Mobile, Game-Based Training for Myoelectric Prosthesis Control

Myoelectric prostheses provide upper limb amputees with hand and arm movement control using muscle activity of the residual limb, but require intensive training to effectively operate. The result is that many amputees abandon their prosthesis before mastering control of their device. In the present study, we examine a novel, mobile, game-based approach to myoelectric prosthesis training. Using the non-dominant limb in a group of able-bodied participants to model amputee pre-prosthetic training, a significant improvement in factors underlying successful myoelectric prosthesis use, including muscle control, sequencing, and isolation were observed. Participants also reported high levels of usability, and motivation with the game-based approach to training. Given fiscal or geographic constraints that limit pre-prosthetic amputee care, mobile myosite training, as described in the current study, has the potential to improve rehabilitation success rates by providing myosite training outside of the clinical environment. Future research should include longitudinal studies in amputee populations to evaluate the impact of pre-prosthetic training methods on prosthesis acceptance, wear time, abandonment, functional outcomes, quality of life, and return to work

Combining EEG and Eye Tracking: Using Fixation-Locked Potentials in Visual Search

Visual search is a complex task that involves many neural pathways to identify relevant areas of interest within a scene. Humans remain a critical component in visual search tasks, as they can effectively perceive anomalies within complex scenes. However, this task can be challenging, particularly under time pressure. In order to improve visual search training and performance, an objective, process-based measure is needed. Eye tracking technology can be used to drive real-time parsing of EEG recordings, providing an indication of the analysis process. In the current study, eye fixations were used to generate ERPs during a visual search task. Clear differences were observed following performance, suggesting that neurophysiological signatures could be developed to prevent errors in visual search tasks

Integration of the Pleasant Events and Activity Restriction Models: Development and Validation of a “PEAR” Model of Negative Outcomes in Alzheimer's Caregivers

This study examined an activity restriction/pleasurable activities mismatch model for psychosocial and health-related outcomes. A total of 108 spousal caregivers of patients with Alzheimer's Disease (AD) were assessed for their experience of social and recreational activities over the past month as well as their perception of how restricted they were for engaging in social and recreational activities. Participants were divided into three groups based on their reported activities and activity restriction: HPLR=High Pleasant Events+Low Activity Restriction (i.e., reference group; N=28); HPHR/LPLR=Either High Pleasant Events+High Activity Restriction or Low Pleasant Events+Low Activity Restriction (N=43); LPHR=Low Pleasant Events+High Activity Restriction (N=37). We hypothesized that participants reporting low pleasant events combined with high activity restriction (LPHR) would demonstrate greater disturbance relative to other two groups in multiple outcome domains, including: (a) greater mood disturbance, (b) greater use of negative coping factors, (c) reduced use of positive coping strategies, (d) reduced report of psychological resource factors (e.g., personal mastery, self-efficacy), and (e) increased report of subjective health difficulties (e.g., sleep disturbance). Results generally supported our hypotheses, suggesting that assessment of both constructs is important for best predicting quality of well-being in AD caregivers, and potentially for establishing maximal effect in behavior therapy for caregivers

PhD

dissertationImplanted biomedical devices play an important role in the treatment of central nervous system diseases and disorders, but are also subject to a foreign body response which has the potential to affect device function. For example, the recordings obtained from the cerebral cortex using penetrating microelectrode arrays have been shown to be sufficient in limited clinical trials to control computers or external devices in the field of neuroprosthetics, but such recordings are largely inconsistent in chronic applications. Following implantation, a local area of inflammation develops surrounding the implant, which consists of activated microglia/macrophages, astrocyte hypertrophy, and neuronal loss, and which appears to last for the lifetime of the implant. The focus of this dissertation was to investigate the local tissue response in rat cerebral cortex adjacent to microwires, silicon microelectrodes with different surface chemistries, and microelectrode designs with decreased surface area available for inflammatory cell attachment

Improved Mental Acuity Forecasting with an Individualized Quantitative Sleep Model

Sleep impairment significantly alters human brain structure and cognitive function, but available evidence suggests that adults in developed nations are sleeping less. A growing body of research has sought to use sleep to forecast cognitive performance by modeling the relationship between the two, but has generally focused on vigilance rather than other cognitive constructs affected by sleep, such as reaction time, executive function, and working memory. Previous modeling efforts have also utilized subjective, self-reported sleep durations and were restricted to laboratory environments. In the current effort, we addressed these limitations by employing wearable systems and mobile applications to gather objective sleep information, assess multi-construct cognitive performance, and model/predict changes to mental acuity. Thirty participants were recruited for participation in the study, which lasted 1 week. Using the Fitbit Charge HR and a mobile version of the automated neuropsychological assessment metric called CogGauge, we gathered a series of features and utilized the unified model of performance to predict mental acuity based on sleep records. Our results suggest that individuals poorly rate their sleep duration, supporting the need for objective sleep metrics to model circadian changes to mental acuity. Participant compliance in using the wearable throughout the week and responding to the CogGauge assessments was 80%. Specific biases were identified in temporal metrics across mobile devices and operating systems and were excluded from the mental acuity metric development. Individualized prediction of mental acuity consistently outperformed group modeling. This effort indicates the feasibility of creating an individualized, mobile assessment and prediction of mental acuity, compatible with the majority of current mobile devices

Development and Clinical Evaluation of an mHealth Application for Stress Management

A large number of individuals experience mental health disorders, with cognitive behavioral therapy (CBT) emerging as a standard practice for reduction in psychiatric symptoms including stress, anger, anxiety and depression. However, CBT is associated with significant patient dropout, and lacks the means to provide objective data regarding a patient’s experience and symptoms between sessions. Emerging wearables and mobile health (mHealth) applications represent an approach that may provide objective data to the patient and provider between CBT sessions. Here we describe the development of a classifier of real-time physiological stress in a healthy population (n=35), and apply it in a controlled clinical evaluation for armed forces veterans undergoing CBT for stress and anger management (n=16). Using cardiovascular and electrodermal inputs from a wearable device, the classifier was able to detect physiological stress in a non-clinical sample with an accuracy greater than 90%. In a small clinical sample, patients who used the classifier and an associated mHealth application were less likely to discontinue therapy (p=0.016, d=1.34) and significantly improved on measures of stress (p=0.032, d=1.61), anxiety (p=0.050, d=1.26), and anger (p=0.046, d=1.41) compared to controls undergoing CBT alone. Given the large number of individuals that experience mental health disorders, and the unmet need for treatment, especially in developing nations, such mHealth approaches have the potential to provide or augment treatment at low cost in the absence of in-person care