A method and device for quantifying and evaluating fatigue of a person operating an electric power tool

Abstract

The application provides a method for quantifying and evaluating fatigue of personnel operating electric tools. The method comprises: selecting at least one muscle related to personnel operating electric tools to deploy an electromyography sensor; a person to be tested operates a specified electric tool in a test scene, and an electromyography sensor is used to continuously collect muscle electrical signals of the person to be tested when operating the specified electric tool; the collected electromyography signals are preprocessed and corrected for abnormalities to determine the start and end points of the electromyography signals corresponding to each action of the person to be tested when operating the specified electric tool; further multi-dimensional feature extraction is performed to obtain multiple fatigue quantification characterization indexes; according to the multiple fatigue quantification characterization indexes, the difference between each fatigue quantification characterization index in a specified action segment is determined, and a comprehensive fatigue evaluation quantity of the person to be tested is calculated according to a preset weight parameter. The application realizes more effective and accurate identification and quantification of the fatigue of the person to be tested.

Description

Technical Field

[0001] This application relates to the fields of aerospace and human factors engineering technology, and more specifically, to a method and apparatus for quantitatively evaluating fatigue of personnel operating power tools. Background Technology

[0002] Power tools are specialized equipment powered by electricity and widely used in various operations such as drilling and disassembly. Compared to traditional hand tools, power tools not only offer faster operation speeds and higher efficiency, significantly improving work quality and completion rates, but also effectively reduce the physical exertion of operators, lowering labor intensity and operational risks. Their applications are extensive, deeply covering multiple industries such as machining, ship repair, and aerospace assembly, becoming an important tool for improving work efficiency and ensuring ease of operation in various scenarios.

[0003] When operating power tools, personnel are prone to accumulating work fatigue due to the need to maintain a fixed posture while holding and starting / stopping the tools, and the vibrations they experience during operation. This fatigue not only leads to decreased concentration and reduced coordination, resulting in operational errors and increasing the risk of equipment damage and personnel injury, but also causes occupational injuries such as tenosynovitis and carpal tunnel syndrome due to prolonged muscle and joint tension, thus harming the operator's health. Particularly in the aerospace field, astronauts experience a significant 40%–50% reduction in hand strength due to the fabric resistance of spacesuit gloves and the pressure inside the suit. Under these conditions, astronauts operating power tools outside the space station while wearing spacesuits for screw assembly and disassembly are highly susceptible to hand fatigue. Quantitatively assessing personnel work fatigue using scientific methods can optimize task allocation based on the evaluation results, reducing operator fatigue load, and provide data support for the ergonomic design of power tools. This is of great and practical significance for ensuring operational safety and protecting personnel health.

[0004] Therefore, it is necessary to provide a method and apparatus for quantitatively evaluating the fatigue of personnel operating power tools in order to solve one of the aforementioned technical problems.

[0005] Application content

[0006] The purpose of this application is to provide a method, apparatus, medium, and electronic device for quantitatively evaluating fatigue of personnel operating power tools, which can solve at least one of the aforementioned technical problems. The specific solution is as follows:

[0007] According to a specific embodiment of this application, this application provides a method for quantitatively assessing fatigue during operation of power tools, comprising: selecting at least one muscle related to the operation of power tools to deploy an electromyography (EMG) sensor for collecting the EMG signals of the person under test when operating a specified power tool; setting up a test scenario according to specific environmental and operational conditions, selecting a person under test, and having the person under test operate the specified power tool under the test scenario; the test scenario includes a space operation environment test scenario; continuously collecting the EMG signals of the person under test when operating the specified power tool; preprocessing and correcting anomalies in the collected EMG signals to determine the start and end points of the EMG signals corresponding to each movement of the person under test when operating the specified power tool; extracting multi-dimensional features from the EMG signals with determined start and end points to obtain multiple quantitative fatigue assessment indicators; determining the difference between each quantitative fatigue assessment indicator in a specified movement segment based on the extracted multiple quantitative fatigue assessment indicators, and calculating the comprehensive fatigue assessment value of the person under test according to preset weight parameters.

[0008] According to a specific embodiment of this application, this application also provides a device for quantitatively assessing fatigue during operation of power tools, comprising: a selection processing module, used to select at least one muscle related to the operation of power tools to deploy electromyography (EMG) sensors, and determine the sensor deployment positions corresponding to each muscle; an operation processing module, used to set up a test scenario according to specific environment and operating conditions, select a test subject, and have the test subject operate a specified power tool under the test scenario; the test scenario includes a space operation environment test scenario; an acquisition processing module, used to continuously acquire the EMG signals of the test subject when operating the specified power tool; a first determination module, used to preprocess and correct anomalies in the acquired EMG signals, and determine the start and end points of the EMG signals corresponding to each action of the test subject when operating the specified power tool; an extraction processing module, used to extract multi-dimensional features from the EMG signals with determined start and end points to obtain multiple quantitative fatigue characterization indicators; and a second determination module, based on the extracted multiple quantitative fatigue characterization indicators, determining the difference between each quantitative fatigue characterization indicator in a specified action segment, and calculating the comprehensive fatigue assessment value of the test subject according to preset weight parameters.

[0009] According to a specific embodiment of this application, this application also provides a computer-readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method for quantitative evaluation of fatigue measurement of personnel operating power tools as described in any of the preceding claims.

[0010] According to a specific embodiment of this application, this application also provides an electronic device, including: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement the method for quantitative evaluation of fatigue measurement of personnel operating power tools as described in any of the preceding claims.

