Mason Journals (George Mason Univ.)
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Phlexor: A Residual Force Controlled, Synergy-Based Hand Exoskeleton for Grasping Assistance
No full textAs a result of stroke, millions across the United States struggle with limited upper extremity function, specifically limited hand dexterity. Hand exoskeletons are designed to improve and restore hand motor function as well as quality of life. However, providing robust and intuitive user control remains challenging due to reliability issues or limited applicability to daily life scenarios. Phlexor’s control method addresses these obstacles by using a force sensor placed within a button on the palm which utilizes the residual isometric grip force of the ring and little finger to actuate the remaining three fingers. Phlexor uses a differential mechanism consisting of a single bevel gear and three spur gears to power separate rack-and-pinions, stimulating the natural postural synergies of the hand and supporting multiple variations of grasps with a single servo motor. A spring-driven slider placed on the dorsal side of the hand allows the user to mechanically fix the thumb in three different positions, enabling them to create opposition when needed. To improve the user’s donning and doffing experience, Phlexor is designed to attach to the dorsal side of the hand using compliant clasps. The inexpensive design prioritizes a lightweight and compact profile on the hand and arm while simultaneously achieving consistent movement. Future studies look to validate the functionality of Phlexor to both restore the user’s ability to perform acts of daily living and rehabilitate natural biomechanical functionality
DR-CyCADA: Depth Retentive Cycle Consistent Adversarial Domain Adaptation
No full textImage depth data can hold a wealth of information for robotic systems that rely on visual input while navigating in unknown environments. Domain adaptation algorithms have enabled robots to transfer knowledge of a known domain to an unfamiliar environment. However, current domain adaptation approaches typically focus on visual appearance, without regard for the structure of an environment. Thus, current approaches can unexpectedly fail when translating between environments. We overcome this mismatch between the original and adapted image by introducing a depth retention loss component to a popular existing domain adaptation model, CyCADA [1]. Our DR-CyCADA approach retains structural components of images and displays promising results in generating high quality images that retain their original structural layout. We evaluate our model via its depth retention loss. We show that DR-CyCADA outperforms similar systems that do not leverage depth retention loss in retaining the structural layout of an environment, demonstrating that our DR-CyCADA model can be used to perform domain adaptation tasks in structurally similar environments and is thus capable of state-of-the-art domain adaptation for robotic systems.
References: [1] Hoffman, J., Tzeng, E., Park, T., Zhu J., Isola, P., Saenko, K., Efros, A., Darrell, T. (2018, July 10-15). CyCADA: Cycle-Consistent Adversarial Domain Adaptation [Paper Presentation]. International Conference on Machine Learning 35th Annual Meeting, Stockholmsmässan, Sweden. https://proceedings.mlr.press/v80/hoffman18a.html 
Improving Accuracy in PM2.5 Interpolation Using AI and ML
No full textAir quality prediction is increasingly critical, especially considering recent events like the Canadian Wildfires releasing hazardous particulate matter over the US. The growing awareness of AI and machine learning have been increasingly used to facilitate applications in scientific studies, notably PM2.5 retrieval. PM2.5 retrieval uses machine learning (ML) techniques to estimate accurate PM2.5 values by considering predictors such as meteorological variables. One of the challenges in PM2.5 retrieval is dealing with different predictors with varying spatial resolutions. Prior to retrieval, predictor variables needed to be interpolated into a uniform grid, which currently lacks a standardized and validated model. Different interpolation methods (Inverse Distance Weighting (IDW), Kriging, and Natural Neighbor) offer techniques for estimating values in PM2.5 retrieval. Understanding the results involves assessing the spatial behavior of the data and validating the interpolation methods using metrics like Root Mean Squared Error (RMSE) and R-squared (R2). In this study, different interpolation models in ArcGIS Pro were used to interpolate meteorological variables. The models employed estimated values at a uniform grid with a spatial resolution of 1 km x 1 km. Interpolation methods were carefully evaluated using validation metrics to assess their effectiveness and accuracy in capturing spatial patterns and variations at this resolution. 
Employers’ Perspectives on Ohio’s Postsecondary Programs for Students with Intellectual and Developmental Disabilities: Employer Perspectives on Inclusive Postsecondary Programs
No full textAs postsecondary programs grow for students with intellectual and developmental disabilities (SwIDD), there is an increased focus on program evaluation and effectiveness, specifically related to impact on employment outcomes. Ohio’s Statewide Consortium (OSC) of postsecondary programs for individuals with intellectual and developmental disabilities interviewed seventeen current employer partners to determine to what degree they perceive OSC programs demonstrate quality indicators, as outlined in the eight Think College Standards for Inclusive Higher Education (TCSIHE). Results indicate OSC programs are providing high-quality, positive work-based learning experiences for SwIDD and their employer partners that align to TCSIHE. Key components of quality partnerships are highlighted as well as aspects of work-based learning experiences critical to skill development for SwIDD, and barriers to post-graduation employment outcomes
An Interview with a World Historian: Deborah Smith Johnston, a World History Association Awardee as a "Pioneer in World History"
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