A hand speed-duty cycle equation for estimating the ACGIH hand activity level rating.

An equation was developed for estimating hand activity level (HAL) directly from tracked root mean square (RMS) hand speed (S) and duty cycle (D). Table lookup, equation or marker-less video tracking can estimate HAL from motion/exertion frequency (F) and D. Since automatically estimating F is sometimes complex, HAL may be more readily assessed using S. Hands from 33 videos originally used for the HAL rating were tracked to estimate S, scaled relative to hand breadth (HB), and single-frame analysis was used to measure D. Since HBs were unknown, a Monte Carlo method was employed for iteratively estimating the regression coefficients from US Army anthropometry survey data. The equation: HAL = 10[e(-15:87+0:02D+2:25 ln S)/(1+e(-15:87+0:02D+2:25 ln S)], R(2) = 0.97, had a residual range ± 0.5 HAL. The S equation superiorly fits the Latko et al. ( 1997 ) data and predicted independently observed HAL values (Harris 2011) better (MSE = 0.16) than the F equation (MSE = 1.28).

Evaluation of Hands-On Clinical Exam Performance Using Marker-less Video Tracking.

This study investigates the potential of using marker-less video tracking of the hands for evaluating hands-on clinical skills. Experienced family practitioners attending a national conference were recruited and asked to conduct a breast examination on a simulator that simulates different clinical presentations. Videos were made of the clinician's hands during the exam and video processing software for tracking hand motion to quantify hand motion kinematics was used. Practitioner motion patterns indicated consistent behavior of participants across multiple pathologies. Different pathologies exhibited characteristic motion patterns in the aggregate at specific parts of an exam, indicating consistent inter-participant behavior. Marker-less video kinematic tracking therefore shows promise in discriminating between different examination procedures, clinicians, and pathologies.