[0011] Compared with the prior art, the above-described solutions of this application have at least the following beneficial effects:

[0012] This application deploys an electromyography (EMG) sensor on at least one muscle associated with a person operating a power tool. The system continuously collects EMG signals from the subject's muscles while they operate the power tool in a simulated test scenario. The collected EMG signals are preprocessed and anomaly corrected to accurately determine the start and end points of the EMG signals for each movement during the operation. Through multi-dimensional feature extraction, multiple fatigue quantification indicators are obtained. The differences between these indicators across specified movement segments are determined, and a comprehensive fatigue assessment of the subject is calculated based on preset weight parameters. This approach achieves more effective and accurate identification and quantification of the subject's fatigue level. Attached Figure Description

[0013] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:

[0014] Figure 1 This is a flowchart illustrating a method for quantitatively evaluating fatigue levels of personnel operating power tools, as shown in an embodiment of this application.

[0015] Figure 2 This is a schematic diagram illustrating an example of the deployment location of the electromyography sensor corresponding to the selected muscle in the quantitative evaluation method for measuring fatigue during operation of power tools, as shown in an embodiment of this application.

[0016] Figure 3 This is a schematic diagram of another example of the deployment location of the electromyography sensor corresponding to the muscle selected in the method for quantitative evaluation of fatigue during operation of power tools as shown in the embodiments of this application.

[0017] Figure 4 This is a schematic diagram illustrating an example of the deployment location of the electromyography sensor corresponding to the selected muscle in the quantitative evaluation method for measuring fatigue during operation of power tools, as shown in an embodiment of this application.

[0018] Figure 5 This is an example diagram of electromyographic signals after preprocessing and anomaly correction in the quantitative evaluation method for measuring fatigue of personnel operating power tools as shown in the embodiments of this application.

[0019] Figure 6 This is an example diagram showing the origin and termination points of electromyography signals determined in the quantitative evaluation method for measuring fatigue of personnel operating power tools, as illustrated in an embodiment of this application.

[0020] Figure 7 This is an example diagram showing the adjustment of a specified factor in the quantitative evaluation method for measuring fatigue of personnel operating power tools, as illustrated in an embodiment of this application.

[0021] Figure 8 This is an example diagram showing the adjustment of a specified factor in the quantitative evaluation method for measuring fatigue of personnel operating power tools, as illustrated in an embodiment of this application.

[0022] Figure 9 This is a structural block diagram of a device for quantitatively evaluating the fatigue of personnel operating power tools, as shown in an embodiment of this application.

[0023] Figure 10 This is a schematic diagram of the electronic device structure shown in an embodiment of this application. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0025] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the application. The singular forms “a,” “said,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0026] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0027] It should be understood that although the terms first, second, third, etc., may be used in the embodiments of this application, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, first may also be referred to as second without departing from the scope of the embodiments of this application, and similarly, second may also be referred to as first.

[0028] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that an article or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the article or device that includes said element.

[0029] The purpose of this application is to provide a quantitative evaluation method for the fatigue of personnel operating power tools.

[0030] For the scenario of "personnel operating power tools" (i.e., the space operation environment test scenario), it is necessary to solve problems such as the deployment planning of multi-muscle electromyography sensors associated with personnel operating power tools, the detection of the start and end points of electromyography signals corresponding to the movements, and the quantitative determination of multi-dimensional fusion fatigue measurement.

[0031] This application provides a method for quantitatively assessing fatigue in personnel operating power tools. This method can accurately determine the start and end points of electromyographic signals corresponding to each action of the person being tested when operating a specified power tool. Through multi-dimensional feature extraction, multiple fatigue quantitative characterization indicators are obtained. The difference between each fatigue quantitative characterization indicator in the specified action segment is determined. Based on preset weight parameters, the comprehensive fatigue assessment of the person being tested is calculated, thus achieving more effective and accurate identification and quantification of the fatigue level of the person being tested.

[0032] It should be noted that the method described in this application has a wide range of applications, and is particularly suitable for scenarios where astronauts use power tools in space.

[0033] The following is combined with Figures 1 to 8 Detailed description of optional embodiments of the method of this application.

[0034] like Figure 1 As shown, in step S101, at least one muscle related to the operation of the power tool by the person is selected to deploy the electromyography sensor.

[0035] In one specific embodiment, the operation of the power tools by the personnel includes tightening or loosening screws using handheld power tools.

[0036] Specifically, three muscles associated with the operation of power tools (e.g., tightening or loosening screws) are selected for deploying electromyography (EMG) sensors. The three EMG sensors are directly attached to the designated three muscles of the subject, including the extensor digitorum superficialis, flexor digitorum superficialis, and flexor carpi ulnaris. For details on the deployment locations of the three EMG sensors, please refer to [link to relevant documentation]. Figures 2 to 4 The text refers to the deployment locations of "sensor 1", "sensor 2", and "sensor 3".

[0037] As for the extensor digitorum, as a key muscle group closely related to the operation of power tools (such as gripping, pressing buttons, operating tool switches and performing reciprocating / rotating movements), its anatomical structure is as follows: the extensor digitorum originates from the lateral epicondyle of the lower end of the humerus, descends to the posterior end of the forearm, and finally divides into four tendons.

[0038] To determine the placement of the electromyographic sensor for the extensor digitorum muscle, the subject should assume a flexed elbow position, clench their fist, and dorsiflex their wrist. The muscle belly of the extensor digitorum muscle can be palpated in the area below the lateral epicondyle of the humerus, exhibiting a distinctly arched shape. Sensor 1 (the electromyographic sensor) should be placed at this location. See [link to details]. Figure 2 .

[0039] The flexor digitorum superficialis is a core driving muscle for finger flexion during power tool operation, closely related to the grip tightness, button triggering force, and fine manipulation (such as adjusting tool speed and switching operating modes). Its anatomical features are as follows: it originates at the medial epicondyle of the humerus and extends to the interphalangeal joints of the four fingers.

