33 data points (in actual operation, the data can be flexibly processed according to its characteristics; for ease of understanding, it is rounded up to approximately 2000 data points per segment, and the remaining data can be randomly distributed among the segments, the same applies below).

[0103] For sensor B, its 30008 data points are divided into 18 segments, with each segment containing approximately 30008 ÷ 18 ≈ 1667.11 data points (rounded down to 1667 data points per segment).

[0104] S123 randomly deletes one data point from each data segment.

[0105] In each segment of 200 data points from sensor A, one data point is randomly selected and deleted. For example, the 50th data point is randomly deleted from the first segment, the 120th data point is randomly deleted from the second segment, and so on, until 15 data points are deleted, bringing the total number of data points in sensor A to 2990.

[0106] In each segment of 167 data points from sensor B, one data point is randomly deleted. For example, the 80th data point is randomly deleted from the first segment, the 35th data point is randomly deleted from the second segment, and so on, for a total of 18 data points are deleted, bringing the total number of data points in sensor B to 2990.

[0107] Through this cropping operation, the data volume of sensors A, B, and C was unified to 29,900 records, providing a unified data foundation for subsequent data analysis and ensuring the accuracy and consistency of experimental data processing.

[0108] S13 screens all sensor data to be selected and corrects any abnormal data found during the screening.

[0109] Outlier data may be caused by environmental interference (such as electromagnetic interference, vibration, etc.), sensor malfunction, or abnormal mouse movement. Without intervention, this outlier data can severely impact the accuracy and reliability of the data. Identifying outlier data using specific algorithms or rules, and then correcting it using appropriate methods (such as replacing outliers with the mean of preceding and following normal data), can improve data quality and make subsequent calculations and analyses more reliable.

[0110] More specifically, the steps for screening the sensor data to be selected and correcting any abnormal data found include:

[0111] S131 For a certain sensor with filtering capabilities, if the sensor data value at a certain time point is greater than the angular velocity threshold and the angular velocity standard deviation is greater than the standard deviation threshold, the sensor data at that time point is determined to be abnormal data; the angular velocity standard deviation is calculated using the formula:

[0112]

[0113] Calculate the standard deviation of angular velocity; where σ ω Let ωi be the standard deviation of angular velocity, and ωi be the i-th angular velocity value. Let N be the mean angular velocity of all sensor data, and N be the number of sensor data points. The standard deviation of angular velocity is an indicator of the dispersion of angular velocity data over a period of time; it reflects the fluctuation of angular velocity. By calculating the standard deviation of angular velocity, we can understand the stability of the sensor data.

[0114] This step involves setting angular velocity thresholds and angular velocity standard deviation thresholds to evaluate the data from a sensor under consideration at each time point. Only when the sensor data value exceeds both the angular velocity threshold and the angular velocity standard deviation exceeds the standard deviation threshold is the sensor data at that time point considered abnormal. This dual-criteria approach is more rigorous and scientific than a single standard, enabling more accurate identification of truly abnormal data.

[0115] S132 deletes abnormal data and fills the empty spaces in the deleted abnormal data by using the average of the two normal data before and after the abnormal data.

[0116] Once data from a specific point in time is identified as outlier, it should be deleted immediately. Outlier data can severely interfere with subsequent data analysis and results; retaining these outliers may lead to erroneous conclusions. For example, when calculating metrics such as the average angular velocity and total number of rotations on a mouse running wheel, outlier data can skew these metrics, failing to accurately reflect the mouse's movement.

[0117] After deleting outlier data, gaps are left in the data sequence. To ensure data continuity and integrity, these gaps are filled using the average of the two normal data points preceding and following the outlier. This filling method is based on the assumption of data continuity, meaning that data changes are relatively stable over a short period. For example, if data at a certain time point is identified as outlier and deleted, the gap at that time point is filled with the average of the normal data at that time point. This restores data continuity to some extent without introducing excessive errors, allowing subsequent data analysis to be based on relatively reasonable data.

[0118] S14 calculates the number of mouse wheel rotations recorded by each sensor to be screened based on the data from each sensor to be screened.

[0119] In this embodiment, the formula is used:

[0120]

[0121] Calculate the number of rotations of the mouse running wheel; where R n Let ωi be the number of rotations of the mouse running wheel recorded by the sensor numbered n, ωi be the angular velocity value of the i-th sensor, and N be the number of sensor data.

