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Margaret K.Y. Mak, Oron Levin, Joseph Mizrahi and Christina W.Y. Hui-Chan

Calculation of joint torques during the rising phase of sit-to-stand motion is in most cases indeterminate, due to the unknown thighs/chair reaction forces in addition to the other sources of uncertainties such as joint positioning and anthropometric data. In the present study we tested the reliability of computation of the joint torques from a five-segment model; we used force plate data of thighs/chair and feet/ground reaction forces, in addition to kinematic measurements. While solving for joint torques before and after seat-off, differences between model solutions and measured data were calculated and minimized using an iterative algorithm for the reestimation of joint positioning and anthropometric properties. The above method was demonstrated for a group of six normal elderly persons.

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Lisa S. Jutte, Kenneth L. Knight and Blaine C. Long

Objective:

Examine thermocouple model uncertainty (reliability + validity).

Design:

First, a 3 × 3 repeated measures design with independent variables electrothermometers and thermocouple model. Second, a 1 × 3 repeated measures design with independent variable subprobe.

Intervention:

Three electrothermometers, 3 thermocouple models, a multi-sensor probe and a mercury thermometer measured a stable water bath.

Main Outcome Measures:

Temperature and absolute temperature differences between thermocouples and a mercury thermometer.

Results:

Thermocouple uncertainty was greater than manufactures’ claims. For all thermocouple models, validity and reliability were better in the Iso-Themex than the Datalogger, but there were no practical differences between models within an electrothermometers. Validity of multi-sensor probes and thermocouples within a probe were not different but were greater than manufacturers’ claims. Reliability of multiprobes and thermocouples within a probe were within manufacturers claims.

Conclusion:

Thermocouple models vary in reliability and validity. Scientists should test and report the uncertainty of their equipment rather than depending on manufactures’ claims.

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Brian M. Wood, Herman Pontzer, Jacob A. Harris, Audax Z.P. Mabulla, Marc T. Hamilton, Theodore W. Zderic, Bret A. Beheim and David A. Raichlen

). To visualize model uncertainty, we now plot posterior predictions of a Bayesian model that includes the same predictor variables as those listed in Table  4 . In Figure  6 , we represent the stand-alone effects of subject height, distance traveled, and average speed, when statistically controlling