[0040] The placement of the electromyography (EMG) sensor for the superficial flexor muscles is determined by the layered arrangement of the forearm palmar flexor muscles. The radial and ulnar flexor carpi radialis muscles are located in the superficial layer (first layer), while the superficial flexor digitorum superficialis muscles are located in the deep layer (second layer). Due to the coverage of these superficial muscles, the EMG sensor electrodes cannot directly contact the muscle belly of the superficial flexor digitorum superficialis muscles. However, the superficial flexor digitorum superficialis muscles gradually become more superficial near the proximal end of the wrist joint, and in this area, their muscle fibers attach to the tendons of the carpal tunnel. Therefore, the EMG sensor can be attached to a location where the tendon is noticeably arched. First, locate the palmaris longus tendon on the palmar side of the forearm of the subject. Move along this tendon proximally (towards the elbow joint), and then instruct the subject to clench their fist. At this point, a noticeable arch can be felt near the palmaris longus tendon. This arch serves as a surface landmark for the attachment location of the superficial flexor digitorum superficialis muscles. Deploy sensor 2 (i.e., the EMG sensor) at the location where the tendon is noticeably arched. See [link to details]. Figure 3 .

[0041] The flexor carpi ulnaris is a key muscle for wrist stability and hand coordination during power tool operation. It directly affects wrist support when holding the tool, the coordination of force exertion during operation (such as twisting and pressing movements), and muscle fatigue after prolonged operation. Its anatomical features are as follows: it originates from the medial epicondyle of the humerus and inserts into the pisiform bone.

[0042] To determine the placement of the electromyography (EMG) sensor for the flexor carpi ulnaris muscle, the subject, with their palm facing upward, performs a wrist flexion motion. A raised tendon can be found proximal to the pisiform bone. Approximately 17 cm above this tendon, a distinctly arched muscle can be felt; this arched location is the surface location of the flexor carpi ulnaris muscle belly. The EMG sensor is then attached to this arched location. See [link to details]. Figure 4 .

[0043] Specifically, the deployed electromyography (EMG) sensors are used to collect the electrical muscle signals of the person being tested while operating a designated power tool.

[0044] Optionally, the personnel to be tested are astronauts, and the number of personnel to be tested is greater than 12. In this example, the number of personnel to be tested is 13.

[0045] In one specific implementation, 13 subjects aged, for example, 26 to 35 years old (e.g., height range 160cm to 175cm, weight range 55kg to 75kg) were selected to participate in the trial and were required to be in good health.

[0046] For example, the following health and condition requirements must be met: good physical condition, no chronic diseases (such as arthritis, neuropathy, etc.), and no discomfort symptoms such as pain, soreness, or weakness in the forearm and hand muscles; no history of muscle or bone injury (no forearm fracture, wrist ligament injury, tendinitis, or other injuries affecting muscle function within the past 3 years); no participation in high-intensity physical activities (such as weightlifting, push-ups, long-distance cycling, etc., which may cause forearm muscle fatigue) in the week before the test, and no forearm muscle-related strength training or repetitive movements (such as prolonged use of keyboard and mouse) within 24 hours before the test, ensuring that the flexor carpi ulnaris and surrounding muscles are in a normal physiological state, and avoiding special circumstances such as fatigue or overactivation interfering with electromyographic signal acquisition.

[0047] It should be noted that there is no particular limitation on the number of muscles. The above is only an optional example and should not be construed as a limitation on this application.

[0048] Next, in step S102, a test scenario is set according to specific environment and operating conditions, a test subject is selected, and the test subject operates a specified power tool under the test scenario; the test scenario includes a space operation environment test scenario.

[0049] In one specific implementation, a space operation environment test scenario is simulated according to specific environmental and operational conditions.

[0050] Specifically, the test scenario of simulating the space operation environment is based on the weightlessness state and operating conditions in a specific environment. The simulated space operation environment test scenario includes a glove box containing the gloves of the personnel to be tested, an indoor temperature maintained at 22℃~25℃, a relative humidity controlled at 45%~60%, and no obvious electromagnetic interference sources.

[0051] Furthermore, the simulated space operation environment includes a temperature and humidity-controlled open area in the laboratory (with at least 2m×2m operating space reserved), with the indoor temperature maintained at 22℃~25℃ and the relative humidity controlled at 45%~60%.

[0052] A glove box containing astronaut gloves was used to simulate the astronaut's operating environment in space. The glove box was evacuated to 0.6 atmospheres, and a suspension device was installed inside the gloves to counteract the weight of the power tools. The simulated space operating environment needed to be quiet (ambient noise ≤ 40 decibels) and free from significant sources of electromagnetic interference.

[0053] When the test subject operates the designated power tool in the test scenario, he / she shall adopt a standard standing posture with his / her feet shoulder-width apart, toes pointing in the direction of operation, knees slightly bent (bending angle of about 8°), upper body upright, arms hanging naturally, and then slowly raise his / her right forearm to the screw operation height, keeping the forearm in a pronated position and the wrist joint in a neutral position to start the operation of the power tool.

[0054] The process from starting a power tool to it automatically stopping is considered a single action, such as the action of tightening a screw or the action of loosening a screw.

[0055] It should be noted that the above is only an optional example and should not be construed as a limitation of this application.

[0056] Next, in step S103, the deployed electromyography (EMG) sensor is used to continuously collect the electromyographic signals of the test subject when operating the specified power tools.

[0057] Specifically, the designated power tool operation is, for example, an operation of tightening and loosening screws C times. The operation to be performed by the personnel specifically refers to performing 2C operations.

