SEPARATION AND SUMMATION OF EMG RECORDINGS BY TASK USING VIDEO RECORDS

Anne Moore, Richard Wells, Dwayne Van Eerd, Stephen Krajcarski, Melanie Banina, Donald Cole,

and Sheilah Hogg-JohnsonYork University, Toronto, Canada

University of Waterloo, Waterloo, Canada Institute for Work and Health, Toronto Canada

Introduction

Exposure to musculoskeletal loading at work depends on many factors (Wells et al)

-tasks performed, workload

-workstation, equipment, technique-task-time organization

Difficult to separate out the effects of each factor from overall level of musculoskeletal loading

Introduction – cont’d

Combining EMG and task identification using video has shown promising results in industry (Formsan et al, 2002)

Can we differentiate between tasks in an office setting even when these tasks are done within an environment of other tasks that may or may not be done simultaneously?

Methods

33 Participants: –Newspaper advertising and finance employees–Clerical, administration, sales, customer accounts and call centre–10 male/ 23 female

Methods (cont’d)

Electromyographic signals bilaterally from:

–Extensor Carpi Ulnaris Brevis (ECRB)–Trapezius

Recorded using portable EMG system (ME3000P8, Mega Electronics, Finland)

Simultaneous video recording

Protocol

Participant reported to a private room for hook up, signal verification and calibration:

–Maximal shoulder shrug with arms abducted against resistance–Wrist extension with maximal grasp

Participant and researcher returned to participant’s usual workstation

Protocol

EMG and video recorded while participant performed usual job for 2 hours

Subset repeated protocol on a 2nd day (n=20)

Video Analysis

30 minutes of Video chosen for analysis based on:

–Included “mark” for time synchronization–Emphasis on seated work

On/off states of 7 tasks identified while viewing video and simultaneously recorded on computer (Observer Pro 4.0, Noldus Technology, Netherlands)

Video Analysis (cont’d)

Seven states/activities identified:

State “On” Definition

1 Keying On/Off Either hand in contact with keyboard or poised as if to use

2 Mousing On/Off Hand in contact with mouse or poised as if to use

3 Phone On/Off Hand grasps handset of phone

4 DeskworkOn/Off

Visibly working with job relevant items on desk

5 Sitting/Standing Buttocks are in contact with seat pan

6 At desk/Away Subject recognized to be at workstation or in camera view

7 Other On/Off Cannot be assigned to first 4 states and occurs simultaneously

EMG Analysis

Custom software performed:

–Link in time with video file–EMG calibration–Amplitude Probability Distribution Function (APDF) at 10th, 50th, and 90th level (Jonsson, 1982)

–Gaps Analysis (Veiersted et al, 1990)

All analyses performed at:–Whole file level–General Task level (individual and concatonated)–Specific Group level (individual and concatonated)

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Time (min)

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0 1 2 3 4 5 6Time (min)

-10

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EM

G (

% M

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Task Identification and Concatonation Process

Results

Keyboarding – Static EMG

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Rt ECRB Rt Traps Lt ECRB Lt Traps

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ic E

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keyoff

keyon

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*

*

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Keyboarding - Gaptime

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R.ECRB R. Traps L. ECRB L. Traps

Gap

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Keyon*

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Mousing – Static EMG

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Rt ECRB Rt Traps Lt ECRB Lt Traps

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ic E

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mouse off

mouse on

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Mousing – Peak EMG

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Rt ECRB Rt Traps Lt ECRB Lt Traps

Pea

k E

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mouse off

mouse on

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Phone – Static EMG

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R.ECRB R. Traps L. ECRB L. Traps

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ic E

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Phoneoff

Phoneon

Conclusions

Separating EMG by task in the workplace allows examination effects of specific tasks on musculoskeletal load in situ

Conclusions - continued

Use of a mouse is a constrained task that has high static muscle activity and low peak muscle activity in mouse hand

The period of time while keyboarding was marked by significantly higher static loading in both the forearms and shoulders

Acknowledgements

NIOSH/NIH R010H03708-02

Center for VDT & Health Research

Toronto Star

Southern Ontario Newspaper Guild