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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).

This study investigates the potential of using marker-less video tracking 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 presents different clinical pathologies. Videos were taken of the clinician’s hands during the exam. Video processing software for tracking and quantifying hand motion kinematics was used. Videos were divided into two segments: a general search segment and a mass exploration segment. The general exploration segments exhibited motion patterns which included 72% faster movement and 73% higher acceleration across clinical pathologies. The most complex pathology exhibited 14% greater displacement for pressing/rubbing than for general exploration. Marker-less video kinematic tracking shows promise in discriminating between different examination procedures, clinicians, and pathologies.