[0058] Using deployed electromyography (EMG) sensors, the electromyographic signals of the muscles of the subject are continuously collected while operating a specified power tool. Specifically, the EMG signals of the three muscles are continuously collected while operating the specified power tool.

[0059] Optionally, before deploying the electromyography (EMG) sensor, the subject should undergo necessary hair removal to ensure that the electrodes on the skin surface are in close contact with the subject's skin.

[0060] It should be noted that the above is only an optional example and should not be construed as a limitation of this application.

[0061] Next, in step S104, the collected electromyographic signals are preprocessed and anomaly corrected to determine the start and end points of the electromyographic signals corresponding to each action of the person under test when operating the specified power tools.

[0062] Specifically, a notch filter with a frequency of f0 was used to filter out power frequency interference generated by the laboratory power supply system. After filtering, spectrum analysis confirmed that the signal energy in the f0 band was reduced to the baseline level. A bandpass filter was used for bidirectional filtering to remove low-frequency drift and high-frequency noise: the low cutoff frequency was set to fL, the high cutoff frequency was set to fH, and the filter type was a finite impulse response (FIR) filter.

[0063] Taking the electromyography (EMG) signals of the current test subject as an example, EMG signal analysis, i.e., preprocessing and anomaly correction, was performed. A 50Hz notch filter was used to filter out power frequency interference from the laboratory power supply system. After filtering, spectrum analysis confirmed that the signal energy in the 50Hz band was reduced to the baseline level. A bandpass filter was used for bidirectional filtering to remove low-frequency drift and high-frequency noise: the low cutoff frequency was set to 20Hz, and the high cutoff frequency was set to 450Hz. This removed the signal energy in the 50Hz band. The finite impulse response (FIR) filter type was selected. For details on the preprocessed and anomaly-corrected EMG signals, please refer to [link to documentation]. Figure 5 .

[0064] It should be noted that because electromyographic signals are easily affected by various noises, preprocessing and anomaly correction are necessary.

[0065] Next, for the preprocessed and anomaly-corrected electromyographic signals, the start and end points of the electromyographic signals corresponding to each action of the subject when operating the specified power tools are determined.

[0066] A sliding window with a specific number of sampling points is set, and the effective signal threshold is used as a multiple of the signal standard deviation to filter out active signal regions and non-active signal regions.

[0067] Specifically, based on the characteristic that the amplitude of electromyographic signals is significantly higher than the baseline at rest when performing each action, a sliding window method is used to filter the action signal region and the non-action signal region. For example, the sliding window size is set to 450 (i.e., a specific number) sampling points, and a specified multiple (e.g., 2 times) of the signal standard deviation is used as the effective signal threshold.

[0068] Optionally, the specific quantity is 300 to 600.

[0069] Traverse all sliding windows and count the number of sampling points within each window whose absolute amplitude exceeds the effective signal threshold. The number of samples is close to zero in the static state and increases significantly in the dynamic state, thus initially distinguishing between the "dynamic window segment" and the "static window segment," i.e., the dynamic signal region and the non-dynamic signal region.

[0070] To eliminate misjudgments of motion signal regions caused by noise, a dynamically adjusted window threshold is used. A rule is set that a specified number of consecutive windows meeting the motion conditions is used as the motion start determination rule, while a specified number of consecutive windows failing to meet the motion conditions is used as the motion end determination rule. This is to accurately determine each motion related to the operation of a specified power tool.

[0071] Specifically, the window threshold of the current window is dynamically adjusted by determining whether the number of actions determined based on the filtered action signal region and non-action signal region is equal to the number of actual actions recorded.

[0072] When the number of actions determined based on the filtered action signal region and non-action signal region is less than the actual number of recorded actions, the value of the specified multiple is increased or the specific number of sampling points is decreased.

[0073] When the number of actions determined based on the filtered action signal region and non-action signal region is greater than the actual number of recorded actions, the value of the specified multiple is reduced or the specific number of sampling points is increased.

[0074] Based on 20 actual repetitions of the movements and a dynamic window threshold, the start and end points of each movement are located: A movement initiation rule is defined as four consecutive windows meeting the movement conditions; a movement termination rule is defined as four consecutive windows failing to meet the movement conditions. When the number of determined movements equals the recorded number of movements, the electromyographic (EMG) signal initiation and termination point detection is complete. For details on the determined EMG signal initiation and termination points, please refer to [link to documentation]. Figure 6 .

[0075] Specifically, the specified multiple ranges from 0.2 to 3. For example, when the current value of the specified multiple is 0.4, the number of actions determined based on the filtering of the action signal region and the non-action signal region is 9. See details below. Figure 7 When the number of actions determined based on the filtered action signal region and non-action signal region is less than the actual number of recorded actions, the current value of the specified multiple is increased to 0.6 to 1.5 times the current value. That is, when the current value of the specified multiple is adjusted to 0.6 times, the number of actions consistent with the actual number of recorded actions can be obtained. See details in [link to documentation]. Figure 8 .

[0076] It should be noted that the number of sampling points is fixed, and the sampling frequency of the electromyography (EMG) device is taken. For example, the number of sampling points per second is 450, which corresponds to a sampling frequency of 450Hz / s for the EMG signal acquisition device.

[0077] For example, when the specified multiplier is 3, the number of actions determined based on the filtered action signal area and the non-action signal area is 11. When the number of actions determined based on the filtered action signal area and the non-action signal area is greater than the actual number of recorded actions, the current value of the specified multiplier is reduced to 2 / 3 to 2 times the current value. Adjusting the current value of the specified multiplier to 1 to 6 times will yield the number of actions consistent with the actual number of recorded actions.

