WO2018042407A1

WO2018042407A1 - System for assisting with risk assessment relating to the development of wrist injuries sustained at work as a result of repetitive strain

Info

Publication number: WO2018042407A1
Application number: PCT/IB2017/055348
Authority: WO
Inventors: Diego MARTINEZ CASTRO; Cristian Alberto SALAZAR DURAN
Original assignee: Universidad Autonoma De Occidente
Priority date: 2016-09-05
Filing date: 2017-09-05
Publication date: 2018-03-08

Abstract

The invention relates to a system for assisting with risk assessment relating to the development of wrist injuries sustained at work as a result of repetitive strain, comprising a monitoring sub-system, a processing sub-system and a recording and viewing sub-system, where the monitoring subsystem comprises transducers and electronic signal conditioning and capture steps and the processing subsystem is a system embedded and synthesised in hardware or in software, which merges the monitored information so as to obtain combined factors considered to have a large impact on the development of this type of injury.

Description

SUPPORT SYSTEM FOR THE ESTIMATION OF THE RISK OF DEVELOPING LABOR INJURIES OF DOLLS BY REPETITIVE EFFORT

FIELD OF THE INVENTION

The present invention is part of the developments aimed at preventing occupational injuries due to repetitive effort. Based on medical studies and methods that support occupational health professionals to perform their analyzes, such as the OCRA Check List, the system developed integrates monitoring and processing subsystems of the most representative variables in the development of wrist injuries.

SUMMARY OF THE INVENTION

The invention is directed to a support system for estimating the risk of developing labor wrist injuries due to repetitive effort, comprising a monitoring subsystem, a processing subsystem and a recording and visualization subsystem; wherein the monitoring subsystem comprises transducers and stages of electronic signal conditioning and capture, and the processing subsystem is a system embedded and synthesized in hardware or software.

STATE OF THE TECHNIQUE At the industrial level, studies of jobs are carried out with the purpose of assessing the risk of a worker in developing some type of work injury that affects his physical condition. These studies are supported in the observation of the development of the activity and the application of surveys, obtaining subjective results with a high percentage of error.

The least impact on the treatment of repetitive strain injury problems is achieved through prevention and non-detection methods. Early pathologies. The methods currently used are mainly based on factors weighted to perception and not by precise quantitative measurement of variables related to the development of lesions. Among the most influential factors in the case of repetitive stress injuries are joint movements, joint movements with effort, excessive application of force, and the use of vibrating tools. In parallel, electronic devices capable of detecting and monitoring different factors of interest related to this type of injury have been developed.

One of the devices found is that presented in US6334852, which uses Hall effect sensors adapted to a clothing that is fitted in the hand and forearm, and is capable of monitoring and detecting articular movements of the wrist without inhibiting movement natural hand; However, it only detects the movement factor and not the other factors of interest related to this type of injury.

Devices capable of wirelessly monitoring muscle activity by means of EMG processing have also been developed without affecting the natural mobility of the arm in work settings, among them is the device presented in US4807642, which compares the EMG signal obtained from the user with a reference signal, so that if the signal resembles the reference the device generates an acoustic signal in order to indicate to the user that he is performing a dangerous muscular action. Another device that uses EMG signal is the one described in WO2004 / 098406A1 which measures the inferred force of a subject in an object, and has storage in a database and allows to analyze the results through a user interface on a screen, by means of which it shows the user the EMG signal in real time. These devices only monitor one influencing factor, the other factors are not monitored. Due to the above, it is clear that there is still a need in the art for a support system to estimate the risk of developing wrist injuries due to repetitive effort, which is capable of simultaneously monitoring all risk factors in the wrist, in order to then be presented to the person in charge of making decisions regarding the risk to which a worker may be exposed.

The present invention is conditioned on a subject of possible occupational risk and during a real work session provides information related to the risk to which he is exposed in relation to the development of a wrist injury, which is of great interest for decision-making. that contribute to mitigate this type of injury. Unlike traditional methods, the system developed in the present invention relies on real measurements of said variables during a work session, providing objective and more precise analyzes of the risk of developing an injury, where the selection of the measured variables and the processing of them, allows to merge basic information to generate new results according to medical studies and evaluation methods to know the risk of developing wrist injuries. Additionally, the system allows to visualize and record the session measurements and the results obtained from the processing.

DETAILED DESCRIPTION OF THE DRAWINGS FIG. 1 illustrates the monitoring subsystem developed and its conditioned distribution to a subject;

FIG. 2 teaches the electromyography capture module according to the present invention;

FIG 3 shows the motion detector module of the monitoring subsystem according to the present invention;

FIG. 4 teaches the mechanomyography capture module; FIG. 5 schematically shows the transmission of information from the processing subsystem to the recording and display subsystem,

FIG. 6 shows the interface for displaying captured data and processing results.

