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.
The process of fitting a prosthetic hand that is a comfortable, functional, easy to use, has an acceptable appearance and overall improves the amputees' quality of life is a complex, tedious and costly process. The very high price tag due to the time spent on manually fitting the device by a trained specialist makes these devices inaccessible to large portions of the population. We present a concept and preliminary results for a fully automated fitting and manufacturing pipeline for a personalized low-cost prosthetic hand. The hand is personalized in almost every aspect, from appearance to user interface, control and feedback. The pipeline only requires a 3D printer, a RealSense camera, a few basic mechanical components and basic tools for the model assembly. The user scan-driven data and the user preferences initiate a fully-automated pipeline which culminates in a customized, easy-to-assemble PCB design and ready to print STL files, including the optimized orientation, support and layout, such that the final parts are only one click away. We believe that the proposed pipeline and design can substantially improve quality of life for amputees and could potentially be expanded to other medical applications.
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