Biomedical subjects

Don B Chaffin

Publications and source records attributed to Don B Chaffin.

4 recordsLinked to original sources

PubMed2006

The relationship between shoulder torques and the perception of muscular effort in loaded reaches.

The objective of this study was to define the quantitative relationship between external dynamic shoulder torques and calibrated perceived muscular effort levels for load delivery tasks, for application in job analyses. Subjects performed a series of loaded reaches and, following each exertion, rated their perceived shoulder muscular effort. Motion and task physical requirements data were processed with a biomechanical upper extremity model to calculate external dynamic shoulder torques. Calculated torque values were then statistically compared to reported calibrated perceived muscular effort scores. Individual subject torque profiles were significantly positively correlated with perceived effort scores (r2 = 0.45-0.77), with good population agreement (r2 = 0.50). The accuracy of the general regression model improved (r2 = 0.72) with inclusion of factors specific to task geometry and individual subjects. This suggests two major conclusions: 1) that the perception of muscular shoulder effort integrates several factors and this interplay should be considered when evaluating tasks for their impact on the shoulder region; 2) the torque/perception relationship may be usefully leveraged in job design and analysis.

Biomechanical Phenomena↗

PubMed2005

Representing and identifying alternative movement techniques for goal-directed manual tasks.

Differences in motion patterns subserving the same movement goal can be identified qualitatively. These alternatives, which may characterize 'movement techniques' (e.g., the stoop and the squat lifting technique), may be associated with significantly different biomechanical constraints and physiological responses. Despite the widely shared understanding of the significance of alternative movement techniques, quantitative representation and identification of movement techniques have received little attention, especially for three-dimensional whole-body motions. In an attempt to systematically differentiate movement techniques, this study introduces a quantitative index termed joint contribution vector (JCV) representing a motion in terms of contributions of individual joint degrees-of-freedom to the achievement of the task goal. Given a set of uncharacterized (unlabeled) motions represented by joint angle trajectories (motion capture data), the JCV and statistical clustering methods enable automated motion classification to uncover a taxonomy of alternative movement techniques. The results of our motion data analyses show that the JCV was able to characterize and discern stoop and squat lifting motions, and also to identify movement techniques for a three-dimensional, whole-body, one-handed load-transfer task. The JCV index would facilitate consideration of alternative movement techniques in a variety of applications, including work method comparison and selection, and human motion modeling and simulation.

Cluster Analysis↗

PubMed2004

A computer algorithm for representing spatial-temporal structure of human motion and a motion generalization method.

Inspired by the generalized motor program (GMP) theory, this study presents a symbolic motion structure representation (SMSR) algorithm that identifies a basic spatial-temporal structure of a human motion. The algorithm resolves each joint angle-time trajectory of a multi-joint motion into a sequence of elemental motion segments and labels each motion segment with a symbol representing its shape ("U": monotonically increasing; "D": monotonically decreasing; "S": stationary). By concatenating symbols according to their order in time, the spatial-temporal structure of a joint angle-time trajectory is represented as a symbolic string. The structure of a multi-joint motion is then represented as a set of symbolic strings. A sample motion, whose structure is identified by the SMSR algorithm, can be generalized to produce an infinite number of similar motion variants. To generate a variant of a sample motion, segment boundary points of the sample motion are first relocated to new locations in the angle-time space, and then individual motion segments of the original joint angle trajectories are shifted and proportionally rescaled to fit the new segment boundary points. This motion generalization method provides a basis for developing GMP-based motion simulation models, and exploring ideas and hypotheses related to the GMP theory through simulation. As an application of the motion generalization method, a motion modification (MoM) algorithm is presented, which adapts existing reach motions for new target locations. Some examples generated by the MoM algorithm are illustrated.

Algorithms↗

PubMedDate not supplied

Primary prevention of low back pain through the application of biomechanics in manual materials handling tasks.

Biomechanical models of the torso have become quite sophisticated in recent years. This paper describes how injurious stresses on the low back can be predicted by such models during the early phases of designing materials handling tasks in industry. It is shown that these biomechanical models can be used to simulate novel materials handling tasks, and thus be used to guide the design of such tasks to reduce various low back stresses. In addition, biomechanical simulations are described which continue to play a major role in understanding the complex stresses that can cause low back pain. These simulations provide a scientific basis for specific ergonomics guidelines meant to reduce the risk of future low back pain in industry. Limitations in the present biomechanical simulations are also presented to stimulate additional research.

Biomechanical Phenomena↗