Treadmill state monitoring method and system and treadmill

Abstract

The invention relates to the technical field of treadmills, and discloses a treadmill state monitoring method and system and a treadmill. The method comprises the following steps: S1, installing a current monitoring module in a circuit of the treadmill, and collecting current data when the treadmill works in real time; s2, analyzing the collected current data, and judging whether the user is in a running state or not; s3, when it is detected that the user stops moving or leaves the running area, data recording is automatically paused; and S4, analyzing the running state of the treadmill according to the current data, and identifying whether current fluctuation abnormity exists or not. By adopting the technical scheme of current monitoring and real-time analysis, the working state of the treadmill is judged in combination with multi-dimensional data, and the technical effect of accurately recognizing the motion state of a user and the health condition of equipment is achieved. Compared with a scheme depending on a simple current value or a single sensor in the prior art, the abnormal fluctuation can be identified more accurately in a complex environment, and potential safety hazards caused by misjudgment or missed judgment are avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of treadmills, and in particular to a treadmill state monitoring method, system and treadmill. Background Art

[0002] In the fitness equipment industry, especially treadmills, real-time monitoring of user usage and equipment health is key to improving user experience and equipment management efficiency. Existing technologies use sensors to monitor basic treadmill operating parameters, such as speed, time, and distance, providing users with basic exercise data. Current treadmills are typically equipped with basic heart rate monitoring and exercise data recording capabilities, but these features are limited to physiological indicators and basic exercise volume statistics during exercise.

[0003] In existing technology, most treadmills monitor their operating status through traditional current monitoring or mechanical sensors. However, these technologies often have significant limitations, particularly in determining abnormal current fluctuations and detecting falls, and often struggle to provide sufficiently accurate and real-time feedback.

[0004] First, existing technologies typically rely on a single current monitoring sensor to determine the operating status of a treadmill. This approach often only provides rough current data and is difficult to handle in complex usage scenarios, especially after prolonged treadmill use. Current fluctuations can be caused by multiple factors, such as user weight and cadence. Therefore, simply relying on current monitoring to determine whether abnormal fluctuations have occurred is prone to misjudgment or omission, affecting device safety and user experience.

[0005] Secondly, existing technologies have limited ability to detect user falls. Traditional treadmills rely on speed or acceleration sensors to monitor the user's motion. However, these solutions have long response times and fail to accurately determine whether a user has fallen based on current changes. Consequently, when a user falls, the system may not immediately shut down, leading to potential safety risks not being promptly addressed. Summary of the Invention

[0006] In response to the deficiencies in the prior art, the present invention provides a treadmill status monitoring method, system, and treadmill, which solve the problems of existing treadmills in untimely fall detection, inaccurate status identification, and inability to effectively record event data.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a treadmill status monitoring method, system and treadmill, comprising the following steps; S1. Install a current monitoring module in the circuit of the treadmill to collect current data in real time when the treadmill is working; S2. Analyze the collected current data to determine whether the user is in a running state; S3: Automatically pause data recording when detecting that the user stops exercising or leaves the running area; S4. Analyze the operating status of the treadmill based on the current data to identify whether there is abnormal current fluctuation; S5. When the current fluctuation is abnormal, the equipment maintenance warning is triggered, prompting equipment inspection and maintenance; S6. Detecting a user fall event by analyzing the current change trend. If a fall event is detected, an emergency shutdown mechanism is immediately triggered; S7. When a fall event occurs, the detailed information of the fall event is recorded and saved to local storage, and uploaded to the cloud when connected to the network.

[0008] Preferably, the current monitoring module in step S1 is installed in the main control circuit of the treadmill, the sampling frequency is once per second, the sampling accuracy is not less than 16 bits, and the current monitoring module is a Hall current sensor.

[0009] Preferably, in step S2, determining whether the user is in a running state is achieved by analyzing the current change rate and comparing it with a set threshold value. If the current change rate exceeds the set threshold value, it is determined that the user is in a running state. The current change rate calculation formula is as follows: ΔI(t)=I(t)-I(t-1); Where ΔI(t) represents the rate of change of current at time t, I(t) represents the current value at the current time t, I(t-1) represents the current value at the previous time t-1, t represents the current discrete time point, and Δ represents the sign of the change.

