Leaders in Applied Research and Inclusive Design

Play is an essential part of a child’s development that helps to increase their understanding of themselves and the world around them through the mastery of physical, cognitive, language, and social skills. Since play opportunities can be limited for children and toddlers living with disabilities, these robots serve to fill that role as alternative, interactive, and therapeutic toys. Please see a list of the current and historical research projects to learn more about the innovative projects conducted through the SAR lab at ATP.

Stroke is the leading cause of long-term disability in the U.S., impacting roughly 795,000 adults each year. The goal of stroke recovery is to partner emerging medical interventions with rehabilitation during the post stroke phase when neural plasticity has the greatest ability to facilitate recovery. The use of social rehabilitation robotics (SARs) is a promising field in physical medicine and rehabilitation. SARs combine therapeutic interventions with social or emotional support to improve outcomes. CIDE is researching and developing a SAR with age-appropriate look and feel for adults who are at least six-months post stroke in order to measure the effects of social robotics on upper limb movement recovery and social engagement, while also testing the correct development of the sensors and response systems.

We seek to accomplish our goal of developing inclusive AR user interface design guidelines for cognitively impaired populations through two synergistic approaches - usability testing of existing AR technology and development of AR simulators.  Usability testing will be conducted for existing software and apps across both handheld and wearable AR devices, with initial focus on handheld technology given the pervasive smartphone/tablet infrastructure; more emphasis will be placed on wearable AR as it becomes more widely adopted and affordable for everyday consumers.  AR software and apps will be selected based on their functional utility and capacity to enhance user independence, social engagement, and/or safety.  Task performance with and without AR will be evaluated with mixed methods; correlation between quantitative and qualitative metrics will be used to assess user perception versus objective performance differences with AR.  Participant cognitive test batteries will be assessed as well to discern phenotypic performance differences with AR. 

Touchscreen technology has rapidly become a mainstream method of user interaction with essential devices such as smartphones, tablet, and computers. While moderate research has been conducted to help characterize the best practices for touchscreen UI design, these guidelines are not inclusive for individuals with physical/cognitive disabilities, populations who arguably stand to benefit the most from this technology. Currently, we are examining the paradigm of social media apps and their usability for individuals with mild to moderate cognitive disabilities, a population known to experience social isolation. Towards this effort, we have conducted over 130+ usability tests across 25+ participants, and will be continuing to expand these numbers. Ultimately, we aim to develop inclusive touchscreen UI design guidelines that encourage developers to increase the usability of their products for everyone.

Given the diverse landscape that is cognitive disability, it is often difficult to characterize unique needs when designing inclusively. To address this challenge, CIDE has partnered with UC Health Neuropsychology to develop a cognitive test battery (CTB) that is administered to every participant in our usability studies. 

Our goals are to:

• Identify the usability issues that an individual may experience with technology
• Characterize how these issues may relate to cognitive strengths and weaknesses. 
• Use the CTB to help partition our inclusive design guidelines such that they can uniquely address the usability needs of individuals with particular cognitive characteristics.

Many power wheelchair users cannot reach to the floor to retrieve a charging cable, or plug it into a small port on the front or bottom of the control joystick. These people currently rely on others to charge their wheelchairs for them, reducing their independence. In collaboration with electrical engineering teams at Utah State University, we have developed and refined a wireless power transfer system that allows a wheelchair to be charged wirelessly. Our latest battery system also holds nearly twice as much energy as traditional wheelchair batteries, and can be charged much faster. Our user-centered iterative design process has produced a prototype system that is usable and applicable in real-world settings.

Activity monitoring technology can track an individual’s average daily activity and offer lifestyle recommendations to improve their efficiency and health.  This is of particular importance for individuals who are full-time manual wheelchair users (MWUs), where a primary goal is to retain the health and function of their upper-extremities.  Specifying an optimal wheelchair configuration and components that fit a MWU’s daily mobility needs is critical.  This project seeks to develop a low-profile sensor platform that tracks the manual wheelchair’s kinematics (velocity, brake frequency, turning rates) and traversed environments (slope grade, surface types) to serve as a diagnostic tool that enables clinicians to offer more informed recommendations that suit each MWU’s mobility needs. 

People who are aging or have cognitive impairments face increased risk of falls, and when they do fall, it is important that they receive fast and appropriate medical care. However, this population often find themselves unable to call for help, or to tell responders exactly how they’ve been injured. With Anthem Memory Care and other industry collaborators, we are working to develop comfortable wearable sensors and algorithms that will detect and classify falls. These tools will alert caregivers immediately when a user falls, and relay vital information on the direction and type of fall to aid in effective diagnosis and treatment.

CIDE's IoT sensor lab will support the massive amounts of secure data needed for machine learning and AI. This lab will enable us to work with disparate, sensor-derived data streams, verify best fit for various use cases, determine requirements for data analysis; test fidelity of specific sensor data against real world activity; experiment with fusion techniques; and identify complimentary approaches to sensing human activity. The IoT sensor lab will create the infrastructure necessary to launch novel and innovative machine learning (ML)/artificial intelligence (AI) applications and contribute to future sensor development and refinement.