Computer Science - Open Access Policy Deposits

This series is automatically populated with publications deposited by UC Santa Barbara Department of Computer Science researchers in accordance with the University of California’s open access policies. For more information see Open Access Policy Deposits and the UC Publication Management System.

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Aligning Visual Prosthetic Development With Implantee Needs

(2024)

Purpose

Visual prosthetics are a promising assistive technology for vision loss, yet research often overlooks the human aspects of this technology. While previous studies focus on the perceptual experiences or attitudes of implant recipients (implantees), a systematic account of how current implants are being used in everyday life is still lacking.

Methods

We interviewed six recipients of the most widely used visual implants (Argus II and Orion) and six leading researchers in the field. Through thematic analyses, we explored the daily usage of these implants by implantees and compared their responses to the expectations of researchers. We also sought implantees' input on desired features for future versions, aiming to inform the development of the next generation of implants.

Results

Although implants are designed to facilitate various daily activities, we found that implantees use them less frequently than researchers expect. This discrepancy primarily stems from issues with usability and reliability, with implantees finding alternative methods to accomplish tasks, reducing the need to rely on the implant. For future implants, implantees emphasized the desire for improved vision, smart integration, and increased independence.

Conclusions

Our study reveals a significant gap between researcher expectations and implantee experiences with visual prostheses. Although limited by access to a small population of implantees, this study highlights the importance of focusing future research on usability and real-world applications.

Translational relevance

This retrospective qualitative study advocates for a better alignment between technology development and implantee needs to enhance clinical relevance and practical utility of visual prosthetics.

Bayesian polynomial neural networks and polynomial neural ordinary differential equations.

(2024)

Symbolic regression with polynomial neural networks and polynomial neural ordinary differential equations (ODEs) are two recent and powerful approaches for equation recovery of many science and engineering problems. However, these methods provide point estimates for the model parameters and are currently unable to accommodate noisy data. We address this challenge by developing and validating the following Bayesian inference methods: the Laplace approximation, Markov Chain Monte Carlo (MCMC) sampling methods, and variational inference. We have found the Laplace approximation to be the best method for this class of problems. Our work can be easily extended to the broader class of symbolic neural networks to which the polynomial neural network belongs.

Airflow Modeling for Citrus under Protective Screens.

(2024)

This study explores the development and validation of an airflow model to support climate prediction for Citrus Under Protective Screens (CUPS) in California. CUPS is a permeable screen structure designed to protect a field of citrus trees from large insects including the vector that causes the devastating citrus greening disease. Because screen structures modify the environmental conditions (e.g., temperature, relative humidity, airflow), farm management and treatment strategies (e.g., pesticide spraying events) must be modified to account for these differences. Toward this end, we develop a model for predicting wind speed and direction in a commercial-scale research CUPS, using a computational fluid dynamics (CFD) model. We describe the model and validate it in two ways. In the first, we model a small-scale replica CUPS under controlled conditions and compare modeled and measured airflow in and around the replica structure. In the second, we model the full-scale CUPS and use historical measurements to back test the models accuracy. In both settings, the modeled airflow values fall within statistical confidence intervals generated from the corresponding measurements of the conditions being modeled. These findings suggest that the model can aid decision support and smart agriculture solutions for farmers as they adapt their farm management practices for CUPS structures.

OpenPiton4HPC: Optimizing OpenPiton Toward High-Performance Manycores

(2024)

The Efficacy of the Connect America Fund in Addressing US Internet Access Inequities

(2024)

Zoomie: A Software-like Debugging Tool for FPGAs

(2024)

Axonal stimulation affects the linear summation of single-point perception in three Argus II users.

(2024)

Purpose

Retinal implants use electrical stimulation to elicit perceived flashes of light ("phosphenes"). Single-electrode phosphene shape has been shown to vary systematically with stimulus parameters and the retinal location of the stimulating electrode, due to incidental activation of passing nerve fiber bundles. However, this knowledge has yet to be extended to paired-electrode stimulation.

Methods

We retrospectively analyzed 3548 phosphene drawings made by three blind participants implanted with an Argus II Retinal Prosthesis. Phosphene shape (characterized by area, perimeter, major and minor axis length) and number of perceived phosphenes were averaged across trials and correlated with the corresponding single-electrode parameters. In addition, the number of phosphenes was correlated with stimulus amplitude and neuroanatomical parameters: electrode-retina and electrode-fovea distance as well as the electrode-electrode distance to ("between-axon") and along axon bundles ("along-axon"). Statistical analyses were conducted using linear regression and partial correlation analysis.

Results

Simple regression revealed that each paired-electrode shape descriptor could be predicted by the sum of the two corresponding single-electrode shape descriptors (p<.001). Multiple regression revealed that paired-electrode phosphene shape was primarily predicted by stimulus amplitude and electrode-fovea distance (p<.05). Interestingly, the number of elicited phosphenes tended to increase with between-axon distance (p<.05), but not with along-axon distance, in two out of three participants.

Conclusions

The shape of phosphenes elicited by paired-electrode stimulation was well predicted by the shape of their corresponding single-electrode phosphenes, suggesting that two-point perception can be expressed as the linear summation of single-point perception. The notable impact of the between-axon distance on the perceived number of phosphenes provides further evidence in support of the axon map model for epiretinal stimulation. These findings contribute to the growing literature on phosphene perception and have important implications for the design of future retinal prostheses.

