Here at the REACH lab, we focus on human performance and determining biomedical engineering solutions that can improve quality of life
Investigating the impacts of virtual reality training and visual stimuli on older adults' balance
Thesis Project: Roni Romero Ph.D. candidate
Methodologies and technologies used toward facilitating the reduction of the falls in older adults is critical. Falls are the second leading cause of unintentional injury-related deaths worldwide, with adults over the age of 60 suffering the greatest number of fatal falls. Since 2009, falls have been the leading cause of death in the United States for adults 65-74 years old, with falls ranking 1st in leading causes of death for age groups 76 years old and above. Further, each year the United States spends approximately $50 billion on medical costs related to older adult falls ($754 million is spent on fatal injuries, while the remaining $49.3 billion is spent on non-fatal fall injuries). Although there is potential and promise, there have been relatively limited studies utilizing immersive virtual reality (VR) toward the investigation of improving postural control and balance in older adults, a high fall-risk population.
VR re-creates a realistic experience through simulation, combining vision, sound, touch, and even inducing perceptions of motion to ‘trick’ the brain, thereby leading to a tangible response. VR has the potential to help improve postural control in older adults through training, which could reduce the likelihood of falls. Here, we examine the effects of VR-based training and assessment on balance and sensory reweighting (how people use inputs from vision (eyes), somatosensory (touch), and vestibular (inner ear/equilibrium) systems) in aging individuals. We strive to obtain new knowledge on the effects of VR-based training on standing balance (Aim 1) and walking/gait (Aim 2) during multiple weeks of repetitive training, as well as by investigating older adults’ sensory reweighting via a distinctive VR-based visual perturbation stimulus (Aim 3). We hypothesize that, after several weeks of training, standing balance and gait parameters will indicate increased stability, and that a distinctive VR-based visual perturbation stimulus will provide a mechanism to quantify sensory changes in older adults.
Understanding the Effects of Concussion on Balance Performance and Muscle Coordination in Athletes
Thesis Projects: Visalakshi Chockalingham and Oshin Wilson Ph.D. candidates
Sports-related concussions remain a significant public health concern, particularly among collegiate athletes participating in high-impact sports. Concussions are among the most prevalent injuries in high-impact sports, such as ice hockey, soccer, and football, and lead to persistent deficits in neuromuscular coordination, postural control, and sensorimotor integration that extend beyond apparent clinical recovery. Concussion and mild traumatic brain injury represent a major public health concern, affecting an estimated 1.6 – 3.8 million athletes annually in the United States from sports- and recreation-related activities. More broadly, over 2.8 million traumatic brain injury–related emergency department visits, hospitalizations, and deaths occur each year in the United States. While often classified as “mild,” concussions can lead to both short- and long-term neurological impairments, ranging from dizziness, irritability, anxiousness, imbalance, memory and information processing or slowed reaction time to cognitive, visual, and vestibular dysfunction. While many athletes appear to recover,
subtle impairments in balance, gait, sensorimotor integration, and muscle coordination (synergies) may remain, elevating their risk of re-injury and long-term dysfunction.
An overarching goal of this project is to investigate the effects of concussion on balance, sensory reweighting, and muscle synergies in athletes that participate in high impact sports. We will accomplish our research goal through investigating standing balance and postural control (Aim 1), gait and dynamic stability during walking and dual-task conditions (Aim 2), and sensorimotor integration in response to controlled sensory (visual) perturbations (Aim 3) in athletes with or without a history of concussion.
Exploring Pediatric Impaired Speech Features and Data-Driven Speech Diagnostics
Thesis Project: Aliya Newby Ph.D. candidate
In the United States, speech, voice, and language disorders affect approximately 1 in 14 children aged 3-17 years old. The diagnosis of speech and communication disorders in children is particularly important in that, if impairments are left undiagnosed, they could impact communication, academics, social interactions, and overall quality of life as the child develops. Currently, speech diagnostic approaches rely heavily on highly subjective clinical assessments. Assessments can show variability in accuracy or incorrect diagnosis. Further, there may be delays in obtaining an appointment toward diagnosis; yet, accuracy and timeliness of diagnosis is particularly critical for developing children. Hence, there is a need for objective, quantifiable diagnostic methods for children that can offer guidance while informing personalized and effective, speech training strategies.
Our overarching goals are to identify characteristic markers of voice in children with impaired speech by investigating extracted acoustic features and to distinguish between different speech disorders using trained machine learning (ML) models. We will achieve our research goals by characterizing the key features of select pediatric speech disorders (Aim 1), distinguishing the key features of each using ML models (Aim 2), and exploring methodologies used for speech screening and training in children (Aim 3). To support these aims, recorded audio samples from open-source pediatric speech datasets which include phoneme articulation, word repetition tasks and spontaneous speech will be processed to extract a comprehensive set of time-domain, frequency-domain and nonlinear acoustic features. These features will support the characterization of speech patterns in pediatric patients.
Investigating Biometric Features tied to Alzheimer's Disease
Among individuals over 65 years old, Alzheimer’s Disease (AD) is the most prevalent form of dementia and remains incurable. The number of patients with AD is rapidly increasing and, according to the World Health Organization, projections suggest that the number of impacted individuals will reach 78 million by 2030 and 139 million by 2050. Conventional methods for diagnosis such as Magnetic Resonance Imaging(MRI), Positron Emission Tomography (PET), and Cerebrospinal Fluid (CSF) biomarker (Aβ and tau) detection are invasive and high-priced, while neuropsychological tests (like the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA)) are subjective.
The overarching goal of our project is to examine gait, eye movement, and electric brain activity biomarkers for AD. These measures are widely recognized as biomarkers for various subgroups of mild cognitive impairment, however, a knowledge gap exists tied to AD markers in electric brain activity, movement, and ocular behavior of individuals with AD; and further examining the effectiveness of machine learning (ML) algorithms for predicting AD in individuals in conjunction with these measures.