Uncertainty-Aware Estimation of Population Abundance using Machine Learning

Machine Learning is widely used for mining collections, such as images, sounds, or texts, by classifying their elements into categories. Automatic classication based on supervised learning requires groundtruth datasets for modeling the elements to classify, and for testing the quality of the classication. Because collecting groundtruth is tedious, a method for estimating the potential errors in large datasets based on limited groundtruth is ne

Development and Validation of the Tele-Pulmonary Rehabilitation Acceptance Scale

BACKGROUND: Using telehealth in pulmonary rehabilitation (telerehabilitation) is a new field of health-care practice. To successfully implement a telerehabilitation program, measures of acceptance of this new type of program need to be assessed among potential users. The purpose of this study was to develop a scale to measure acceptance of using telerehabilitation by health-care practitioners and patients. METHODS: Three objectives were met (a) constructing a modified scale of the technology acceptance model, (b) judging the items for content validity, and (c) judging the scale for face validity. Nine experts agreed to participate and evaluate item relevance to theoretical definitions of domains. To establish face validity, 7 health-care practitioners and 5 patients were interviewed to provide feedback about the scale's clarity and ease of reading. RESULTS: The final items were divided into 2 scales that reflected the health-care practitioner and patient responses. Each scale included 3 subscales: perceived usefulness, perceived ease of use, and behavioral intention. CONCLUSIONS: The 2 scales, each with 3 subscales, exhibited evidence of content validity and face validity. The 17-item telerehabilitation acceptance scale for health-care practitioners and the 13-item telerehabilitation acceptance scale among patients warrant further psychometric testing as valuable measures for pulmonary rehabilitation programs

Health Care Practitioners’ Determinants of Telerehabilitation Acceptance

Background: Pulmonary rehabilitation is a multidisciplinary patient-tailored intervention that aims to improve the physical and psychological condition of people with chronic respiratory diseases. Providing pulmonary rehabilitation (PR) services to the growing population of patients is challenging due to shortages in health care practitioners and pulmonary rehabilitation programs. Telerehabilitation has the potential to address this shortage in practitioners and PR programs as well as improve patients’ participation and adherence. This study’s purpose was to identify and evaluate the influences of intention of health care practitioners to use telerehabilitation. Methods: Data were collected through a self-administered Internet-based survey. Results: Surveys were completed by 222 health care practitioners working in pulmonary rehabilitation with 79% having a positive intention to use telerehabilitation. Specifically, perceived usefulness was a significant individual predictor of positive intentions to use telerehabilitation. Conclusion: Perceived usefulness may be an important factor associated with health care providers’ intent to use telerehabilitation for pulmonary rehabilitation

A brief introduction to mixed effects modelling and multi-model inference in ecology

Acknowledgements This paper is the result of a University of Exeter workshop on best practice for the application of mixed effects models and model selection in ecological studies Funding Xavier A. Harrison was funded by an Institute of Zoology Research Fellowship. David Fisher was funded by NERC studentship NE/H02249X/1. Lynda Donaldson was funded by NERC studentship NE/L501669/1. Beth S. Robinson was funded by the University of Exeter and the Animal and Plant Health Agency as part of ‘Wildlife Research Co-Operative’. Maria Correa-Cano was funded by CONACYT (The Mexican National Council for Science and Technology) and SEP (The Mexican Ministry of Education). Cecily Goodwin was funded by the Forestry Commission and NERC studentship NE/L501669/1. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD

Long-term underwater camera surveillance for monitoring and analysis of fish populations

Long-term monitoring of the underwater environment is still labour intensive work. Using underwater surveillance cameras to monitor this environment has the potential advantage to make the task become less labour intensive. Also, the obtained data can be stored making the research reproducible. In this work, a system to analyse long-term underwater camera footage (more than 3 years of 12 hours a day underwater camera footage from 10 cameras) is described. This system uses video processing software to detect and recognise fish species. This footage is processed on supercomputers, which allow marine biologists to request automatic processing on these videos and afterwards analyse the results using a web-interface that allows them to display counts of fish species in the camera footage

Uncertainty-aware estimation of population abundance using machine learning

Machine Learning is widely used for mining collections, such as images, sounds, or texts, by classifying their elements into categories. Automatic classification based on supervised learning requires groundtruth datasets for modeling the elements to classify, and for testing the quality of the classification. Because collecting groundtruth is tedious, a method for estimating the potential errors in large datasets based on limited groundtruth is needed. We propose a method that improves classification quality by using limited groundtruth data to extrapolate the po-tential errors in larger datasets. It significantly improves the counting of elements per class. We further propose visualization designs for understanding and evaluating the classification un-certainty. They support end-users in considering the impact of potential misclassifications for interpreting the classification output. This work was developed to address the needs of ecologists studying fish population abundance using computer vision, but generalizes to a larger range of applications. Our method is largely applicable for a variety of Machine Learning technologies, and our visualizations further support their transfer to end-users