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RESEARCH PRODUCT
Combining Ergonomic Risk Assessment (RULA) with Inertial Motion Capture Technology in Dentistry—Using the Benefits from Two Worlds
Fabian HolzgreveChristian Maurer-grubingerDavid A. GronebergDoerthe BrueggmannDaniela OhlendorfWerner BetzEileen M. WankeAlbert NienhausChristina ErbeLaura Fraeulinsubject
MaleTechnologyErgonomic riskInertial motion captureComputer sciencekinematic analysisDentistryContext (language use)TP1-1185Kinematicsdental treatment conceptwork place evaluationRisk AssessmentBiochemistryArticleAnalytical ChemistryUpper ExtremityScore distribution03 medical and health sciences0302 clinical medicineInertial measurement unitHumans0501 psychology and cognitive sciencesMusculoskeletal DiseasesElectrical and Electronic EngineeringInstrumentation050107 human factorsMaxillofacial surgeonsbusiness.industryChemical technologywearable sensorsdentist05 social sciencesWork (physics)030210 environmental & occupational healthAtomic and Molecular Physics and OpticsOccupational DiseasesergonomicsDentistryinertial motion unitsFemaledental assistantbusinesshuman factorsdescription
Traditional ergonomic risk assessment tools such as the Rapid Upper Limb Assessment (RULA) are often not sensitive enough to evaluate well-optimized work routines. An implementation of kinematic data captured by inertial sensors is applied to compare two work routines in dentistry. The surgical dental treatment was performed in two different conditions, which were recorded by means of inertial sensors (Xsens MVN Link). For this purpose, 15 (12 males/3 females) oral and maxillofacial surgeons took part in the study. Data were post processed with costume written MATLAB® routines, including a full implementation of RULA (slightly adjusted to dentistry). For an in-depth comparison, five newly introduced levels of complexity of the RULA analysis were applied, i.e., from lowest complexity to highest: (1) RULA score, (2) relative RULA score distribution, (3) RULA steps score, (4) relative RULA steps score occurrence, and (5) relative angle distribution. With increasing complexity, the number of variables times (the number of resolvable units per variable) increased. In our example, only significant differences between the treatment concepts were observed at levels that are more complex: the relative RULA step score occurrence and the relative angle distribution (level 4 + 5). With the presented approach, an objective and detailed ergonomic analysis is possible. The data-driven approach adds significant additional context to the RULA score evaluation. The presented method captures data, evaluates the full task cycle, and allows different levels of analysis. These points are a clear benefit to a standard, manual assessment of one main body position during a working task.
year | journal | country | edition | language |
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2021-06-01 | Sensors (Basel, Switzerland) |