Biology subjects

Shorter, K. A.

Publications and source records attributed to Shorter, K. A..

5 recordsLinked to original sources

bioRxiv2023

Understanding the Influence of Context on Real-World Walking Energetics

Speeds that minimize energetic cost during steady-state walking have been observed during lab-based investigations of walking biomechanics and energetics. However, in real-world scenarios, humans walk in various contexts that elicit different walking strategies, which may not always prioritize minimizing energetic cost. To investigate whether individuals tend to select energetically optimal speeds in real-world situations and how contextual factors influence gait, we conducted a study combining data from lab and real-world experiments. Walking kinematics and context were measured during daily life over a week (N = 17) using wearable sensors and a mobile phone. To determine context, we utilized self-reported activity logs, GPS data, and follow-up exit interviews. Additionally, we estimated energetic cost using respirometry over a range of gait speeds in the lab. Cost of transport during these trials was used to identify an energetically optimal walking speed range for each participant. The proportion of real world steady-state stride speeds within this range was identified for all data and for each context. We found that participants walked significantly faster than what was energetically optimal, and the proportion of steady-state strides within the energetically optimal speed range was dependent on the context. On average, 45.6% of all steady-state stride speeds were energetically optimal across all contexts for all participants. These results suggest that while energetic cost is a factor considered by humans when selecting gait speed in daily life, it is not the sole determining factor. Context contributes to the observed variability in movement parameters both within and between individuals.

animal behavior and cognition↗

bioRxiv2022

Dynamics and Energetics of Bottlenose Dolphins (Tursiops truncatus) Fluke-and-Glide Gait

Intermittent locomotion composed of periods of active flapping/stroking followed by inactive gliding has been observed with species that inhabit both aerial and marine environments. However, studies on the energetic benefits of a fluke-and-glide (FG) gait during horizontal locomotion are limited for dolphins. This work presents a physics-based model of FG gait and analysis of the associated costs of transport of bottlenose dolphins (Tursiops truncatus). New estimates of gliding drag coefficients for the model were estimated experimentally from free-swimming bottlenose dolphins. The data-driven approach used kinematic measurement from 84 hours of biologging tag data collected from 3 animals to estimate the coefficients. A set of 532 qualified gliding events were automatically extracted for gliding drag coefficient estimation, and an additional 783 FG bouts were parameterized and used to inform the model-based dynamic analysis. Experimental results indicate that FG gait was preferred at speeds around 2.2 - 2.7 m/s. Observed FG bouts had an average duty factor of 0.45 and gliding duration of 5 sec. The average associated metabolic cost of transport (COT) and mechanical cost of transport (MECOT) of FG gait are 2.53 and 0.35 J {middle dot} m-1 {middle dot} kg-1 at the preferred speeds. This corresponded to an 18.9% and 27.1% reduction in cost when compared to modeled continuous fluking gait at the same reference speed. Average thrust was positively correlated with fluking frequency and amplitude as animals accelerated during the FG bouts. While fluking frequency and amplitude were negatively correlated for a given thrust range. These results support the supposition that FG gait enhances the horizontal swimming efficiency of bottlenose dolphins and provides new dynamical insights into the gait of these animals.

animal behavior and cognition↗

bioRxiv2021

Pose-gait analysis for cetaceans with biologging tags

Biologging tags are a key enabling tool for investigating cetacean behavior and locomotion in their natural habitat. Identifying and then parameterizing gait from movement sensor data is critical for these investigations. But how best to characterize gait from tag data remains an open question. Further, the location and orientation of the tag on an animal in the field are variable and can change multiple times during deployment. As a result, the relative orientation of the tag with respect to (wrt) the animal must be determined before a wide variety of further analyses. Currently, custom scripts that involve specific manual heuristics methods tend to be used in the literature. These methods require a level of knowledge and experience that can affect the reliability and repeatability of the analysis. The authors of this work argue that an animals gait is composed of a sequence of body poses observed by the tag, demonstrating a specific spatial pattern in the data that can be utilized for different purposes. This work presents an automated data processing pipeline (and software) that takes advantage of the common characteristics of pose and gait of the animal to 1) Identify time instances associated with occurrences of relative motion between the tag and animal; 2) Identify the relative orientation of tag wrt the animals body for a given data segment; and 3) Extract gait parameters that are invariant to pose and tag orientation. The authors included biologging tag data from bottlenose dolphins, humpback whales, and beluga whales in this work to validate and demonstrate the approach. Results show that the average relative orientation error of the tag wrt the dolphins body after processing was within 11 degrees in roll, pitch, and yaw directions. The average precision and recall for identifying relative tag motion were 0.87 and 0.89, respectively. Examples of the resulting pose and gait analysis demonstrate the potential of this approach to enhance studies that use tag data to investigate movement and behavior. MATLAB source code and data presented in the paper were made available to the public (https://github.com/ding-z/cetacean-pose-gait-analysis.git), with suggestions related to tag data processing practices provided in this paper. The proposed analysis approach will facilitate the use of biologging tags to study cetacean locomotion and behavior.

animal behavior and cognition↗

bioRxiv2021

Vision-based monitoring and measurement of bottlenose dolphins' daily habitat use and kinematics

This research presents a framework to enable computer-automated observation and monitoring of bottlenose dolphins (Tursiops truncatus) in a professionally managed environment. Results from this work provide insight into the dolphins movement patterns, kinematic diversity, and how changes in the environment affect their dynamics. Fixed overhead cameras were used to collect [~]100 hours of observations, recorded over multiple days including time both during and outside of formal training sessions. Animal locations were estimated using convolutional neural network (CNN) object detectors and Kalman filter post-processing. The resulting animal tracks were used to quantify habitat use and animal dynamics. Additionally, Kolmogorov-Smirnov analyses of the swimming kinematics were used for high-level behavioral mode classification. The detectors achieved a minimum Average Precision of 0.76. Performing detections and post-processing yielded 1.24x107 estimated dolphin locations. Animal kinematic diversity was found to be lowest in the morning and peaked immediately before noon. Regions of the habitat displaying the highest activity levels correlated to locations associated with animal care specialists, conspecifics, or enrichment. The work presented here demonstrates that CNN object detection is not only viable for large-scale marine mammal tracking, it also enables automated analyses of dynamics that provide new insight into animal movement and behavior.

animal behavior and cognition↗

bioRxiv2019

Augmenting biologging with supervised machine learning to study in situ behavior of the medusa Chrysaora fuscescens

Zooplankton occupy critical roles in marine ecosystems, yet their fine-scale behavior remains poorly understood due to the difficulty of studying individuals in situ. Here we combine biologging with supervised machine learning (ML) to demonstrate a pipeline for studying in situ behavior of larger zooplankton such as jellyfish. We deployed the ITAG, a biologging package with high-resolution motion sensors designed for soft-bodied invertebrates, on 8 Chrysaora fuscescens in Monterey Bay, using the tether method for retrieval. Using simultaneous video footage of the tagged jellyfish, we develop ML methods to 1) identify periods of tag data corrupted by the tether method, which may have compromised prior research findings, and 2) classify jellyfish behaviors. Our tools yield characterizations of fine-scale jellyfish activity and orientation over long durations, and provide evidence that developing behavioral classifiers on in situ rather than laboratory data is essential.\n\nSummary StatementHigh-resolution motion sensors paired with supervised machine learning can be used to infer fine-scale in situ behavior of zooplankton for long durations.

animal behavior and cognition↗