Postural inaccuracies in persistent dental tasks indicated an upsurge in the prevalence of musculoskeletal disorders in dentists. This makes it imperative to restrain awkward postural movements while working. Biased results in self-reporting surveys; discomfort, expense, and time consumption involved in using wearable sensors; and expert’s opinion are required in observational methods. Hence, it is important to use significantly reliable, cheap technology as a substitute to overcome the shortcomings of the mentioned techniques. In this study, the markerless Kinect V2–based system was developed and compared with the conventional imaging technique for real-time postural assessment of dental seating tasks. The study assessed the angle parameters related to the dentist’s bodily movement of upper arm, lower arm, wrist, neck, and trunk. Ten dentists from the local dental institution volunteered for the study. Dentists were monitored with both techniques while performing real-time dental procedures. The agreement between the techniques was assessed using Bland–Altman plot at 95% bias, Pearson’s (r1) and concordance (r2) correlation coefficients, mean difference, and percentage error. For conclusive agreement analysis, contingency coefficient, proportion agreement index, Cohen’s kappa, and Mann–Whitney U at 95% confidence interval (CI) were evaluated. Data acquired from both techniques possessed strong correlations (r1 and r2 >0.90). Good agreement in Rapid Upper Limb Assessment data using Cohen’s kappa (0.67) at standard Landis and Koch’s scale was also observed. Postural analysis of slow-motion tasks like dentistry using the Kinect V2 system proved to be unobtrusive and efficient. This may be used by dentists to have periodic postural check. In future, Kinect V2–based feedback system may be used to develop an assistive technology using predictive algorithms, which may help in reducing the probability of occurrence of work-related musculoskeletal disorders in dentists.

中文翻译：

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在牙科就座任务中测量姿势参数的成像和新型无标记方法的比较分析

持续牙科任务中的姿势不准确表明牙医中肌肉骨骼疾病的患病率激增。这使得在工作时抑制笨拙的姿势运动势在必行。自我报告调查中的偏见结果；使用可穿戴传感器所涉及的不适、费用和时间消耗；观察方法需要专家的意见。因此，重要的是使用非常可靠、廉价的技术作为替代来克服上述技术的缺点。在这项研究中，开发了基于 Kinect V2 的无标记系统，并与传统成像技术进行了比较，以对牙科坐位任务进行实时姿势评估。该研究评估了与牙医上臂、下臂、手腕、颈部和躯干的身体运动相关的角度参数。来自当地牙科机构的十名牙医自愿参加了这项研究。牙医在进行实时牙科手术时会使用这两种技术进行监控。使用 95% 偏倚的 Bland-Altman 图、Pearson (r1) 和一致性 (r2) 相关系数、平均差和百分比误差来评估技术之间的一致性。对于结论性一致性分析，评估了列联系数、比例一致性指数、Cohen's kappa 和 Mann-Whitney U 在 95% 置信区间 (CI)。从这两种技术获得的数据具有很强的相关性（r1 和 r2 > 0.90）。还观察到使用 Cohen's kappa (0.67) 在标准 Landis 和 Koch's 量表下的快速上肢评估数据具有良好的一致性。事实证明，使用 Kinect V2 系统对牙科等慢动作任务进行姿势分析是不显眼且高效的。牙医可以使用它来进行定期姿势检查。未来，基于 Kinect V2 的反馈系统可用于开发使用预测算法的辅助技术，这可能有助于降低牙医发生与工作相关的肌肉骨骼疾病的可能性。