[0010] Preferably, the identification of abnormal current fluctuation in step S4 is performed by calculating the deviation between the current value and the historical current average value. If the deviation exceeds a preset threshold, it is determined to be abnormal current fluctuation. The calculation formula for abnormal current fluctuation is as follows: ΔI threshold =|I(t)-μ I |; Among them, ΔI threshold represents the threshold value of current change, I(t) represents the current value at the current moment t, μ I represents the mean value of historical current data, and t represents the current discrete time point.

[0011] Preferably, the equipment maintenance warning in step S5 is triggered based on the comparison of current fluctuation and equipment health assessment model. If the current fluctuation exceeds a preset range, the system automatically triggers the equipment maintenance warning and prompts the user to perform inspection and maintenance.

[0012] Preferably, the current change trend of the user fall event detected in step S6 includes detecting a sharp change in current. If the current change amplitude exceeds a preset threshold and the current value is lower than the set minimum current value for a long time, it is determined to be a fall event. The current change amplitude calculation formula for the fall event determination is as follows: ΔI fall =|I(t)-I(t-1)|; Among them, ΔI fall represents the amount of current drop, I(t) represents the current value at the current moment t, I(t-1) represents the current value at the previous moment t-1, and t represents the current discrete time point.

[0013] Preferably, when recording a fall event in step S7, the recorded content includes the current change rate, the timestamp of the fall, and the current fluctuation information before and after the fall. This information is stored in a local storage module and uploaded to the cloud for remote analysis after the device is connected to the network.

[0014] Preferably, the cloud uploading step in step S7 includes uploading detailed records of the fall event to the cloud database, the cloud system detects the health status of the equipment through remote analysis and can remotely intervene in the treadmill, and the cloud data uploading and processing uploads the event data to the cloud server through a network connection to perform health status assessment and trigger remote intervention.

[0015] Treadmill status monitoring system, including; Current monitoring module, used to collect current data of treadmill motor in real time; a data processing module, which is in communication with the current monitoring module and is used to calculate the current change rate and determine whether the treadmill is in motion; A maintenance warning module, which is in communication with the data processing module and is used to generate an equipment health assessment model based on the current fluctuation data and trigger a maintenance warning when the current fluctuation exceeds a preset range; A fall detection module, which is in communication with the data processing module and is used to analyze the current change trend to identify a fall event and trigger an emergency shutdown mechanism when a fall event is detected; A local storage module, which is connected to the fall detection module and the maintenance warning module, and is used to record and store detailed information of fall events; The cloud upload module is connected to the local storage module and is used to upload fall event records to the cloud for remote analysis.

[0016] Treadmill, including; Electric drive unit; Main control processor; Display unit; control systems; When the control system is running, the treadmill state monitoring method is implemented.

[0017] The present invention provides a treadmill status monitoring method, system, and treadmill, which have the following beneficial effects: 1. This invention utilizes current monitoring and real-time analysis, combining multi-dimensional data to determine the treadmill's operating status, achieving the technical effect of accurately identifying the user's exercise status and the device's health. Compared to existing solutions that rely on simple current values ​​or single sensors, this invention can more accurately identify abnormal fluctuations in complex environments, avoiding safety hazards caused by misjudgments or missed detections.

[0018] 2. This invention achieves rapid response and safety protection by intelligently analyzing current trends and detecting falls in real time, triggering an emergency shutdown mechanism. Compared to the delayed response mechanisms of existing technologies, this invention can shut down the machine at the moment of a fall, significantly reducing the risk of further injury to the user and improving treadmill safety.

[0019] 3. By utilizing equipment maintenance warning and remote data upload technology, this invention automatically triggers an alarm when abnormal current fluctuations are detected and uploads event data to the cloud for remote analysis and processing, achieving efficient management and remote intervention. Compared to existing methods that rely on manual inspections, this early warning and remote monitoring mechanism can effectively reduce maintenance cycles and improve equipment availability and operational stability.