[0078] It should be noted that the above is only an optional example and should not be construed as a limitation of this application.

[0079] Next, in step S105, multi-dimensional feature extraction is performed on the electromyographic signals whose start and end points are determined to obtain multiple quantitative characterization indicators of fatigue.

[0080] Optionally, the root mean square (RMS), integrated electromyography (iEMG), and median frequency (MF) of the electromyography signal in the time domain are extracted as the first, second, and third quantitative indicators of muscle fatigue of the test subject when operating a specified power tool.

[0081] Specifically, the root mean square (RMS) temporal characteristic is a core temporal index reflecting the amplitude fluctuation characteristics of electromyographic (EMG) signals. Its value is directly related to the number of activated motor units and the firing frequency during muscle contraction. In the initial stage of operation of power tools, the muscles of the test subject are in a normal physiological state, motor unit activation is stable, and the RMS value remains within a relatively stable baseline range. As the operation time increases, the muscles gradually enter a state of fatigue. Some motor units cannot be activated normally due to fatigue, causing the muscles to increase the firing intensity of the remaining motor units to maintain the force required for the operation. This is manifested as an upward trend in the RMS value with increasing fatigue, thus allowing the extraction of the root mean square (RMS) temporal characteristic of the EMG signal.

[0082] The root mean square of the time-domain characteristics of the electromyographic signal is calculated using the following expression:

[0083]

[0084] Where RMS represents the root mean square of the time-domain characteristics of the electromyographic signal when the subject performs the specified action; k is the number of samples for a tightening or loosening action; x i Let k be the amplitude of the electromyographic signal at the i-th sampling point, where k and i are both positive integers, specifically 1, 2, ..., n.

[0085] Next, the integrated electromyography (iEMG) value is a core time-domain indicator reflecting the energy accumulation characteristics of electromyographic signals. It is defined as the integral of the absolute value of the electromyographic signal amplitude over a certain period of time. In the early stage of work, the muscle energy supply is sufficient, and the muscle contraction intensity is stable in each operation cycle, and the iEMG value remains within a relatively fixed range. As fatigue accumulates, the muscle ATP energy supply is insufficient, resulting in a decrease in contraction efficiency. This is manifested as the iEMG value gradually increasing with the number of operation cycles. Therefore, the integrated electromyographic value (iEMG) of the electromyographic signal is extracted.

[0086] The integral electromyographic value of the electromyographic signal is calculated using the following expression:

[0087]

[0088] Where iEMG represents the integrated electromyographic value of the electromyographic signal of the subject performing the specified action; i represents the i-th sampling point of the electromyographic signal of the subject performing the specified action; k represents the number of samples for a tightening action or a loosening action; x i This represents the amplitude of the electromyographic signal at the i-th sampling point.

[0089] Next, the median frequency (MF) is a key indicator reflecting the frequency distribution characteristics of electromyographic (EMG) signals. It is defined as the frequency point in the EMG power spectrum (PSD) where the power on both sides accounts for 50% of the total power. In the initial stage of work, muscles are mainly activated by high-frequency motor units, and the MF value is at a high level. As fatigue accumulates, high-frequency motor units are gradually replaced by low-frequency motor units, and the EMG power spectrum shifts towards lower frequencies. This is manifested as a continuous downward trend in the MF value as fatigue intensifies. Therefore, the magnitude of the MF value shift can be directly used as a quantitative basis for judging the degree of muscle fatigue.

[0090] The median frequency of the frequency characteristics of an electromyographic signal is calculated using the following expression:

[0091]

[0092] in, The PSD(f) represents the median frequency of the electromyographic signal during the performance of a specified action by the subject. PSD(f) is a function obtained by transforming the electromyographic signal from the time domain to the frequency domain using Fourier transform, where f is the frequency of the electromyographic signal. low f high These represent the lowest and highest frequencies calculated from the actions performed by the test subject.

[0093] It should be noted that the above is only an optional example and should not be construed as a limitation of this application.

[0094] Next, in step S106, based on the extracted multiple fatigue quantification indicators, the difference between each fatigue quantification indicator and the specified action segment is determined, and the comprehensive fatigue assessment of the person being tested is calculated according to the preset weight parameters.

[0095] Based on the extracted multiple fatigue quantification indicators, the differences between specified action segments are determined, and the comprehensive fatigue assessment of the test subject is calculated according to preset weight parameters. Specifically, the difference between the action segment corresponding to the first action signal region and the action segment corresponding to the last action signal region is determined.

[0096] The comprehensive fatigue assessment score of the test subject is calculated using the following expression:

[0097]

[0098] Wherein, CFI represents the comprehensive fatigue assessment score of the subject; m represents the number of muscles in the selected subject. This represents the quantitative characterization index of the first fatigue measure of the i-th muscle in the p-th movement segment of the test subject. This represents the quantitative characterization index of the first fatigue measure of the first movement segment of the i-th muscle in the subject of the test. This represents the second quantitative characterization index of fatigue in the p-th movement segment of the i-th muscle of the test subject. This represents the second fatigue quantification index of the first movement segment of the i-th muscle in the subject of the test. This represents the third quantitative characterization index of fatigue in the i-th muscle during the p-th movement segment of the test subject. λ represents the quantitative characterization index of fatigue in the first movement segment of the i-th muscle of the test subject; RMS , λ iEMG and λ MF These represent the first weight parameter, the second weight parameter, and the third weight parameter corresponding to the first fatigue quantification index, the second fatigue quantification index, and the third fatigue quantification index, respectively.

[0099] Furthermore, the entropy weight method is used to determine λ. RMS , λ iEMG and λ MF The value of is determined by the following steps.