Explainable machine learning predictions of perceptual sensitivity for retinal prostheses.

(2024)

Objective

Retinal prostheses evoke visual precepts by electrically stimulating functioning cells in the retina. Despite high variance in perceptual thresholds across subjects, among electrodes within a subject, and over time, retinal prosthesis users must undergo `system fitting', a process performed to calibrate stimulation parameters according to the subject's perceptual thresholds. Although previous work has identified electrode-retina distance and impedance as key factors affecting thresholds, an accurate predictive model is still lacking.

Approach

To address these challenges, we 1) fitted machine learning (ML) models to a large longitudinal dataset with the goal of predicting individual electrode thresholds and deactivation as a function of stimulus, electrode, and clinical parameters (`predictors') and 2) leveraged explainable artificial intelligence (XAI) to reveal which of these predictors were most important.

Main results

Our models accounted for up to 76% of the perceptual threshold response variance and enabled predictions of whether an electrode was deactivated in a given trial with F1 and AUC scores of up to 0.730 and 0.910, respectively. Our models identified novel predictors of perceptual sensitivity, including subject age, time since blindness onset, and electrode-fovea distance.

Significance

Our results demonstrate that routinely collected clinical measures and a single session of system fitting might be sufficient to inform an XAI-based threshold prediction strategy, which has the potential to transform clinical practice in predicting visual outcomes. .

Cover page of Comparison of Rapid-, Kaolin-, and Native-TEG Parameters in Burn Patient Cohorts With Acute Burn-induced Coagulopathy and Abnormal Fibrinolytic Function.

Comparison of Rapid-, Kaolin-, and Native-TEG Parameters in Burn Patient Cohorts With Acute Burn-induced Coagulopathy and Abnormal Fibrinolytic Function.

(2024)

Although use of thromboelastography (TEG) to diagnose coagulopathy and guide clinical decision-making is increasing, relative performance of different TEG methods has not been well-defined. Rapid-TEG (rTEG), kaolin-TEG (kTEG), and native-TEG (nTEG) were performed on blood samples from burn patients presenting to a regional center from admission to 21 days. Patients were categorized by burn severity, mortality, and fibrinolytic phenotypes (Shutdown [SD], Physiologic [PHYS], and Hyperfibrinolytic [HF]). Manufacturer ranges and published TEG cutoffs were examined. Concordance correlations (Rc) of TEG parameters (R, α-angle, maximum amplitude [MA], LY30) measured agreement and Cohens Kappa (κ) determined interclass reliability. Patients (n = 121) were mostly male (n = 84; 69.4%), with median age 40 years, median TBSA burn 13%, and mortality 17% (n = 21). Severe burns (≥40% TBSA) were associated with lower admission α-angle for rTEG (P = .03) and lower MA for rTEG (P = .02) and kTEG (P = .01). MA was lower in patients who died (nTEG, P = .04; kTEG, P = .02; rTEG, P = .003). Admission HF was associated with increased mortality (OR, 10.45; 95% CI, 2.54-43.31, P = .001) on rTEG only. Delayed SD was associated with mortality using rTEG and nTEG (OR 9.46; 95% CI, 1.96-45.73; P = .005 and OR, 6.91; 95% CI, 1.35-35.48; P = .02). Admission TEGs showed poor agreement on R-time (Rc, 0.00-0.56) and α-angle (0.40 to 0.55), and moderate agreement on MA (0.67-0.81) and LY30 (0.72-0.93). Interclass reliability was lowest for R-time (κ, -0.07 to 0.01) and α-angle (-0.06 to 0.17) and highest for MA (0.22-0.51) and LY30 (0.29-0.49). Choice of TEG method may impact clinical decision-making. rTEG appeared most sensitive in parameter-specific associations with injury severity, abnormal fibrinolysis, and mortality.

Home-Use and Portable Biofeedback Lowers Anxiety and Pain in Chronic Pain Subjects.

(2023)

In this study, we investigated the use of novel, home-use and portable biofeedback devices in a remote program for managing chronic pain. In three separate 4-week pilot studies, participants engaged in twice-daily, 10-minute biofeedback sessions, with self-assessed reductions in anxiety and pain levels using the 6-item State-Trait Anxiety Inventory (STAI-6) and Visual Analogue Scale (VAS), respectively, in Studies 2 and 3. Among these 113 (Study 2) and 237 (Study 3) biofeedback sessions, 81 (∼72%) and 130 (∼55%) showed reductions in pain, while 93 (∼82%) and 184 (∼78%) experienced reductions in anxiety. A positive relationship was found between anxiety and pain reduction, indicating that larger reductions in anxiety correspond to larger reductions in pain. In Study 1, only anxiety reductions were measured: across 143 biofeedback sessions, 127 experienced reductions in anxiety (∼89%). Participants in all studies demonstrated reductions in baseline to final results in pain, anxiety, and showed increases in satisfaction and recovery. Our results provide strong evidence that portable biofeedback devices can enhance pain management programs by helping to alleviate anxiety and pain in individuals living with chronic conditions. This study can provide a basis for the integration of biofeedback devices into the expanding research of lifestyle and integrative medicine.