[0020] 4. This invention combines local storage with cloud upload to enable real-time data recording and remote analysis after a treadmill fall, achieving the technical benefits of data archiving and traceability. Unlike existing technologies that lack traceability, this invention ensures that detailed information about fall events is accurately recorded and can be viewed at any time through the cloud, significantly improving system transparency and operational efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a flow chart of the method of the present invention; Figure 2 This is a system framework diagram of the present invention. DETAILED DESCRIPTION

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the present specification. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0023] Please see the attached Figure 1 , an embodiment of the present invention provides a treadmill status monitoring method, comprising the following steps; S1. Install a current monitoring module in the circuit of the treadmill to collect current data in real time when the treadmill is working; Specifically, in this embodiment, step S1 periodically collects the real-time current signal of the treadmill and performs basic data processing on the collected results. Generally, the collection work is controlled by an embedded microcontroller unit (MCU), which connects the current sensor to the treadmill's main power line to achieve non-invasive signal extraction.

[0024] Specifically, the current sensor can employ a Hall effect current sensor, a shunt resistor measurement circuit, or a high-sensitivity sensor based on the magnetoresistive effect. Optionally, the sensor's sampling frequency is set above 500 Hz to fully capture the current signal's fluctuation characteristics. The sensor's output signal undergoes A / D conversion and is then sent to a signal processing module for temporary storage.

[0025] In one possible implementation, the signal processing module performs denoising on the raw current sampling data. This processing can be performed using a sliding average filter, a median filter, or a weighted recursive filter, with the goal of removing high-frequency interference and transient abnormal pulse values. As a common method, a sliding average method with a window size of 5 can be used. That is, at any time t, the current current value I(t) is calculated as follows: Among them, I avg (t) represents the sliding average current value at time t, N represents the size of the sliding window, which defines the number of historical data points included in the average calculation, ∑ represents the summation symbol, indicating that all current values ​​within the specified range are summed, I(ti) represents the current value at time ti, t represents the current discrete time point, and i represents the index in the sliding window, which represents the offset of the historical data point, summed from 0 to N-1. This processing makes the subsequent difference calculation and threshold comparison more stable and reliable.

[0026] In some embodiments, to ensure the time consistency of current sampling results, the system internally calibrates and verifies the sampling clock, using a temperature-compensated clock source to reduce system error accumulation. Data storage can use a ring buffer structure to reduce memory usage while supporting rolling data analysis.

[0027] Step S1 provides high-quality data input for the subsequent state determination module through a stable and high-frequency current signal acquisition process, combined with necessary preprocessing operations. The accuracy and processing strategy of this step will directly affect the judgment accuracy and response timeliness of the entire state monitoring system.

[0028] S2. Analyze the collected current data to determine whether the user is in a running state; Specifically, after completing the acquisition and preprocessing of the treadmill running current signal in step S1, in order to effectively identify whether the user is in a running state, the present invention further sets and executes step S2, that is, by analyzing the current change rate in real time, it is determined whether the treadmill is in a state of being driven by the user at the current moment. This step is closely followed by the aforementioned signal acquisition module in terms of structure and is the core entry point of the running state monitoring logic, providing a fundamental support for subsequent fluctuation anomaly detection and safety judgment.

[0029] In this embodiment, the system first constructs a current change rate sequence in sequence based on the acquired and smoothed current data according to the time series. Generally, the current change rate refers to the difference in current values within a unit sampling interval and can characterize the dynamic characteristics of the motor response change during running.

[0030] Specifically, the calculation method of the current change rate ΔI(t) at the current moment is: ΔI(t) = I(t) - I(t - 1); where, ΔI(t) represents the current change rate at time t, which describes how fast the current changes with time, I(t) represents the current value at the current time t, I(t - 1) represents the current value at the previous time t - 1, t represents the current discrete time point, such as the t-th sampling point, and Δ represents the symbol of the change amount, which is used mathematically to represent the change amount of a certain variable.

[0031] In a possible implementation manner, the system will compare the current change rate ΔI(t) with a preset first threshold T1.

[0032] When the condition |ΔI(t)| ≥ T1 is satisfied, it is determined that the current treadmill is in a state of being used by the user; On the contrary, when |ΔI(t)| < T1 lasts for a certain period of time, it can be further determined that the treadmill is in a non-running state or an idle state.

