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-Johnson York 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:

EMG Analysis Custom software performed : –Link in time with video file –EMG calibration –Amplitude Probability Distribution Function (APDF) at 10 th, 50 th, and 90 th 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)

Task Identification and Concatonation Process

Results

Keyboarding – Static EMG

Keyboarding - Gaptime * *

Mousing – Static EMG

Mousing – Peak EMG

Phone – Static EMG

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 R010H Center for VDT & Health Research Toronto Star Southern Ontario Newspaper Guild