This article provides a comprehensive exploration of spatial operators in landscape ecology, serving as a critical resource for researchers and scientists.
This article provides a comprehensive framework for assessing the accuracy of machine learning (ML) models in behavioral classification, with a specific focus on applications in drug development and clinical research.
Robust behavior classification is fundamental to translational research, yet methods validated in one species often fail to generalize, creating a significant bottleneck in drug discovery.
This article provides a comprehensive framework for validating accelerometer-derived physical activity energy expenditure (PAEE) estimates, a critical capability for biomedical research and clinical trials.
This article provides a comprehensive analysis for researchers and drug development professionals on the critical, yet often overlooked, impact of biologger tag placement on the accuracy and interpretation of Dynamic...
This article provides a comprehensive comparison of supervised and unsupervised machine learning approaches for classifying behavior from accelerometer data, tailored for researchers and professionals in drug development and biomedical science.
This article provides a systematic comparison of accelerometer and GPS technologies for classifying animal behavior, a critical tool for biomedical and pharmacological research.
Accurately capturing short-burst, high-frequency animal behaviors—such as prey catching, swallowing, or escape maneuvers—with accelerometers presents unique methodological challenges.
This article provides a comprehensive guide for researchers and scientists on maximizing the operational lifespan of GPS-accelerometer biologging tags.
This article provides a comprehensive guide for researchers and drug development professionals on addressing the critical challenge of accelerometer data aliasing in animal studies.