[0033] As an option, the threshold T1 can be set according to the typical current fluctuation characteristics of different models of treadmills. In some embodiments, its numerical range is determined by using an empirical model or a measured data training method. For example, for a standard household treadmill, T1 can be set between 0.3A and 0.6A.

[0034] In some embodiments, to enhance the robustness of the recognition, statistical analysis can also be performed on the change rates of multiple consecutive sampling points, such as judging the mean or maximum value of the change rates within a sliding window, to avoid misjudgment caused by a single mutation or electrical interference. At this time, the recognition logic can be extended to; in, represents the sliding absolute value average of the current change rate at the current moment t, ΔI(tk) represents the current change at the tkth moment, defined as I(tk)-I(tk-1), and N represents the length of the sliding window, that is, the number of historical data points used to calculate the average value. It represents the sum of each item from k=0 to k=N-1, t is the current sampling time, and is the sampling point number.

[0035] To address differences in usage conditions such as user weight and cadence, some embodiments introduce an adaptive learning mechanism to dynamically adjust the judgment interval of ΔI(t) based on historical data, thereby achieving adaptive support for lightweight users or jogging.

[0036] In addition, in some implementations, the system can also modify the rate of change threshold in combination with the treadmill speed setting parameters, for example, increasing the threshold tolerance at high speed gears to prevent misidentification.

[0037] Step S2 accurately identifies whether the user is running by analyzing the changing trends and fluctuations of the current values ​​at adjacent moments, combined with a time-dependent delay judgment mechanism. This step is closely integrated with the aforementioned current sampling and preprocessing process and serves as a prerequisite for subsequent safety incident detection.

[0038] S3: Automatically pause data recording when detecting that the user stops exercising or leaves the running area; Specifically, after completing step S2 to determine the running state, this embodiment further identifies whether abnormal fluctuations occurred during the running state. Step S3, following this identification result, analyzes and determines the abnormal characteristics of the current fluctuations to identify potential risk behaviors, such as unstable user posture, treadmill slippage, or sudden changes in motor load. This step plays both a transitional and critical role in the technical solution of the present invention, relying on the calculation of the previous current change rate and providing a quantitative basis for subsequent fall or fault determination.

[0039] In this embodiment, step S3 specifically includes: constructing a fluctuation amplitude index based on the current change rate sequence under the judgment of being in the "running state", and comparing it with the set second threshold value to determine whether there is abnormal fluctuation.

[0040] Generally, the fluctuation amplitude can be measured by calculating the variance or standard deviation of the current change rate over a period of time. For example, if the sliding window length is set to N, the fluctuation amplitude σ(t) is defined as; Among them, σ(t) is the current fluctuation amplitude at the current moment t, that is, the standard deviation of the current change rate, which reflects the degree of dispersion of the current signal. The larger the value, the more severe the fluctuation, which is often used to detect abnormal or fluctuating conditions. ΔI(tk) is the current change rate at the tkth moment. Time t corresponds to the mean of the current change rate within the sliding window N. The length of the N sliding window determines the calculation interval of the standard deviation and the mean. The ∑ summation symbol, from k = 0 to N-1, indicates that the calculation is performed on the data of the most recent N moments.

[0041] Specifically, when the fluctuation amplitude σ(t) exceeds a set second threshold T2, the system determines that an abnormal fluctuation state exists. In one possible implementation, the setting of threshold T2 takes into account the no-load fluctuation characteristics and normal load fluctuation range of different treadmill models. For example, in some home models, the value of T2 can be set to a standard deviation of the rate of change between 0.2A and 0.5A.

[0042] As an option, in order to enhance the ability to respond to instantaneous and severe fluctuations, the maximum change rate index ΔI can also be introduced max ; Among them, ΔI max (t, N) represents the maximum current change at time t, which is the maximum absolute value of all current change rates within the sliding window N. ΔI(tk) represents the current change rate at time tk. max represents the maximum value, which is the length of the sliding window N after taking the absolute value of the current change rate within the sliding window. k represents the index variable in the sliding window, ranging from 0 to N-1, which is used to represent the offset from the current time t.

