Abstract
A force capacity evaluation for a given posture may provide better understanding of human motor abilities for applications in sport sciences, rehabilitation and ergonomics. From data on posture and maximum isometric joint torques, the upper-limb force feasible set of the hand was predicted by four models called force ellipsoid, scaled force ellipsoid, force polytope and scaled force polytope, which were compared with a measured force polytope. The volume, shape and force prediction errors were assessed. The scaled ellipsoid underestimated the maximal mean force, and the scaled polytope overestimated it. The scaled force ellipsoid underestimated the volume of the measured force distribution, whereas that of the scaled polytope was not significantly different from the measured distribution but exhibited larger variability. All the models characterized well the elongated shape of the measured force distribution. The angles between the main axes of the modelled ellipsoids and polytopes and that of the measured polytope were compared. The values ranged from 7.3° to 14.3°. Over the entire surface of the force ellipsoid, 39.7% of the points had prediction errors less than 50 N; 33.6% had errors between 50 and 100 N; and 26.8% had errors greater than 100 N. For the force polytope, the percentages were 56.2%, 28.3% and 15.4%, respectively.
Highlights
The knowledge of the maximum forces that an individual can exert on the environment allows one to assess his ability to perform a given task without exceeding physiological limits
The dependence of the force feasible set (FFS) on the posture is due to the muscle length, which determines the muscle’s isometric force; muscular moment arms, which contribute to the joint torques; and the Jacobian matrix of the upper limb, which links these joint torques to the external force at the hand
The measured joint angles during the joint torques and force measurements were relatively close despite two significant differences (Table 3)
Summary
The knowledge of the maximum forces that an individual can exert on the environment allows one to assess his ability to perform a given task without exceeding physiological limits. Maximum force can be used as an objective indicator for defining criteria for discomfort evaluation [5]; and in the framework of digital human modeling, it is needed for tuning muscle fatigue models’ parameters [6]. The distribution of the maximum external forces that the upper-limb can apply to the outside world, defined as the force feasible set (FFS), is known to be anisotropic and posture-dependent [10,11]. The dependence of the FFS on the posture is due to the muscle length, which determines the muscle’s isometric force; muscular moment arms, which contribute to the joint torques; and the Jacobian matrix of the upper limb, which links these joint torques to the external force at the hand. The knowledge of a maximal value for a limited set of directions, as is usually found in the literature [12,13,14,15], may not be sufficient to globally represent the force
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