[0122] In the mouse wheel experiment, the speed and direction of the mouse's movement within the wheel vary randomly. Simply summing the angular velocity data will cause the positive angular velocity generated by the mouse's forward movement and the negative angular velocity generated by its reverse movement to cancel each other out. For example, if a mouse first rotates the wheel clockwise to generate a positive angular velocity and then rotates counterclockwise to generate a negative angular velocity, directly summing these two angular velocities will weaken each other, resulting in a calculated number of rotations that is less than the actual number of wheel rotations, thus failing to accurately reflect the mouse's movement.

[0123] By accumulating the absolute values ​​of angular velocities at individual time points, the cancellation of positive and negative motion can be effectively avoided. Regardless of whether the mouse rotates the wheel in either the forward or reverse direction, the absolute value of its angular velocity is included in the calculation, making the calculated number of rotations more consistent with the actual wheel rotation. This method allows for accurate measurement of the total amount of movement the mouse makes on the wheel, providing reliable data support for subsequent experimental analysis. For example, in experiments studying mouse exercise endurance and exercise habits, the accurate number of wheel rotations is a crucial indicator for assessing the mouse's movement; using the absolute value accumulation method ensures the accuracy and scientific validity of these research results.

[0124] S15 compares the number of mouse wheel rotations recorded by each sensor to be screened with the preset number of rotations, and selects one or two angular velocity sensors corresponding to the sensor data with the smallest difference from the preset number of rotations as the target sensors.

[0125] The number of wheel rotations recorded by each sensor to be screened is compared with a preset number of rotations. One or two angular velocity sensors with the smallest difference from the preset number are selected as target sensors. The purpose of this is to select sensors whose measurement results are closest to reality in this screening test. These sensors can provide more reliable data in subsequent measurement steps. Choosing one or two sensors is to meet different experimental needs and scenarios. For example, a single sensor is suitable for situations with relatively low requirements for data accuracy or simple experimental conditions, while dual sensors can corroborate data, improving data reliability and are suitable for experiments with high data accuracy requirements.

[0126] S2 Measurement Steps:

[0127] S21 mounts a target sensor on the mouse running wheel, uses the target sensor to detect the rotation of the mouse running wheel driven by the mouse's running motion, and collects the data transmitted back by the target sensor.

[0128] Since up to two angular velocity sensors can be installed on the mouse running wheel, the number of angular velocity sensors can be selected according to needs.

[0129] S22 calculates the number of rotations of the mouse running wheel based on the data returned by the target sensor when there is only one target sensor.

[0130] When there is only one target sensor, the number of rotations of the mouse running wheel is calculated based on the data returned by that sensor using the method described in step S14. This approach is suitable for experiments where cost and ease of operation are critical, allowing for data acquisition and analysis through a single, reliable sensor.

[0131] S23 If there are two target sensors, compare the data returned by the two target sensors. If the difference is less than a threshold, calculate the number of rotations of the mouse running wheel using the data returned by the two target sensors; otherwise, determine that the data returned by the two target sensors is invalid.

[0132] When there are two target sensors, compare the data returned by the two sensors. If the difference between the two sensor data is less than a preset threshold, it indicates that the measurement results of the two sensors are relatively consistent and the data is reliable. In this case, the number of rotations of the mouse running wheel can be calculated using the data returned by the two sensors. Weighted averaging or other suitable calculation methods can be used to combine the data from the two sensors to further improve the accuracy of the measurement results. If the difference is greater than the threshold, the data returned by the two target sensors is considered invalid. This may indicate a large interference factor or a sensor malfunction, requiring re-inspection of the experimental equipment or replacement of the sensors to ensure the reliability of the experimental data.

[0133] More specifically, when there are two target sensors, the formula is used.

[0134] R = a * R₁² + b * R₂²

[0135] The number of rotations of the mouse running wheel is calculated; where R is the number of rotations, R12 is the number of rotations of the mouse running wheel recorded by the first target sensor in the measurement step, R22 is the number of rotations of the mouse running wheel recorded by the second target sensor in the measurement step, a is the weight of the first target sensor, b is the weight of the second target sensor, and a+b=1.

[0136] The above formula is based on the principle of weighted averaging. By assigning different weights to the two sensors, it combines the number of mouse wheel rotations recorded by the two sensors to obtain a more accurate and reliable result. This is because in actual measurements, the two sensors may be subject to varying degrees of interference, resulting in some differences in their measurement results. Simply averaging may not accurately reflect the actual number of wheel rotations. Weighted averaging, on the other hand, can reasonably allocate weights to the measurement results based on the sensor's performance in the screening process, making the final result closer to the true value.