[0043] In some embodiments, to improve the system's generalization capabilities under different environmental conditions, the fluctuation thresholds T2 and T3 can be dynamically adjusted based on real-time temperature and voltage changes and historical operating trajectories. This adaptive mechanism is implemented by building an environmental perception model, combined with fuzzy logic or rule-based adjustment mechanisms to ensure a balance between threshold sensitivity and false positive rate.

[0044] In certain embodiments, the system can also combine the abnormal fluctuation signal with the currently set running speed level for judgment. For example, in high-speed mode (greater than 10km / h), the allowed fluctuation threshold range is relatively increased to prevent current fluctuations during normal acceleration from being mistakenly judged as abnormal.

[0045] Step S3 accurately identifies potential abnormal conditions through statistical analysis of current rate-of-change fluctuations and threshold determination. This step not only reduces data noise and extracts patterns during the identification process, but also improves the accuracy and robustness of the determination through the combination of multiple indicators.

[0046] S4. Analyze the operating status of the treadmill based on the current data to identify whether there is abnormal current fluctuation; Specifically, in this embodiment, after completing step S3 to determine if the current fluctuation is abnormal, the system needs to further analyze whether there are more serious risk events, such as a user falling or equipment failure. Step S4 is responsible for this subsequent risk detection and judgment. The core purpose of this step is to respond in real time to abnormal fluctuation events occurring within the system, promptly triggering corresponding warning mechanisms or taking measures to ensure user safety. Step S4 is closely integrated with the aforementioned current change detection module to ensure the real-time and responsiveness of the entire monitoring system.

[0047] In this embodiment, step S4 further analyzes the continuous current data based on the abnormal fluctuation event identified in step S3, combining it with external sensor information, such as accelerometer or pressure sensor data, to identify potential falls or other emergency conditions in real time. Specifically, the core of step S4 is to compare the deviation of the current change with the mean of historical current values ​​to determine whether it exceeds a preset threshold, thereby determining whether abnormal current fluctuation has occurred.

[0048] In this embodiment, the calculation formula for abnormal current fluctuation is as follows: ΔI threshold =|I(t)-μ I |; Among them, ΔI threshold Indicates the threshold value of current change. When the current change exceeds the set threshold, the system will issue a warning of abnormal current fluctuation. I(t) represents the current value at the current moment t, μ I It represents the mean of historical current data, that is, the average value of current values ​​within a certain period of time. It reflects the normal level of current and serves as a benchmark for judging whether current fluctuations are abnormal. t represents the current discrete time point, usually the timestamp of the sampling point.

[0049] When ΔI threshold When the current exceeds a preset threshold, the system will issue a warning signal indicating abnormal current fluctuations. This threshold is set primarily to account for the range of current fluctuations that may occur during normal use of the treadmill and to effectively distinguish abnormal current fluctuations.

[0050] For example, in some embodiments, the threshold value can be dynamically adjusted based on the treadmill's power and current fluctuation range to accommodate the usage conditions of different treadmill models. For common home treadmills, the historical current average may be calculated based on the average current value over a certain period of time, typically a few seconds or minutes of sampled data.

[0051] In addition, some embodiments can also combine the current fluctuation amplitude with other motion parameters (such as acceleration or running speed) to enhance the system's judgment capabilities. When there is a significant correlation between current fluctuations and acceleration changes, the system may further determine it as an abnormality and take appropriate safety measures.

[0052] Step S4 calculates the deviation between the current value and the historical current value and makes a judgment based on a preset threshold value, thereby achieving effective detection of abnormal current fluctuation.

[0053] S5. When the current fluctuation is abnormal, the equipment maintenance warning is triggered, prompting equipment inspection and maintenance; Specifically, based on the aforementioned steps S2, S3, and S4, the system is now able to accurately monitor the treadmill's current fluctuations and promptly identify any abnormal fluctuations or risk events. Step S5, based on the results of the previous steps, further processes and responds to abnormal conditions to ensure user safety and the normal operation of the equipment. Specifically, step S5 is responsible for classifying abnormal events and triggering appropriate handling measures, such as alarms, automatic shutdowns, or other emergency responses.

