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Review Article

J. Comput. Inf. Sci. Eng. 2017;18(1):010801-010801-7. doi:10.1115/1.4038291.

An anthropomorphic, under-actuated, prosthetic hand has been designed and developed for upper extremity amputees. This paper proposes a dexterity focused approach to the design of an anthropomorphic electromechanical hand for transradial amputees. Dexterity is increased by the improvement of thumb position, orientation, and work space. The fingers of the hand are also capable of adduction and abduction. It is the intent of this research project to aid the rehabilitation of upper extremity amputees by increasing the amount of tasks the hand can execute. Function and control of the hand are based on micro servo actuation and information acquired from the brain. Electroencephalography (EEG) is used to attain the mental state of the user, which triggers the prosthetic hand. This paper focuses on the mechanical arrangement of the hand and investigates the effect of increasing the degrees-of-freedom (DOFs) the thumb and fingers have.

Commentary by Dr. Valentin Fuster

Research Papers

J. Comput. Inf. Sci. Eng. 2017;18(1):011001-011001-12. doi:10.1115/1.4037934.

The design of complex engineering systems requires that the problem is decomposed into subproblems of manageable size. From the perspective of decision-based design (DBD), typically this results in a set of hierarchical decisions. It is critically important for computational frameworks for engineering system design to be able to capture and document this hierarchical decision-making knowledge for reuse. Ontology is a formal knowledge modeling scheme that provides a means to structure engineering knowledge in a retrievable, computer-interpretable, and reusable manner. In our earlier work, we have created ontologies to represent individual design decisions (selection and compromise). Here, we extend the selection and compromise decision ontologies to an ontology for hierarchical decisions. This can be used to represent workflows with multiple decisions coupling together. The core of the proposed ontology includes the coupled decision support problem (DSP) construct, and two key classes, namely, Process that represents the basic hierarchy building blocks wherein the DSPs are embedded, and Interface to represent the DSP information flows that link different Processes to a hierarchy. The efficacy of the ontology is demonstrated using a portal frame design example. Advantages of this ontology are that it is decomposable and flexible enough to accommodate the dynamic evolution of a process along the design timeline.

Commentary by Dr. Valentin Fuster
J. Comput. Inf. Sci. Eng. 2017;18(1):011002-011002-10. doi:10.1115/1.4037434.

An important part of the engineering design process is prototyping, where designers build and test their designs. This process is typically iterative, time consuming, and manual in nature. For a given task, there are multiple objects that can be used, each with different time units associated with accomplishing the task. Current methods for reducing time spent during the prototyping process have focused primarily on optimizing designer to designer interactions, as opposed to designer to tool interactions. Advancements in commercially available sensing systems (e.g., the Kinect) and machine learning algorithms have opened the pathway toward real-time observation of designer's behavior in engineering workspaces during prototype construction. Toward this end, this work hypothesizes that an object O being used for task i is distinguishable from object O being used for task j, where i is the correct task and j is the incorrect task. The contributions of this work are: (i) the ability to recognize these objects in a free roaming engineering workshop environment and (ii) the ability to distinguish between the correct and incorrect use of objects used during a prototyping task. By distinguishing the difference between correct and incorrect uses, incorrect behavior (which often results in wasted time and materials) can be detected and quickly corrected. The method presented in this work learns as designers use objects, and infers the proper way to use them during prototyping. In order to demonstrate the effectiveness of the proposed method, a case study is presented in which participants in an engineering design workshop are asked to perform correct and incorrect tasks with a tool. The participants' movements are analyzed by an unsupervised clustering algorithm to determine if there is a statistical difference between tasks being performed correctly and incorrectly. Clusters which are a plurality incorrect are found to be significantly distinct for each node considered by the method, each with p ≪ 0.001.

Commentary by Dr. Valentin Fuster

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