[0100] The following expression is used to normalize the first, second, and third fatigue metric indicators:

[0101]

[0102] in, This represents the normalized value of the first fatigue quantification index of the i-th muscle and j-th movement segment of the test subject. This represents the normalized value of the second fatigue quantification index of the i-th muscle and j-th movement segment of the test subject. denoted as the normalized value of the third fatigue quantification index for the j-th movement segment of the i-th muscle in the test subject; p represents the number of all movement segments.

[0103] Next, the weight of each action sample in each quantitative representation index is calculated:

[0104]

[0105] in, This represents the weighting value of the first quantitative characterization index of fatigue measurement for the i-th muscle and j-th movement segment of the test subject. This represents the weighting value of the second fatigue quantification index for the j-th movement segment of the i-th muscle in the test subject. This represents the weighting value of the third quantitative characterization index of fatigue in the j-th movement segment of the i-th muscle of the test subject; This represents the normalized value of the first fatigue quantification index of the i-th muscle and k-th movement segment of the test subject, where k is the counting subscript used for accumulation.

[0106] Next, the information entropy of each quantitative representation index is calculated:

[0107]

[0108] Among them, e_RMS i e_iEMG represents the information entropy of the first quantifiable characteristic index of fatigue in the i-th muscle of the test subject. i e_MF represents the information entropy of the second fatigue quantification index of the i-th muscle of the test subject. i The information entropy represents the quantitative characterization index of the third fatigue measure of the i-th muscle of the test subject.

[0109]

[0110] Wherein, e_RMS represents the information entropy of the first fatigue metric; e_iEMG represents the information entropy of the second fatigue metric; e_MF represents the information entropy of the third fatigue metric; n M The number of muscles corresponding to the collected electromyographic signals is represented by i, where i represents the i-th muscle corresponding to the collected electromyographic signals.

[0111] Next, the weight parameters of each quantitative representation index are calculated:

[0112]

[0113] Where, λRMS λ represents the weight of the first quantitative characterization index of fatigue. iEMG λ represents the weight of the second fatigue quantification index; MF e_RMS represents the weight of the third fatigue quantification index; e_iEMG represents the information entropy of the first fatigue quantification index; e_iEMG represents the information entropy of the second fatigue quantification index; and e_MF represents the information entropy of the third fatigue quantification index.

[0114] Therefore, the influence of the rate of change of each physiological parameter on the comprehensive fatigue index was analyzed and determined, and the respective weighting parameter λ was determined. RMS The value is 0.2 to 0.6, λ iEMG The value is 0.3 to 0.5, λ MF It ranges from 0.5 to 0.6.

[0115] It should be noted that the above is only an optional example and should not be construed as a limitation of this application.

[0116] Compared with existing technologies, this application deploys an electromyography (EMG) sensor on at least one muscle related to the operation of power tools by a person. The sensor continuously collects EMG signals from the person operating the power tools in a simulated test scenario. The collected EMG signals are preprocessed and anomaly corrected to determine the start and end points of the EMG signals for each movement, achieving accurate detection of these points. Through multi-dimensional feature extraction, multiple fatigue quantification indicators are obtained. The differences between these indicators across specified movement segments are determined, and a comprehensive fatigue assessment of the person is calculated based on preset weight parameters. This achieves more effective and accurate identification and quantification of fatigue levels, realizing multi-dimensional fusion-based fatigue quantification.

[0117] The following is combined with Figure 9 Detailed description of optional embodiments of the device in this application.

[0118] This application also provides a device for quantitatively evaluating fatigue of personnel operating power tools, which performs the quantitative evaluation method for evaluating fatigue of personnel operating power tools described in this application.

[0119] like Figure 9 As shown, the fatigue measurement and evaluation device 500 for personnel operating power tools includes a selection processing module 510, an operation processing module 530, an acquisition processing module 530, a first determination module 540, an extraction processing module 550, and a second determination module 560.

[0120] Specifically, the selection processing module 510 is used to select at least one muscle related to the operation of power tools by a person to deploy electromyography (EMG) sensors, and determine the sensor deployment positions corresponding to each muscle. The operation processing module 520 is used to set up a test scenario according to specific environmental and operational conditions, select a test subject, and have the test subject operate a specified power tool under the test scenario; the test scenario includes a space operation environment test scenario. The acquisition processing module 530 is used to continuously acquire the EMG signals of the test subject when operating the specified power tool. The first determination module 540 is used to preprocess and correct anomalies in the acquired EMG signals, and determine the start and end points of the EMG signals corresponding to each movement of the test subject when operating the specified power tool. The extraction processing module 550 is used to extract multi-dimensional features from the EMG signals with determined start and end points to obtain multiple fatigue quantification indicators. The second determination module 560 determines the difference between each fatigue quantification indicator in the specified movement segment based on the extracted multiple fatigue quantification indicators, and calculates the comprehensive fatigue assessment of the test subject according to preset weight parameters.

[0121] According to an optional implementation, determining the start and end points of electromyographic signals corresponding to each action of the person under test when operating a specified power tool includes: setting a sliding window with a specific number of sampling points, using a specified multiple of the signal standard deviation as an effective signal threshold, and filtering action signal regions and non-action signal regions; based on a dynamically adjusted window threshold, setting a rule that a specified number of consecutive windows satisfying the action conditions is the action start determination rule, and a specified number of consecutive windows not satisfying the action conditions is the action end determination rule, so as to determine each action related to the operation of the specified power tool.

[0122] According to an optional implementation, the window threshold of the current window is dynamically adjusted by determining whether the number of actions determined based on the filtered action signal region and non-action signal region is equal to the number of recorded actual actions.