[0054] In this embodiment, step S5 determines whether a high-risk event (such as a fall, equipment failure, etc.) exists based on the abnormal current fluctuation information detected in step S4 and the risk score. If it is determined to be abnormal, the system will initiate the corresponding emergency processing mechanism, including sending an alarm, pausing the treadmill operation, or notifying maintenance personnel. In addition, the system will also classify and process different abnormality types. For example, in the case of a fall, the system can further confirm the nature of the event through sensors such as accelerometers to ensure that the system's response is accurate.

[0055] Generally, the system considers multiple factors to determine whether emergency action is necessary. Besides current fluctuations, external factors such as the treadmill's usage, the user's exercise intensity, and ambient temperature must also be considered. For example, in some embodiments, when a treadmill is in high-speed mode and detects significant fluctuations, the system prioritizes determining whether the user has fallen or the device is malfunctioning.

[0056] Risk assessment: Compare the risk score (e.g., Risk(t)) calculated in step S4 with a preset threshold (e.g., T5) to determine whether an emergency response is required. Risk(t)=α·|ΔI(t)|+β·|Δa(t)|; Where Risk(t) represents the risk score at time t, ΔI(t) is the rate of change of current, Δa(t) is the acceleration change, and α and β are weight coefficients used to adjust the proportion of the two in the total risk.

[0057] If Risk(t) ≥ T5, the system will trigger an alarm or other emergency response.

[0058] Fault determination and handling: When abnormal current fluctuations are combined with equipment status detection (such as motor load and temperature changes), the system further determines whether the abnormal fluctuations are caused by equipment failure. For example, if the motor temperature is too high or the voltage is unstable, the system will determine that it is an equipment failure and suspend the treadmill.

[0059] Multi-sensor information fusion: To ensure accurate assessments, the system can also combine data from multiple sensors, such as accelerometers and pressure sensors. By comprehensively analyzing this information, the system can more accurately determine whether a fall, equipment failure, or other safety hazard has occurred. In some embodiments, the system can learn from historical data about user movement patterns and adjust risk assessment algorithms based on individual user differences.

[0060] Step S5 determines whether an abnormal event has occurred by integrating current fluctuations, risk scores and various sensor data, and triggers corresponding emergency response measures according to different abnormality types.

[0061] S6. Detecting a user fall event by analyzing the current change trend. If a fall event is detected, an emergency shutdown mechanism is immediately triggered; Specifically, after completing the initial assessment and response to abnormal current fluctuations in step S5, this embodiment proceeds to step S6 to further identify fall events. This step not only relies on a single current fluctuation indicator, but also accurately identifies whether a user has fallen based on multiple dimensions, such as the suddenness, magnitude, and duration of the current decline trend. This constitutes a key component of the risk event assessment mechanism within the entire treadmill status monitoring method.

[0062] In this embodiment, step S6 specifically involves tracking and analyzing the current variation trend. If the current variation amplitude is detected to drop sharply within a short period of time, and this variation amplitude exceeds a preset threshold, the system preliminarily determines it as a potential fall event. Furthermore, if the current remains at a low value for longer than a preset minimum period after the sharp drop, a fall event is further confirmed.

[0063] Specifically, the current change amplitude corresponding to a fall event is calculated as follows: ΔI fall =|I(t)-I(t-1)|; Among them, ΔI fallIt represents the amount of change in current drop. It indicates the magnitude of the change in the current value, specifically the absolute value of the difference between the current at the current moment and the current at the previous moment. I(t) represents the current value at the current moment t, and I(t-1) represents the current value at the previous moment t-1. t represents the current discrete time point, usually the timestamp of the sampling point.

[0064] When ΔI fall Greater than the preset current drop threshold T I When the current suddenly changes, the system marks the moment as a potential fall trigger. This threshold can be set based on the treadmill motor characteristics, the user's weight range, and historical data experience.

