WO2018042407A1 - Système d'assistance pour l'estimation du risque de développer des blessures au travail touchant le poignet par effort répétitif - Google Patents

Système d'assistance pour l'estimation du risque de développer des blessures au travail touchant le poignet par effort répétitif Download PDF

Info

Publication number
WO2018042407A1
WO2018042407A1 PCT/IB2017/055348 IB2017055348W WO2018042407A1 WO 2018042407 A1 WO2018042407 A1 WO 2018042407A1 IB 2017055348 W IB2017055348 W IB 2017055348W WO 2018042407 A1 WO2018042407 A1 WO 2018042407A1
Authority
WO
WIPO (PCT)
Prior art keywords
effort
risk
estimating
support system
repetitive
Prior art date
Application number
PCT/IB2017/055348
Other languages
English (en)
Spanish (es)
Inventor
Diego MARTINEZ CASTRO
Cristian Alberto SALAZAR DURAN
Original Assignee
Universidad Autonoma De Occidente
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Universidad Autonoma De Occidente filed Critical Universidad Autonoma De Occidente
Publication of WO2018042407A1 publication Critical patent/WO2018042407A1/fr

Links

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons

Definitions

  • 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.
  • 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.
  • US6334852 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.
  • 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.
  • 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.
  • 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.
  • 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:
  • 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.
  • 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.
  • MMG mechanomyography
  • EMG electromyography
  • Power supply (14) responsible for supplying all circuits
  • 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.
  • 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.
  • 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).
  • a process is performed of amplification by means of a non-inverting amplifier (23).
  • 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.
  • the conditioned signal is connected by cable to the embedded system for further processing.
  • FIG. 3 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;
  • MMG mechanomyography
  • 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.
  • 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.
  • 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.
  • 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.
  • the Fast Fourier Transform FFT
  • the frequencies in which the highest intensity was reported between the two axes are detected.
  • 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 EMG signals (20), figure 2, and the motion detector (30), figure 3 are also used.
  • EMG 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.
  • 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.

Abstract

L'invention concerne un système d'assistance pour l'estimation du risque de développer des blessures au travail touchant le poignet par effort répétitif qui comprend un sous-système de surveillance, un sous-système de traitement et un sous-système d'enregistrement et de visualisation. Le sous-système de surveillance comprend des transducteurs et des étapes de conditionnement électronique de signaux et de capture et le sous-système de traitement est un système intégré et synthétisé dans un élément matériel ou logiciel, qui fusionne l'information surveillée pour obtenir des facteurs combinés considérés comme hautement influents dans le développement de ce type de blessures.
PCT/IB2017/055348 2016-09-05 2017-09-05 Système d'assistance pour l'estimation du risque de développer des blessures au travail touchant le poignet par effort répétitif WO2018042407A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CONC2016/0001605 2016-09-05
CO2016001605 2016-09-05

Publications (1)

Publication Number Publication Date
WO2018042407A1 true WO2018042407A1 (fr) 2018-03-08

Family

ID=62597060

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/IB2017/055348 WO2018042407A1 (fr) 2016-09-05 2017-09-05 Système d'assistance pour l'estimation du risque de développer des blessures au travail touchant le poignet par effort répétitif

Country Status (1)

Country Link
WO (1) WO2018042407A1 (fr)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2021191784A1 (fr) * 2020-03-24 2021-09-30 Lwt3 S.R.L. Start-Up Costituita A Norma Dell'art. 4 Comma 10 Bis Del Decreto Legge 24 Gennaio 2015, N. 3 Système et procédé de commande d'une interaction entre un opérateur et une machine

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150124566A1 (en) * 2013-10-04 2015-05-07 Thalmic Labs Inc. Systems, articles and methods for wearable electronic devices employing contact sensors

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150124566A1 (en) * 2013-10-04 2015-05-07 Thalmic Labs Inc. Systems, articles and methods for wearable electronic devices employing contact sensors

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
PEPPOLONI LORENZO ET AL.: "Assessment of task ergonomics with an upper limb wearable device. 22nd Mediterranean Conference on Control and Automation", IEEE., 16 June 2014 (2014-06-16), pages 340 - 345, XP032687283, ISBN: 978-1-4799-5900-6 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2021191784A1 (fr) * 2020-03-24 2021-09-30 Lwt3 S.R.L. Start-Up Costituita A Norma Dell'art. 4 Comma 10 Bis Del Decreto Legge 24 Gennaio 2015, N. 3 Système et procédé de commande d'une interaction entre un opérateur et une machine

Similar Documents

Publication Publication Date Title
DK2296545T3 (en) Systems to perform overfladeelektromyografi
GB2519987A (en) Biomechanical activity monitoring
JP4292247B2 (ja) 動作解析装置およびその利用
US20120245482A1 (en) Anesthesia Monitoring Device and Method
WO2004103176A1 (fr) Systeme et procede de diagnostic de fonction d'equilibre
JP2012105762A (ja) バイタルサイン計測装置及び体動検出装置
JP6916858B2 (ja) 筋硬直の測定システム、装置、および方法
US20180049653A1 (en) Monitoring vital signs
EP3010404A1 (fr) Procédé d'indication de la probabilité de crises non épileptiques psychogènes
KR101670134B1 (ko) 어지럼증 진단장치
JP2011512208A5 (fr)
WO2018042407A1 (fr) Système d'assistance pour l'estimation du risque de développer des blessures au travail touchant le poignet par effort répétitif
JP2019517371A (ja) 脈拍特性の検出と信頼性のある捕捉のための装置
Allen et al. Evaluation of fall risk for post-stroke patients using bluetooth low-energy wireless sensor
Bhavana et al. Techniques of measurement for Parkinson's tremor highlighting advantages of embedded IMU over EMG
Chan et al. An in–laboratory validity and reliability tested system for quantifying hand–arm tremor in motions
Seçkin Multi-sensor glove design and bio-signal data collection
Sümbül et al. Estimating the value of the volume from acceleration on the diaphragm movements during breathing
Allen et al. Telemedicine assessment of fall risk using wireless sensors
JP2011250945A (ja) 歩行解析方法、歩行解析システム及び歩行解析装置
Miletić et al. Validation of the new wearable instrument for the pendulum test based on inertial sensors
Solihin et al. Design of an electromyograph equipped with digital neck angle elevation gauge
RU79239U1 (ru) Система регистрации пальцевого тремора
Lin et al. Assessment of range of joint motion using Kinect
TWI581757B (zh) 關節活動品質估測系統及方法

Legal Events

Date Code Title Description
DPE2 Request for preliminary examination filed before expiration of 19th month from priority date (pct application filed from 20040101)
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 17845653

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 17845653

Country of ref document: EP

Kind code of ref document: A1