[0123] When the number of actions determined based on the filtered action signal region and non-action signal region is less than the actual number of recorded actions, the value of the specified multiple is reduced or the specific number of sampling points is increased.

[0124] When the number of actions determined based on the filtered action signal region and non-action signal region is greater than the actual number of recorded actions, the value of the specified multiple is increased or the specific number of sampling points is decreased.

[0125] According to an optional implementation, the difference between specified action segments is determined based on the extracted multiple fatigue quantification indicators, and the comprehensive fatigue assessment of the person being tested is calculated based on preset weight parameters.

[0126] The comprehensive fatigue assessment value of the test subject's p-th action is calculated using the following expression:

[0127]

[0128] Wherein, CFI represents the comprehensive fatigue assessment score of the subject; m represents the number of muscles in the selected subject. This represents the quantitative characterization index of the first fatigue measure of the i-th muscle in the p-th movement segment of the test subject. This represents the quantitative characterization index of the first fatigue measure of the first movement segment of the i-th muscle in the subject of the test. This represents the second quantitative characterization index of fatigue in the p-th movement segment of the i-th muscle of the test subject. This represents the second fatigue quantification index of the first movement segment of the i-th muscle in the subject of the test. This represents the third quantitative characterization index of fatigue in the i-th muscle during the p-th movement segment of the test subject. λ represents the quantitative characterization index of fatigue in the first movement segment of the i-th muscle of the test subject; RMS , λ EMG and λ MF These represent the first weight parameter, the second weight parameter, and the third weight parameter corresponding to the first fatigue quantification index, the second fatigue quantification index, and the third fatigue quantification index, respectively.

[0129] According to the optional implementation method, the weight of each action sample in the first fatigue quantification index, the second fatigue quantification index, and the third fatigue quantification index is calculated.

[0130] Calculate the information entropy of the first fatigue quantification index, the second fatigue quantification index, and the third fatigue quantification index, in order to further calculate the weight parameters corresponding to the first fatigue quantification index, the second fatigue quantification index, and the third fatigue quantification index.

[0131] According to the optional implementation method, the root mean square of the time domain features, the integral electromyographic value, and the median frequency of the frequency features of the electromyographic signal are extracted as the first, second, and third fatigue quantification indicators of muscle fatigue of the test subject when operating a specified power tool.

[0132] According to an optional implementation, the specified power tool operation includes C tightening and C loosening operations of the screw.

[0133] According to the optional implementation, a space operation environment test scenario is simulated according to the weightlessness state and operating conditions in a specific environment. The simulated space operation environment test scenario includes a glove box containing the gloves of the personnel to be tested, an indoor temperature maintained at 22℃~25℃, a relative humidity controlled at 45%~60%, and no obvious electromagnetic interference sources.

[0134] According to an optional implementation, three specific muscles related to the operation of power tools by a person are selected to deploy electromyography (EMG) sensors. The three EMG sensors are directly attached to the three specific muscles of the person being tested, which include the extensor digitorum, flexor digitorum superficialis, and flexor carpi ulnaris.

[0135] The individuals to be tested are astronauts, and the number of individuals to be tested is greater than 12.

[0136] Compared with existing technologies, this application deploys an electromyography (EMG) sensor on at least one muscle related to the operation of power tools by a person. The sensor continuously collects EMG signals from the person operating the power tools in a simulated test scenario. The collected EMG signals are preprocessed and anomaly corrected to determine the start and end points of the EMG signals for each movement, achieving accurate detection of these points. Through multi-dimensional feature extraction, multiple fatigue quantification indicators are obtained. The differences between these indicators across specified movement segments are determined, and a comprehensive fatigue assessment of the person is calculated based on preset weight parameters. This achieves more effective and accurate identification and quantification of fatigue levels, realizing multi-dimensional fusion-based fatigue quantification.

[0137] like Figure 10 As shown, this embodiment provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the method steps described in the above embodiment.

[0138] This application provides a non-volatile computer storage medium storing computer-executable instructions that can perform the steps described in the above embodiments.

[0139] The following is for reference. Figure 10The diagram illustrates a structural schematic of an electronic device suitable for implementing the embodiments of this application. The terminal devices in the embodiments of this application may include, but are not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 10 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0140] like Figure 10 As shown, the electronic device may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 401, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 402 or a program loaded from a storage device 408 into a random access memory (RAM) 403. The RAM 403 also stores various programs and data required for the operation of the electronic device. The processing unit 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0141] Typically, the following devices can be connected to I / O interface 405: input devices 406 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 407 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 408 including, for example, magnetic tapes, hard disks, etc.; and communication devices 409. Communication device 409 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 10 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown. More or fewer devices may be implemented or have alternatively.

[0142] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 409, or installed from a storage device 408, or installed from a ROM 402. When the computer program is executed by the processing device 401, it performs the functions defined in the methods of the embodiments of this application.

[0143] It should be noted that the computer-readable medium described above in this application can be a computer-readable signal medium, a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0144] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0145] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0146] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0147] The units described in the embodiments of this application can be implemented in software or hardware. The names of the units are not, in some cases, limiting the scope of the unit itself.