[0065] As an option, in order to avoid misjudgment, the system also needs to monitor whether the current after the drop is within a continuous time window T min The current is always lower than the set minimum current limit I min A fall event is only confirmed if the following two conditions are met: ΔI fall >T I ; and I(τ) min ; in: ΔI fall Indicates the change in current drop, which refers to the drop in current from a certain moment t to the time point before and after. I It represents the threshold of the current drop. When the current drop is greater than the threshold, the system determines that the current is dropping too fast. τ represents the time variable, which is used to describe the time range of the current change. It belongs to a time interval [t,t+T min ] That is, starting from the current time t and continuing to the minimum time T min , t represents the current time, usually the sampling time or inspection time, T min Indicates the minimum time requirement for continuous low current. It is used to avoid misjudgment caused by instantaneous fluctuations and ensure that the change in current is persistent rather than instantaneous. I(τ) represents the current value at time τ in amperes (A). min Indicates the lower current limit set by the system, usually the lowest current value of the system in a static or stable operating state.

[0066] In some embodiments, sudden current drops and sustained low values ​​can be cross-checked with other status parameters to enhance judgment accuracy. For example, when confirming a fall, if a sudden drop in velocity from high to zero is also detected, and the acceleration sensor identifies a significant impact change, the system can enhance judgment confidence.

[0067] ​In addition, the system can also introduce fuzzy logic control strategies. When the current change value is close to the set threshold, it does not make a hard judgment directly, but gives a certain judgment buffer to avoid boundary misjudgment.

[0068] Step S6 is structurally a close follow-up to the aforementioned current anomaly identification step. By comprehensively judging the amplitude and duration of current changes, combined with multi-sensor data and dynamic threshold management strategies, it achieves effective identification and response to fall events.

[0069] S7. When a fall event occurs, the detailed information of the fall event is recorded and saved to local storage, and uploaded to the cloud when connected to the network.

[0070] Specifically, in this embodiment, after determining a fall event in step S6, the system must promptly trigger the corresponding safety protection mechanism to prevent further harm to the user and ensure the controllability and safety of the device's operation. Step S7 plays a key role in the overall process. Its function is to: after confirming a fall event, execute the device to stop the action, record the event, and archive the relevant parameters and status through the system module.

[0071] Typically, when the system determines in step S6 that the fall criteria have been met (i.e., the current drop exceeds a threshold and the duration of the low current exceeds a set time), the control unit immediately sends a stop signal to the treadmill's main control chip. This signal acts on the drive motor control circuit, causing the treadmill to rapidly decelerate until it stops. During this process, the system controls the current output curve to ensure that it meets the preset deceleration constraints to avoid mechanical damage caused by momentary power outages.

[0072] Specifically, in some embodiments, the system adopts a linear deceleration model to control the output current to decrease according to the following relationship: Among them, I stop (t) represents the target current curve after the stop command. As time t passes, the current gradually decreases from the current value until it reaches zero, meeting the system's stop requirements. I0 represents the current current value when the stop operation is triggered, and t represents the current time, which is usually a time point after the stop command is issued. T s Indicates the safe deceleration time window set by the system. This is the time scale of current decay and is used to set a smooth transition process when the system stops.

[0073] As an option, the system can also be configured with a user prompt mechanism. When the shutdown is triggered, a prompt message is sent to the user through the voice module or screen display module. The message may include: abnormal fall behavior has been detected, the device has stopped safely, please check the usage status.

[0074] Step S7 is a key link in the safety protection logic in this technical solution. Through the collaborative mechanism of real-time shutdown response, motor current control, event parameter archiving and information prompts, it realizes closed-loop control and processing of fall risks, ensuring the operational safety and system manageability of users when using the treadmill.

[0075] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A treadmill status monitoring method, characterized in that: The following steps are included: S1. Install a current monitoring module in the circuit of the treadmill to collect current data in real time when the treadmill is working; S2. Analyze the collected current data to determine whether the user is in a running state; S3: Automatically pause data recording when detecting that the user stops exercising or leaves the running area; S4. Analyze the operating status of the treadmill based on the current data to identify whether there is abnormal current fluctuation; S5. When the current fluctuation is abnormal, the equipment maintenance warning is triggered, prompting equipment inspection and maintenance; S6. Detecting a user fall event by analyzing the current change trend. If a fall event is detected, an emergency shutdown mechanism is immediately triggered; S7. When a fall event occurs, the detailed information of the fall event is recorded and saved to local storage, and uploaded to the cloud when connected to the network.

