UX Inquiry: Understanding Concussion can feel like nailing JELL-O to a tree

What is on this page?

Expose students to artificial intelligence (AI), extended reality (XR), and the Internet of Things (IoT) concerning traumatic brain injury.

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Caroline's dedication to enriching and nourishing the lives of those affected by TBI is truly inspiring. With her extensive background in yoga, TBI recovery, and communication studies, she has been able to make a significant impact in the TBI community. Caroline believes that everyone, regardless of their background, is at risk for a TBI, and she has witnessed firsthand the importance of adaptability and technology in aiding treatment, rehabilitation, and compensatory behaviors for living and thriving with a TBI. By utilizing technology, Caroline believes that care providers can better map out treatment programs, users can remain proactive and engaged in daily life, and physical rehabilitation of lost motor function can be aided. 

Caroline's research focuses on using Artificial Intelligence (AI) to aid in diagnosing and monitoring concussion patients. The AI algorithms employed in her study are designed to collect and analyze patient data comprehensively, enabling clinicians to provide the best possible care. Caroline believes that AI technology has the potential to revolutionize the way doctors diagnose and treat concussion patients. She is determined to find out why concussion patients have to explain every symptom they experience to their doctors and whether AI can categorize all possible concussion symptoms for effective diagnosis and treatment. Caroline is confident that her research will pave the way for more accurate and efficient concussion diagnosis and treatment, ultimately improving patient outcomes.

The AI applied in this research incorporates algorithms to log all patient data to provide the best care. Artificial intelligence offers assistance in data sense-making through machine learning algorithms (Miailhe,2017).

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RQ: Why do concussion patients have to teach their doctors how every symptom they are experiencing relates back to their brain injury?

RQ 2: Can AI categorize each possible concussion symptom for the entire body and provide a proper body of evidence for effectively diagnosing and treating concussion patients?

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1. AI will allow teams of physicians to communicate about one patient and their symptoms collectively using algorithms. The AI will collect and distribute diagnostic data of concussion patients to provide insight into the differences in patients’ post-injury deficits and paint a holistic picture of health. Artificial intelligence (AI) can be used to summarize multiple parameters and make interpretation of overall results easier. Specifically, machine learning (ML) has been used in increasing studies to improve clinical diagnoses and explore unexplained phenomena (Visscher, Feddermann-Demont, Fausto Romano, Straumann, & Bertolini 2019).

1.  An increasing amount of evidence suggests that early and case-specific treatment is key to allowing a fast return to daily life for concussion patients (Visscher et al., 2019). Many concussion patients are not seen for weeks or months following injury, delaying the time of intervention. As of April 2019, ML has yet to be used on a vestibular database of concussion patients (Visscher et al., 2019).

This technology might be utilized outside of its intended purpose of optimizing diagnostic criteria and improving patient monitoring (Visscher et. al., 2019). Healthcare facilities and health insurance companies can use this information to decline patient requests for coverage of new symptoms, claiming that the new symptoms are all part of the original injury and thus “pre-existing.” Since TBI is a lifelong diagnosis, many patients experience changes in neurological and vestibular health for the rest of their lives, ranging from insomnia, emotional distress, loss of motor function, seizures, new neurological disorders, PTSD, and vision and hearing disorders, to name a few. This information could also be used against patients seeking employment, benefits, and government assistance. Unlike a broken limb, brain injury is an ongoing process and impacts the entire biological person as well as the fluctuations of the patient’s mind. Broken limbs can be dissected to perfectly provide a cast, physical therapy, and accommodations for healing. With a brain injury, there are too many moving parts to pinpoint exact modifications for healing and recovery. Without a baseline understanding of the TBI patient from AI, the ML may not properly compare and contrast the patient’s history pre- and post-injury (Bansal, 2008).

EXTENDED REALITY (XR)

Here, we will explore an XR therapy for Concussion patients used at the Shepherd Center, located in Atlanta, Georgia. This private, not-for-profit hospital specializes in medical treatment, research, and rehabilitation for people with brain injury, multiple sclerosis, and other neuromuscular conditions. Shepherd Center is ranked by U.S. News & World Report among the top 10 rehabilitation hospitals in the nation.

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Hocoma’s Armeo®Spring (Shepherd Center, 2010) is an arm exoskeleton that marries a therapy device to a computer-simulated, virtual reality environment. After placing an arm in a support system with a handgrip, the user ventures beyond repetitive back-and-forth motions to practice everyday tasks, such as putting fruit into a basket or eggs into a frying pan. The machine also offers motion-sensor video games, making therapy potentially more engaging to people of all ages. 

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XR for neurological condition interventions (Lavner et al., 2017) primarily focuses on upper limb function. The Shepherd Center XR-based interventions may improve motor function for daily performance compared to conventional manual therapy. It is not known which characteristics are the most meaningful for recovery from a patient's perspective. More longitudinal studies examining post-traumatic growth from the perspective of TBI survivors are needed to examine how beneficial XR is as a form of therapy in the long term.

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The Internet of Things application investigated in this section is the use of a Smartwatch for Brain Injury survivors returning home and in the care of a doctor. Brain Injury survivors have difficulty with misplacing things, remembering to take appropriate supplements and prescriptions, keeping track of time, and falling unexpectedly. TBI survivors also experience reduced engagement in leisure and social activities. Due to cognitive differences and memory difficulty. The Smartwatch is equipped with an SOS alert system that calls Emergency services in the case of a fall without any initiation from the user. The smartwatch can also use haptics and subtle tapping to remind users of appointments and other calendar notifications pre-programmed from the phone.

1. This technology will offer memory compensation assistance for people living with acquired and Traumatic brain injury. Current solutions include external prompting, sticky notes, physical calendars, in-home caretakers, and self-coping mechanisms to attempt to compensate for brain injury. Maintaining intrinsic momentum and being organized is a known obstacle for TBI survivors.

2. What are some of the shortfalls of this technology currently? What advancements are needed for this technology to reach its potential to transform the way we learn?

3. There are major concerns about implementing IoT as a patient care service, such as invasion of privacy, volume of shared data, cost of devices, cybersecurity, and the evolution of technology. Brain Injury survivors are subject to identity theft and manipulation from humans; the risk increases when every ounce of data on a person is available via the internet. If Big Data is always listening, how does one discern what evidence will be used for the patient's good and what will be used against them, or simply without consent?