DETAILED DESCRIPTION OF THE INVENTION

The present invention reports a system capable of monitoring the variables associated with factors determined as influential in the development of work injuries due to repetitive strain on the wrist while the worker performs his work activity, and generate factors and indices related to the risk of developing injuries. The factors considered as influential are repetitiveness, vibration and effort; These factors were chosen as the most influential based on the prevention method most used for this type of analysis, Check List OCRA, and a document made by Bruce Bernard for the NIOSH, in which a review of the different official investigations is presented which involved tests to show if the variables force, repetitiveness, vibration and position generated or not the carpal tunnel syndrome.

The system according to the present invention is composed of 3 subsystems:

• Monitoring subsystem;

• Processing subsystem; Y

• Registration and display subsystem. Monitoring subsystem In accordance with the present invention, the monitoring subsystem (10) is constituted by transducers and electronic signal conditioning and capture stages, which allow to record the muscular activity, the acceleration and angular velocity of the hand and the muscle vibration. The subsystem is installed in a clothing on the upper limb of the person to be analyzed as shown in Figure 1. In this way it is achieved that the upper member of the user moves freely, facilitating the analysis during a work session. The monitoring activity can be carried out for as long as the user wishes, for example, in a range between 1 minute to 8 hours, consecutively or interleaved.

As illustrated in Figure 1, the subsystem (10) comprises an apparel (1 1) which has a rear face that matches the back of the hand and an anterior part that matches the palm of the user's hand. On the back of the clothing (1 1), there is a module that allows to detect muscle vibration (12) by mechanomyography (MMG), a module for measuring muscle activity (13) by electromyography (EMG), a module Power supply (14) responsible for supplying all circuits, an embedded device (15) in which the functions that process the captured data are implemented, which can be implemented in technologies such as microcontrollers or FPGA, and an arrangement of two Acceleration and angular velocity measurement modules located (16) (Accel and gyro forearm) and (17) (Accel and gyro hand), on the clothing (1 1) where the module (17) is located at the top of the hand and the module (16) is located in the lower part of the forearm.

The subsystem (10) is based on electromyography, which is a process to detect the bio-potentiality of muscles when contracted. The sensors used to measure EMG are surface electrodes, which are located in the lower part of the forearm (18). The architectural diagram of the circuit for obtaining EMG (20) is shown in Figure 2. By means of surface electrodes the electrical signals that are acquired are acquired. amplified through an instrumentation amplifier (21), the signal is then filtered by an analog band-pass filter (22) with a cut-off frequency of 20 Hz and 8 kHz. The processing devices handle unipolar tensions, for this reason a rectification process (24) is carried out using a diode which decreases the level of the signal by the threshold voltage of this element, therefore, prior to this stage a process is performed of amplification by means of a non-inverting amplifier (23). Depending on the force exerted a certain amount of muscle fibers are activated, this muscle activation is captured by the electrodes increasing or decreasing the amplitude of the EMG signal proportionally depending on the force exerted, therefore for this measurement only the amplitude of the signal but not its frequency, for which a configuration is implemented that allows obtaining the envelope of the signal (25). An analog low-pass filter with a cut-off frequency of 7 Hz is used to limit the bandwidth of the signal to that of the envelope. Finally, the conditioned signal is connected by cable to the embedded system for further processing.

Another component of the monitoring subsystem (10) is that shown in Figure 3 which represents the functional diagram of the motion detector module, which is composed of accelerometers and gyroscopes (31). 4 devices are used; 2 on the back of the clothing (1 1), figure 1, at the location (16) and 2 devices in the upper hand area at the location (17). The information provided by these transducers is sent to the embedded system.

The detection of muscle vibration is performed by means of the module (12) that makes a process of mechanomyography (MMG), figure 1; For this, a microphone is used in contact with the skin on the forearm muscle that you want to monitor. The architectural diagram of the mechanomyography capture module is shown in Figure 4 where the mechanomyography allows to detect the muscular activity by means of the mechanical vibrations that occur when the muscle is contracted. For this, an electret microphone is used in contact with the area to be sensed (41). The microphone needs an adaptation circuit, in addition to the electronics necessary for coupling with the embedded system where the information supplied by this transducer is processed. The MMG signal has relevant information from 5 Hz, so a high pass analog filter (42) is used with that cutoff frequency. Subsequently, an amplification process is carried out by means of a non-inverting amplifier (43). Since the signal is still bipolar up to that stage, a process of adding a DC signal (44) is then carried out since the negative component also provides relevant information. The MMG signal does not contain information after 20 Hz, therefore a low-pass filter (45) is performed with that cutoff frequency. The conditioned signal is connected to the embedded system for further processing.

