Biology subjects

Rhodin, H.

Publications and source records attributed to Rhodin, H..

3 recordsLinked to original sources

bioRxiv2025

The Effective Inertia of the Lower Limb During Locomotion

Accurate modelling of inertial properties of human lower limbs is of great interest to many tasks, from gait analysis in biomechanics to motion tracking and control in computer animation. Previous work typically simplified the human musculoskeletal structure as a chain of rigid capsules, with muscle mass lumped with body segments. Such simplifications lead to errors in the inertia matrix, and the error propagates to torque and pose estimates. In this study, we developed a data-driven model to represent the joint-space inertia of the lower-body of a human in motion. The model does not make any assumptions other than that the estimated inertia matrices must be symmetric and positive definite. We show that a joint-space inertia matrix, estimated from synchronized motion and ground force data following foot strikes, reveals inertial coupling, and that estimated inertia matrices are bilaterally symmetric and motion-type dependent. These are properties which a rigid, mass-lumped inertia matrix fails to entail. Moreover, we show that the data-driven model fits to data better than the articulated rigid body inertia model, and that when used for reconstructing lower body kinematics estimated inertia yields more accurate and stable motion.

bioengineering↗

bioRxiv2020

LiftPose3D, a deep learning-based approach for transforming 2D to 3D pose in laboratory animals

Markerless 3D pose estimation has become an indispensable tool for kinematic studies of laboratory animals. Most current methods recover 3D pose by multi-view triangulation of deep network-based 2D pose estimates. However, triangulation requires multiple, synchronized cameras and elaborate calibration protocols that hinder its widespread adoption in laboratory studies. Here, we describe LiftPose3D, a deep network-based method that overcomes these barriers by reconstructing 3D poses from a single 2D camera view. We illustrate LiftPose3Ds versatility by applying it to multiple experimental systems using flies, mice, rats, and macaque monkeys and in circumstances where 3D triangulation is impractical or impossible. Our framework achieves accurate lifting for stereotyped and non-stereotyped behaviors from different camera angles. Thus, LiftPose3D permits high-quality 3D pose estimation in the absence of complex camera arrays, tedious calibration procedures, and despite occluded body parts in freely behaving animals.

animal behavior and cognition↗

bioRxiv2019

DeepFly3D: A deep learning-based approach for 3D limb and appendage tracking in tethered, adult Drosophila

Studying how neural circuits orchestrate limbed behaviors requires the precise measurement of the positions of each appendage in 3-dimensional (3D) space. Deep neural networks can estimate 2-dimensional (2D) pose in freely behaving and tethered animals. However, the unique challenges associated with transforming these 2D measurements into reliable and precise 3D poses have not been addressed for small animals including the fly, Drosophila melanogaster. Here we present DeepFly3D, a software that infers the 3D pose of tethered, adult Drosophila--or other animals--using multiple camera images. DeepFly3D does not require manual calibration, uses pictorial structures to automatically detect and correct pose estimation errors, and uses active learning to iteratively improve performance. We demonstrate more accurate unsupervised behavioral embedding using 3D joint angles rather than commonly used 2D pose data. Thus, DeepFly3D enables the automated acquisition of behavioral measurements at an unprecedented level of resolution for a variety of biological applications.

animal behavior and cognition↗