[0137] Furthermore, using the formula:

[0138]

[0139] Calculate the weight of the first target sensor; where a is the weight of the first target sensor, R11 is the number of mouse wheel rotations recorded by the first target sensor in the target sensor screening step, R21 is the number of mouse wheel rotations recorded by the second target sensor in the target sensor screening step, and r is the preset number of rotations;

[0140] Using the formula:

[0141]

[0142] Calculate the weight of the second target sensor; where b is the weight of the second target sensor, R11 is the number of mouse wheel rotations recorded by the first target sensor, R21 is the number of mouse wheel rotations recorded by the second target sensor, and r is the preset number of rotations.

[0143] By comparing the deviations of the number of rotations recorded by two sensors during the screening step with a preset number of rotations, the smaller the deviation, the closer the sensor's measurement result is to the actual situation during screening, and the higher its weight should be in the overall calculation. Specifically, the absolute value of the deviation of a sensor from the preset number of rotations is used as the numerator, and the sum of the absolute values ​​of the deviations of the two sensors from the preset number of rotations is used as the denominator. The resulting ratio is the weight of that sensor. For example, if the first target sensor records a smaller deviation from the preset number of rotations during screening, while the second target sensor has a larger deviation, then the value calculated according to the formula will be larger, meaning that the number of rotations recorded by the first target sensor contributes more to the final result in the overall calculation.

[0144] Implementation 3: This embodiment provides a quantitative analysis system for running wheel motion, including:

[0145] The target sensor screening module is used to collect data transmitted back by the sensor after the sensor to be screened is installed on the sensor mounting seat of the mouse running wheel and the mouse running wheel is rotated a preset number of times.

[0146] Using the shortest sensor data as the baseline, other sensor data to be screened are cropped.

[0147] All sensor data to be screened are screened, and abnormal data found during the screening are corrected.

[0148] Calculate the number of mouse wheel rotations recorded by each sensor to be screened based on the data from each sensor to be screened;

[0149] Compare the number of mouse wheel rotations recorded by each sensor to be screened with the preset number of rotations, and select one or two angular velocity sensors corresponding to the sensor data with the smallest difference from the preset number of rotations as the target sensors;

[0150] The measurement module is used to detect the rotation of the mouse running wheel driven by the mouse's running motion after the target sensor is installed on the mouse running wheel, and to collect the data transmitted back by the target sensor.

[0151] When there is only one target sensor, the number of rotations of the mouse running wheel is calculated based on the data returned by the target sensor;

[0152] When there are two target sensors, the data returned by the two target sensors are compared. If the difference is less than a threshold, the number of rotations of the mouse running wheel is calculated using the data returned by the two target sensors; otherwise, the data returned by the two target sensors is considered invalid.

[0153] 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 apparatus 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 apparatus. 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 apparatus that includes that element.

[0154] 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) and includes several instructions to cause a computer terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0155] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A quantitative analysis method for running wheel motion, applied to mouse running wheels, characterized in that: The mouse running wheel includes a running wheel body for accommodating mice for running and a support for supporting the running wheel body; the axles at both ends of the running wheel body are connected to the support via short axles, so that the running wheel body can rotate on the support along its own axis; it also includes a sensor mounting base disposed on the short axle, the sensor mounting base being used to mount an angular velocity sensor to sense the rotation of the short axle; The quantitative analysis method for running wheel motion includes: Target sensor screening steps: Install the sensor to be screened on the sensor mounting base of the mouse running wheel, rotate the mouse running wheel according to the preset number of revolutions, and collect the data returned by the sensor to be screened; Using the shortest sensor data as the baseline, other sensor data to be screened are cropped. All sensor data to be screened are screened, and abnormal data found during the screening are corrected. Calculate the number of mouse wheel rotations recorded by each sensor to be screened based on the data from each sensor to be screened; Compare the number of mouse wheel rotations recorded by each sensor to be screened with the preset number of rotations, and select one or two angular velocity sensors corresponding to the sensor data with the smallest difference from the preset number of rotations as target sensors; Measurement steps: The target sensor was installed on the mouse running wheel. The target sensor was used to detect the rotation of the mouse running wheel driven by the mouse's running motion and to collect the data transmitted back by the target sensor. When there is only one target sensor, the number of rotations of the mouse running wheel is calculated based on the data returned by the target sensor; When there are two target sensors, the data returned by the two target sensors are compared. If the difference is less than a threshold, the number of rotations of the mouse running wheel is calculated using the data returned by the two target sensors; otherwise, the data returned by the two target sensors is considered invalid.

