OBJECTIVES: Existing methods for practically evaluating musculoskeletal exposures such as posture and repetition in workplace settings have limitations. We aimed to automate the estimation of parameters in the revised United States National Institute for Occupational Safety and Health (NIOSH) lifting equation, a standard manual observational tool used to evaluate back injury risk related to lifting in workplace settings, using depth camera (Microsoft Kinect) and skeleton algorithm technology. METHODS: A large dataset (approximately 22,000 frames, derived from six subjects) of simultaneous lifting and other motions recorded in a laboratory setting using the Kinect (Microsoft Corporation, Redmond, Washington, United States) and a standard optical motion capture system (Qualysis, Qualysis Motion Capture Systems, Qualysis AB, Sweden) was assembled. Error-correction regression models were developed to improve the accuracy of NIOSH lifting equation parameters estimated from the Kinect skeleton. Kinect-Qualysis errors were modelled using gradient boosted regression trees with a Huber loss function. Models were trained on data from all but one subject and tested on the excluded subject. Finally, models were tested on three lifting trials performed by subjects not involved in the generation of the model-building dataset. RESULTS: Error-correction appears to produce estimates for NIOSH lifting equation parameters that are more accurate than those derived from the Microsoft Kinect algorithm alone. Our error-correction models substantially decreased the variance of parameter errors. In general, the Kinect underestimated parameters, and modelling reduced this bias, particularly for more biased estimates. Use of the raw Kinect skeleton model tended to result in falsely high safe recommended weight limits of loads, whereas error-corrected models gave more conservative, protective estimates. CONCLUSIONS: Our results suggest that it may be possible to produce reasonable estimates of posture and temporal elements of tasks such as task frequency in an automated fashion, although these findings should be confirmed in a larger study. Further work is needed to incorporate force assessments and address workplace feasibility challenges. We anticipate that this approach could ultimately be used to perform large-scale musculoskeletal exposure assessment not only for research but also to provide real-time feedback to workers and employers during work method improvement activities and employee training.
Automation* ; Back Injuries ; Back Pain ; Bias (Epidemiology) ; Dataset ; Human Engineering ; Lifting* ; National Institute for Occupational Safety and Health (U.S.) ; Posture ; Skeleton ; Trees ; United States ; Washington

Even though the combined use of ultrasound (US) and alpha-fetoprotein (AFP) is recommended for the surveillance of hepatocellular carcinoma (HCC), the utilization of AFP has its challenges, including accuracy dependent on its cut-off levels, degree of liver necroinflammation, and etiology of liver disease. Though various studies have demonstrated the utility of protein induced by vitamin K absence II (PIVKA-II) in surveillance, treatment monitoring, and predicting recurrence, it is still not recommended as a routine biomarker test. A panel of 17 experts from Asia-Pacific, gathered to discuss and reach a consensus on the clinical usefulness and value of PIVKA-II for the surveillance and treatment monitoring of HCC, based on six predetermined statements. The experts agreed that PIVKA-II was valuable in the detection of HCC in AFP-negative patients, and could potentially benefit detection of early HCC in combination with AFP. PIVKA-II is clinically useful for monitoring curative and intra-arterial locoregional treatments, outcomes, and recurrence, and could potentially predict microvascular invasion risk and facilitate patient selection for liver transplant. However, combining PIVKA-II with US and AFP for HCC surveillance, including small HCC, still requires more evidence, whilst its role in detecting AFP-negative HCC will potentially increase as more patients are treated for hepatitis-related HCC. PIVKA-II in combination with AFP and US has a clinical role in the Asia-Pacific region for surveillance. However, implementation of PIVKA-II in the region will have some challenges, such as requiring standardization of cut-off values, its cost-effectiveness and improving awareness among healthcare providers.