Analysis of subject specific grasping patterns

Abstract

Existing haptic feedback devices are limited in their capabilities and are often cumbersome and heavy. In addition, these devices are generic and do not adapt to the users' grasping behavior. Potentially, a human-oriented design process could generate an improved design. While current research done on human grasping was aimed at finding common properties within the research population, we investigated the dynamic patterns that make human grasping behavior distinct rather than generalized, i.e. subject specific. Experiments were conducted on 31 subjects who performed grasping tasks on five different objects. The kinematics and kinetics parameters were measured using a motion capture system and force sensors. The collected data was processed through a pipeline of dimensionality reduction and clustering algorithms. Using finger joint angles and reaction forces as our features, we were able to classify these tasks with over 95% success. In addition, we examined the effects of the objects' mechanical properties on those patterns and the significance of the different features for the differentiation. Our results suggest that grasping patterns are, indeed, subject-specific; this, in turn, could suggest that a device capable of providing personalized feedback can improve the user experience and, in turn, increase the usability in different applications. This paper explores an undiscussed aspect of human dynamic patterns. Furthermore, the collected data offer a valuable dataset of human grasping behavior, containing 1083 grasp instances with both kinetics and kinematics data.

Analytical-stochastic model of motor difficulty for three-dimensional manipulation tasks

“…objets do not influence the population's motor difficulty; the same conclusion holds in [57] where it has been observed that the alteration of the mechanical properties (weight variations in the bottles, stiffness and friction in the ellipsoid ball), does not affect the motor behaviour. Therefore, the mass end stiffness can be removed from the features that affect the subjects' motor difficulty.…”

“…In particular, subjects experience a greater motor difficulty when manipulating the bottles (half-full and full), compared to the ellipsoid balls (soft and plastic). Since results do not provide relevant differences between the two bottles (half-full vs. full), and between the two ellipsoid balls (soft vs. plastic) it can be concluded that the different mass alone as well as, the different stiffness of objets do not influence the population’s motor difficulty; the same conclusion holds in [ 57 ] where it has been observed that the alteration of the mechanical properties (weight variations in the bottles, stiffness and friction in the ellipsoid ball), does not affect the motor behaviour. Therefore, the mass end stiffness can be removed from the features that affect the subjects’ motor difficulty.…”

“…In the present article, the marker on the hand’s palm is considered for the end-effector movements. Further information on the dataset are in [ 57 ].…”

“…An experimental dataset publicly available in [56] is used to test the model. Data analysis is carried out using MATLAB 1 software (version R2021a).…”

“…Results show that there is a significative change in the subjects' motor difficulty when manipulating different objects. [57] it has been observed that objects with different geometry affect the subjects' motor behaviour during the object manipulation. To test this hypothesis, a one-way repeated measures ANOVA on ID obs s � ¼ � t; φ ð Þ values of the 31 subjects for the three geometrical different objects (tennis ball, half-full bottle, plastic ellipsoid ball), has been performed.…”