The embedded module (15) is responsible for digitizing the signals, storing data in a microSD, and wireless transmission.

Processing Subsystem

This subsystem is implemented in an embedded system, which can be synthesized in hardware or software using technologies such as microcontrollers or FPGA. The processing subsystem captures the values of the variables and processes the information through modules that generate as a result the factors quantitative, muscular effort and vibration already quantified, which are calculated as follows: · For the calculation of vibration at the one that is subject to the hand uses a motion detector module on the hand (30), figure 3, which gives the acceleration information in 2 axes. To know the variables intensity and frequency of an external vibration on the hand the Fast Fourier Transform (FFT) is used to obtain the frequency spectrum on each acceleration axis. Once this spectrum is obtained, the frequencies in which the highest intensity was reported between the two axes are detected.

It is also important to know if at the time the vibration was induced large levels of force were also performed, such as, for example, as in the case of a drill drilling wood compared to drilling concrete. For this, the data obtained by the vibration module (17), figure 1, are related to the data acquired by the EMG module (20), figure 2; With this information the level of muscular effort at which the induced vibration occurred was detected.

The data of the vectors that represent the frequency spectrum, the frequencies with greater intensity, and the levels of muscular effort of said frequencies, are stored to be later shown through the Subsystem of recording and visualization.

• The calculation of muscular effort is important to identify the effort required by the person to perform in a particular work activity, so this factor reports the total time in which the different levels of effort were exercised, low, medium and high. The calculation is performed using the EMG signal (20), figure 2; each time the signal is held at an effort level, the respective counter that accumulates the time for that level is activated, if the effort level is changed, the counter related to the new level is activated, and if the previous level is returned, the Counter continues counting from the last number stored, so in the end you get the result of the three counters that represent the total effort time in each level. The three data are transformed to the unit of time measurement and stored for later viewing. • The calculation of the angle of the hand is important to know the different angles reached by the hand while performing an activity, which is related to the posture that the person's hand takes while developing a specific task. The calculation is performed using the motion detector signal which gives the acceleration and angular velocity information in 2 axes. Two sensors are used on the hand (17) and two sensors on the forearm (16) to serve as a reference, figure 1. The relation of these 16 signals allows the calculation of the angle of the hand in 2 axes, figure 3.

• The calculation of repeatability is performed because in the literature this factor is reported as the most influential in the development of pathologies in the wrist, the factor is separated into two actions; dynamic actions (periodic succession of tensions and relaxation of active muscles of short duration) and static actions (contraction of the muscles continued and maintained for a certain period of time). For the calculation of dynamic actions, the data acquired by the MMG module (40) are used, figure 4; The MMG signal varies when a muscular contraction occurs, the repetitive calculation is made by increasing a counter every time a variation in the signal occurs, thus obtaining the amount of dynamic actions exercised over a period of time.

In addition to using the MMG signal, the EMG signals (20), figure 2, and the motion detector (30), figure 3 are also used. By means of the relationship with EMG, false counts are avoided and the level of effort exerted in each dynamic action and the amount of actions exercised, actions with low effort, medium effort and high effort are obtained separately. It should be remembered that dynamic actions with high effort are considered the most influential factor in the development of labor injuries in the wrist. For the calculation of static actions, the data obtained in EMG (20) is used, figure 2; The EMG signal has a level proportional to the effort exerted by the muscle and when holding an object the signal is maintained at a value, this property allows not only to identify when a static action occurs but also at what level of effort was made and the amount of This time lasted. A counter increases every time a static action is detected. This signal is also related to the signal of the motion detector to know how to differentiate whether the action was static or dynamic actions with sustained high effort.

By means of signal processing algorithms the monitored information is merged to obtain combined factors considered as very influential in the development of injuries. The data that are calculated are:

• Dynamic actions:

^■ Dynamic actions with little effort.

^■ Dynamic actions with effort.

• Static actions:

^■ Static actions with little effort.

^■ Static actions with effort.

• Vibration:

^■ Vibration with little effort.

^■ Vibration with effort.

• Effort:

^■ Total time for 3 strength levels (low, medium, high).

• Hand angle: ^■ Hand angle on 2 axes.

Registration and display subsystem.

Finally, the captured and processed data are transmitted (50) to the recording and display subsystem which is implemented in a PC (51), figure 5. The transmission is carried out by radio where the protocol depends on the wireless transmission technology from which available, currently the system developed includes Bluethoot and Zigbee transmitter components that support transmissions. Another option to transfer the data is to manually remove the microSD from the device and enter it directly into a port on the PC.

