Pushing the Acquisition Innovation Envelope at the Office of Naval Research

Developing prototypes may require performers, all with different areas of expertise, working together to address the complexity required for a successful development effort. Current Federal Acquisition Regulation (FAR) policy makes it difficult for these collaborations to assemble efficiently. Complex research projects, such as the Office of Naval Research's Incapacitation Prediction in Expeditionary Domains: An Integrated Software Tool (I-PREDICT) project, which seeks to develop a computational model to predict human injury and functional incapacitation as a result of military hazards, often face difficulty when attempting to transition across the "valley of death"from development to adoption. A decision framework was developed and implemented for I-PREDICT to select the appropriate acquisition strategy aligned with the technical needs of the program. A three-phase implementation strategy was also designed, which included the use of an Other Transaction Authority (OTA) and the use of a Technical Committee to promote communication between performers. The resulting decision framework and implementation strategy may be used Navy-wide or across other military Services for R&D programs requiring acquisition flexibility coupled with collaborative technology development. Additionally, the research produced a customizable method for leveraging OTAs as a mechanism for development of complex prototypes depending on disparate kinds and sources of expertise.Naval Postgraduate School Acquisition Research Progra

A Comparison of Approaches for Segmenting the Reaching and Targeting Motion Primitives in Functional Upper Extremity Reaching Tasks

There is growing interest in the kinematic analysis of human functional upper extremity movement (FUEM) for applications such as health monitoring and rehabilitation. Deconstructing functional movements into activities, actions, and primitives is a necessary procedure for many of these kinematic analyses. Advances in machine learning have led to progress in human activity and action recognition. However, their utility for analyzing the FUEM primitives of reaching and targeting during reach-to-grasp and reach-to-point tasks remains limited. Domain experts use a variety of methods for segmenting the reaching and targeting motion primitives, such as kinematic thresholds, with no consensus on what methods are best to use. Additionally, current studies are small enough that segmentation results can be manually inspected for correctness. As interest in FUEM kinematic analysis expands, such as in the clinic, the amount of data needing segmentation will likely exceed the capacity of existing segmentation workflows used in research laboratories, requiring new methods and workflows for making segmentation less cumbersome. This paper investigates five reaching and targeting motion primitive segmentation methods in two different domains (haptics simulation and real world) and how to evaluate these methods. This work finds that most of the segmentation methods evaluated perform reasonably well given current limitations in our ability to evaluate segmentation results. Furthermore, we propose a method to automatically identify potentially incorrect segmentation results for further review by the human evaluator. Clinical impact: This work supports efforts to automate aspects of processing upper extremity kinematic data used to evaluate reaching and grasping, which will be necessary for more widespread usage in clinical settings