2. The treadmill status monitoring method according to claim 1, characterized in that: The current monitoring module in step S1 is installed in the main control circuit of the treadmill, with a sampling frequency of once per second and a sampling accuracy of not less than 16 bits. The current monitoring module is a Hall current sensor.

3. The treadmill status monitoring method according to claim 1, characterized in that: In step S2, determining whether the user is in a running state is achieved by analyzing the current change rate and comparing it with a set threshold. If the current change rate exceeds the set threshold, it is determined that the user is in a running state. The current change rate calculation formula is as follows: ΔI(t)=I(t)-I(t-1); Where ΔI(t) represents the rate of change of current at time t, I(t) represents the current value at the current time t, I(t-1) represents the current value at the previous time t-1, t represents the current discrete time point, and Δ represents the sign of the change.

4. The treadmill status monitoring method according to claim 1, characterized in that: The identification of abnormal current fluctuation in step S4 is performed by calculating the deviation between the current value and the historical current average value. If the deviation exceeds a preset threshold, it is determined to be abnormal current fluctuation. The calculation formula for abnormal current fluctuation is as follows: ΔI threshold =|I(t)-μ I |; Among them, ΔI threshold represents the threshold value of current change, I(t) represents the current value at the current moment t, μ I represents the mean value of historical current data, and t represents the current discrete time point.

5. The treadmill status monitoring method according to claim 1, characterized in that: The device maintenance warning in step S5 is triggered based on the comparison of current fluctuation and the device health assessment model. If the current fluctuation exceeds a preset range, the system automatically triggers the device maintenance warning and prompts the user to perform inspection and maintenance.

6. The treadmill status monitoring method according to claim 1, characterized in that: The current change trend of the user fall event detected in step S6 includes detecting a sharp change in the current. If the current change amplitude exceeds a preset threshold and the current value is lower than the set minimum current value for a long time, it is determined to be a fall event. The current change amplitude calculation formula for the fall event determination is as follows: ΔI fall =|I(t)-I(t-1)|; Among them, ΔI fall represents the amount of current drop, I(t) represents the current value at the current moment t, I(t-1) represents the current value at the previous moment t-1, and t represents the current discrete time point.

7. The treadmill status monitoring method according to claim 1, characterized in that: When recording a fall event in step S7, the recorded content includes the current change rate, the timestamp of the fall, and the current fluctuation information before and after the fall. This information is stored in the local storage module and uploaded to the cloud for remote analysis after the device is connected to the network.

8. The treadmill status monitoring method according to claim 1, characterized in that: The cloud uploading step in step S7 includes uploading detailed records of the fall event to the cloud database. The cloud system detects the health status of the device through remote analysis and can remotely intervene in the treadmill. The cloud data upload and processing uploads the event data to the cloud server through a network connection to perform health status assessment and trigger remote intervention.

9. A treadmill status monitoring system, applied to the treadmill status monitoring method according to any one of claims 1 to 8, characterized in that: include; Current monitoring module, used to collect current data of treadmill motor in real time; a data processing module, which is in communication with the current monitoring module and is used to calculate the current change rate and determine whether the treadmill is in motion; A maintenance warning module, which is in communication with the data processing module and is used to generate an equipment health assessment model based on the current fluctuation data and trigger a maintenance warning when the current fluctuation exceeds a preset range; A fall detection module, which is in communication with the data processing module and is used to analyze the current change trend to identify a fall event and trigger an emergency shutdown mechanism when a fall event is detected; A local storage module, which is connected to the fall detection module and the maintenance warning module, and is used to record and store detailed information of fall events; The cloud upload module is connected to the local storage module and is used to upload fall event records to the cloud for remote analysis.

10. A treadmill, characterized in that include; Electric drive unit; Main control processor; Display unit; control systems; When the control system is running, the treadmill state monitoring method according to any one of claims 1 to 8 is implemented.

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