The risk factors together with the recording of the values of the variables are stored in a database to have a record of the activity and subsequently displayed through a graphical interface, figure 6.

Claims

1. A support system for estimating the risk of developing labor wrist injuries due to repetitive stress characterized in that it comprises a monitoring subsystem; a processing subsystem and a registration and display subsystem.

2. The support system for estimating the risk of developing labor wrist injuries by repetitive effort according to claim 1, characterized in that the monitoring subsystem comprises transducers and stages of electronic signal conditioning and capture.

3. The support system for estimating the risk of developing labor wrist injuries by repetitive effort according to claim 1, characterized in that the processing subsystem is a system embedded and synthesized in a hardware or software.

4. The support system for estimating the risk of developing labor wrist injuries due to repetitive strain according to claim 1, characterized in that the registration and display subsystem comprises Bluethoot and Zigbee transmitter components or a microSD memory.

5. The support system for estimating the risk of developing labor wrist injuries due to repetitive strain according to claim 1 or 2, characterized in that the monitoring subsystem comprises an apparel (1 1) wherein it has a rear face that agrees with the back of the hand and an anterior part that matches the palm of the user's hand and where a module is located on the back of the clothing (11) to detect muscle vibration (12) by mechanomyography ( MMG), a module of measurement of muscle activity (13) by electromyography (EMG), a power source module (14) responsible for supplying all circuits, an embedded device (15) in which the functions that process the captured data are implemented, and an arrangement of two angular velocity and acceleration measurement modules (16) and (17) located on the clothing (11).

6. The support system for estimating the risk of developing labor injuries from repetitive strain according to claim 5, characterized in that the sensors for measuring EMG are surface electrodes located in the lower part of the forearm (18) for obtaining EMG (20) where the electrical signals are amplified through an instrumentation amplifier (21) and subsequently filtered by an analog band-pass filter (22) with a 20 Hz and 8 kHz cutoff frequency and where the Processing devices handle unipolar tensions.

7. The support system for estimating the risk of developing labor wrist injuries due to repetitive stress according to claim 1 to 6, characterized in that the monitoring subsystem further comprises a motion detector module which is composed of accelerometers and gyroscopes ( 31).

8. The support system for estimating the risk of developing labor wrist injuries due to repetitive effort according to claim 1 to 7, characterized in that the processing subsystem captures the values of the variables and processes the information through modules that generate as a result the factors repetitiveness, muscular effort and vibration, where the vibration to which the hand is subjected is measured with a motion detector module on the hand (30) which gives the acceleration information in 2 axes and where the variables intensity and frequency of external vibration on the hand are It is done using the Fast Fourier FFT Transform to obtain the frequency spectrum on each acceleration axis.

9. The support system for estimating the risk of developing labor wrist injuries by repetitive effort according to claim 8, characterized in that the data of the vectors representing the frequency spectrum, the frequencies with greater intensity, and the levels of muscular effort of these frequencies, are stored and displayed through the Subsystem of registration and visualization.

10. The support system for estimating the risk of developing labor wrist injuries by repetitive effort according to claim 1 to 9 characterized in that the muscular effort is calculated using the EMG signal (20) each time the signal is sustained at a low, medium and high effort level activating the respective counter that accumulates the time for that level where, if the effort level is changed, the counter related to the new level is activated, and if the counter is returned to the previous level It continues counting from the last number stored and the result is obtained from the three counters that represent the total effort time at each effort level.

11. The support system for estimating the risk of developing labor wrist injuries by repetitive effort according to claim 10, characterized in that the three data are transformed to the unit of time measurement and stored for later visualization.

12. The support system for estimating the risk of developing labor injuries from repetitive strain according to claim 1 to 11, characterized in that the angle of the hand is determined by the signal of the motion detector which gives the information of acceleration and angular velocity in 2 axes, using two sensors on the hand (17) and two sensors on the forearm (16) as a reference.

13. The support system for estimating the risk of developing labor wrist injuries due to repetitive effort according to claim 1 to 12, characterized in that the repetitiveness factor is determined in two actions: dynamic actions (periodic succession of tensions and relaxations of the active muscles of short duration) and static actions (continuous and sustained muscle contraction for a certain period of time), using the MMG module (40).

14. The support system for estimating the risk of developing labor injuries from repetitive strain according to claim 13, characterized in that in addition to using the MMG signal, the EMG (20) and motion detector signals are also used (30), where false relationships are avoided through the relationship with EMG and the level of effort exerted in each dynamic action is reported and the amount of actions taken, actions with low effort, medium effort and effort are obtained separately tall.

15. The support system for estimating the risk of developing labor wrist injuries by repetitive effort according to claim 13, characterized in that the static actions are determined by using the data obtained in EMG (20).