2. The method for quantitative analysis of running wheel motion according to claim 1, characterized in that: The running wheel body has connecting holes on the shafts at both ends. One end of the short shaft is fixed to the running wheel body through the connecting hole, and the other end is embedded in the U-shaped groove on the bracket and can rotate in the U-shaped groove to drive the running wheel body to rotate.

3. The method for quantitative analysis of running wheel motion according to claim 2, characterized in that: The short shaft is made of engineering plastic and is equipped with a limit ring. The limit rings on the two short shafts clamp and fix the running wheel body.

4. The method for quantitative analysis of running wheel motion according to claim 1, characterized in that: The sensor mounting base is located at the end of the short shaft, and there are two sensor mounting bases located on the two short shafts respectively.

5. The method for quantitative analysis of running wheel motion according to claim 1, characterized in that: The steps for cropping the sensor data to be screened include: calculating the difference in the number of sensor data to be screened compared with the baseline data; The sensor data to be screened is divided into corresponding segments based on the difference in quantity. One data point is randomly deleted from each data segment.

6. The method for quantitative analysis of running wheel motion according to claim 1, characterized in that: The method for calculating the number of rotations of the mouse running wheel based on sensor data is as follows: Calculate the number of rotations of the mouse running wheel; where Rn is the number of rotations of the mouse running wheel recorded by sensor number n, ωi is the angular velocity value of the i-th time, and N is the number of sensor data.

7. The method for quantitative analysis of running wheel motion according to claim 1, characterized in that: When there are two target sensors, the formula R = a*R1² + b*R2² is used. To calculate the number of rotations of the mouse running wheel; where R is the number of rotations, R12 is the number of rotations of the mouse running wheel recorded by the first target sensor in the measurement step, R22 is the number of rotations of the mouse running wheel recorded by the second target sensor in the measurement step, a is the weight of the first target sensor, b is the weight of the second target sensor, and a+b=1; Using the formula: Calculate the weight of the first target sensor; where a is the weight of the first target sensor, R11 is the number of mouse wheel rotations recorded by the first target sensor in the target sensor screening step, R21 is the number of mouse wheel rotations recorded by the second target sensor in the target sensor screening step, and r is the preset number of rotations; Using the formula: Calculate the weight of the second target sensor; where b is the weight of the second target sensor, R11 is the number of mouse wheel rotations recorded by the first target sensor, R21 is the number of mouse wheel rotations recorded by the second target sensor, and r is the preset number of rotations.

8. The method for quantitative analysis of running wheel motion according to claim 1, characterized in that: The steps of screening sensor data and correcting abnormal data include: for a given sensor, if the sensor data value at a certain time point is greater than the angular velocity threshold and the standard deviation of the angular velocity is greater than the standard deviation threshold, the sensor data at that time point is determined to be abnormal data; the standard deviation of the angular velocity is calculated using the formula: Calculate the standard deviation of angular velocity; where σ ω Let ωi be the standard deviation of angular velocity, and ωi be the i-th angular velocity value. Let N be the average angular velocity of all sensor data, and N be the number of sensor data. Delete the abnormal data and fill the empty spaces left by the deleted abnormal data with the average of the two normal data before and after the abnormal data.

9. A quantitative analysis system for running wheel motion, characterized in that: include: The target sensor screening module is used to collect data transmitted back by the sensor after the sensor to be screened is installed on the sensor mounting seat of the mouse running wheel and the mouse running wheel is rotated a preset number of times. Using the shortest sensor data as the baseline, other sensor data to be screened are cropped. All sensor data to be screened are screened, and abnormal data found during the screening are corrected. Calculate the number of mouse wheel rotations recorded by each sensor to be screened based on the data from each sensor to be screened; Compare the number of mouse wheel rotations recorded by each sensor to be screened with the preset number of rotations, and select one or two angular velocity sensors corresponding to the sensor data with the smallest difference from the preset number of rotations as target sensors; The measurement module is used to detect the rotation of the mouse running wheel driven by the mouse's running motion after the target sensor is installed on the mouse running wheel, and to collect the data transmitted back by the target sensor. When there is only one target sensor, the number of rotations of the mouse running wheel is calculated based on the data returned by the target sensor; When there are two target sensors, the data returned by the two target sensors are compared. If the difference is less than a threshold, the number of rotations of the mouse running wheel is calculated using the data returned by the two target sensors. Otherwise, the data returned by the two target sensors will be deemed invalid.

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