Among the currently available grasp-type selection techniques for hand prostheses, there is a distinct lack of intuitive, robust, low-latency solutions. In this paper we investigate the use of a portable, forearm-mounted, video-based technique for the prediction of hand-grasp preshaping for arbitrary objects. The purpose of this system is to automatically select the grasp-type for the user of the prosthesis, potentially increasing ease-of-use and functionality. This system can be used to supplement and improve existing control strategies, such as surface electromyography (sEMG) pattern recognition, for prosthetic and orthotic devices. We designed and created a suitable dataset consisting of RGB-D video data for 2212 grasp examples split evenly across 7 classes; 6 grasps commonly used in activities of daily living, and an additional no-grasp category. We processed and analyzed the dataset using several state-of-the-art deep learning architectures. Our selected model shows promising results for realistic, intuitive, real-world use, reaching per-frame accuracies on video sequences of up to 95.90% on the validation set. Such a system could be integrated into the palm of a hand prosthesis, allowing an automatic prediction of the grasp-type without requiring any special movements or aiming by the user.

Dispersed data sources, incompatible data formats and a lack of non-ambiguous and machine readable meta-data are major obstacles in data analytics and data mining projects in process industries. Usually, meta-information is only available in unstructured format optimized for human consumption. This contribution captures common problems when handling data from process plants in analytics, develops the vision of a data collection process supported by a search tool, and describe a search tool for the heterogeneous plant data as a proof-of-concept.