Claims

1. A quantitative evaluation method for measuring fatigue of personnel operating power tools, characterized in that, include: Select at least one muscle associated with the operation of power tools to deploy an electromyography sensor; Test scenarios are set up according to specific environments and operating conditions, test subjects are selected, and the test subjects operate designated power tools in the test scenarios; the test scenarios include space operation environment test scenarios; Using the deployed electromyography (EMG) sensors, the electromyographic signals of the muscles of the person being tested are continuously collected when operating a specified power tool. The collected electromyographic signals are preprocessed and anomaly corrected to determine the start and end points of the electromyographic signals corresponding to each action of the subject when operating the specified power tools. A sliding window with a specific number of sampling points is set, and the effective signal threshold is used as a multiple of the signal standard deviation to filter out active signal regions and non-active signal regions. Based on a dynamically adjusted window threshold, a rule is set that a specified number of consecutive windows satisfying the action condition is used as the action initiation judgment rule, and a specified number of consecutive windows failing to satisfy the action condition is used as the action termination judgment rule, to determine each action related to a specified power tool operation. The window threshold is dynamically adjusted by determining whether the number of actions determined based on the filtered action signal area and non-action signal area is equal to the recorded actual number of actions. Specifically, when the number of actions determined based on the filtered action signal area and non-action signal area is less than the recorded actual number of actions, the value of the specified multiple is decreased or the specific number of sampling points is increased; when the number of actions determined based on the filtered action signal area and non-action signal area is greater than the recorded actual number of actions, the value of the specified multiple is increased or the specific number of sampling points is decreased. Multidimensional feature extraction was performed on the electromyographic signals with determined origin and termination points to obtain multiple fatigue quantification indicators. The root mean square time-domain feature, integral electromyographic value, and median frequency of the electromyographic signal were extracted as the first, second, and third fatigue quantification indicators of muscle fatigue of the test subject when operating a specified power tool. Based on the extracted multiple fatigue quantification indicators, the difference between each fatigue quantification indicator and the specified action segment is determined, and the comprehensive fatigue assessment of the test subject is calculated according to the preset weight parameters.

2. The method for quantitatively evaluating fatigue of personnel operating power tools according to claim 1, characterized in that, The step of determining the difference between specified action segments based on the extracted multiple fatigue quantification indicators, and calculating the comprehensive fatigue assessment of the test subject according to preset weight parameters, includes: The comprehensive fatigue assessment value of the p-th action segment of the test subject is calculated using the following expression: ， Wherein, CFI represents the comprehensive fatigue assessment score of the subject; m represents the number of muscles in the selected subject. Indicates the number of the person to be tested i The first quantitative characterization index of fatigue in the p-th movement segment of a muscle group. Indicates the number of the person to be tested i The first fatigue quantification index for the first motor segment of a muscle mass. Indicates the number of the person to be tested i The second fatigue quantification index for the p-th movement segment of a muscle group. Indicates the number of the person to be tested i The second fatigue quantification index for the first motor segment of a muscle mass. Indicates the number of the person to be tested i The third fatigue quantification index for the p-th movement segment of a muscle group. Indicates the number of the person to be tested i The third fatigue quantification index for the first motor segment of a muscle mass. λ RMS , λ iEMG and λ MF These represent the first weight parameter, the second weight parameter, and the third weight parameter corresponding to the first fatigue quantification index, the second fatigue quantification index, and the third fatigue quantification index, respectively.

3. The method for quantitatively evaluating fatigue of personnel operating power tools according to claim 2, characterized in that, include: Calculate the weight of each action sample in the first, second, and third fatigue quantification indicators; Calculate the information entropy of the first fatigue quantification index, the second fatigue quantification index, and the third fatigue quantification index, in order to further calculate the weight parameters corresponding to the first fatigue quantification index, the second fatigue quantification index, and the third fatigue quantification index.

4. The method for quantitatively evaluating fatigue of personnel operating power tools according to claim 1, characterized in that, The specified power tool operation includes C tightening and C loosening operations of the screws.

5. The method for quantitatively evaluating fatigue of personnel operating power tools according to claim 1, characterized in that, include: The simulated space operation environment test scenario is based on the weightlessness and operating conditions in a specific environment. The simulated space operation environment test scenario includes a glove box containing the gloves of the personnel to be tested, an indoor temperature maintained at 22℃~25℃, a relative humidity controlled at 45%~60%, and no obvious electromagnetic interference sources.

6. The method for quantitatively evaluating fatigue of personnel operating power tools according to claim 1, characterized in that, include: Select three specific muscles related to the operation of power tools by the personnel to deploy electromyography (EMG) sensors. The three EMG sensors are directly attached to the three specific muscles of the personnel being tested. The three specific muscles include the extensor digitorum, flexor digitorum superficialis, and flexor carpi ulnaris. The individuals to be tested are astronauts, and the number of individuals to be tested is greater than 12.

7. A quantitative evaluation device for measuring fatigue of personnel operating power tools, characterized in that, The method for quantitatively evaluating fatigue of personnel operating power tools according to any one of claims 1 to 6, wherein the device for quantitatively evaluating fatigue of personnel operating power tools comprises: The selection processing module is used to select at least one muscle related to the operation of power tools by the operator to deploy an electromyography sensor, and to determine the sensor deployment location corresponding to each muscle. The operation processing module is used to set up test scenarios according to specific environments and operating conditions, select test subjects, and enable the test subjects to operate specified power tools in the test scenarios; the test scenarios include space operation environment test scenarios. The data acquisition and processing module is used to continuously acquire the electromuscular signals of the person under test when operating a specified power tool. The first determining module is used to preprocess and correct anomalies in the collected electromyographic signals to determine the start and end points of the electromyographic signals corresponding to each action of the person under test when operating the specified power tools. The extraction and processing module is used to extract multi-dimensional features from the electromyographic signals that have determined the start and end points of the muscle signals, so as to obtain multiple quantitative characterization indicators of fatigue. The second determining module determines the difference between each fatigue quantification index and the specified action segment based on the extracted multiple fatigue quantification indexes, and calculates the comprehensive fatigue assessment of the person being tested based on preset weight parameters.

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