Journal: bioRxiv

Article Title: Two distinct types of eye-head coupling in freely moving mice

doi: 10.1101/2020.02.20.957712

Figure Lengend Snippet: (A) Top: Head tilt was measured using the IMU sensor attached to the animal’s head. Eye positions were measured using the head-mounted camera system. Computational models were used to predict horizontal and vertical eye positions from head pitch and roll for each eye. Bottom: Measured (colored lines) and predicted (black lines) horizontal and vertical eye positions for both eyes. (B) Cross-validated explained variance along the horizontal (horiz.) and vertical (vert.) eye axes ( n = 47 recordings from 5 mice, 10 min each). Head tilt explained 86% variance in vertical but only 62% in horizontal eye position. Recordings for each eye axis pooled across eyes and mice. (C) Interocular correlation of the eye movements that were predictable by head pitch and roll (i.e. the predictions of independent models for the two eyes as shown in A). Strong negative correlation for horizontal eye movements indicates convergence and divergence across eyes. Blue arrows show horizontal convergence. Same data as in B. (D) Prediction errors for the eye position traces in A showed strong co-fluctuations in horizontal but not vertical eye direction. (E) Interocular correlation of the eye movements that were not predictable by head pitch and roll (i.e. the prediction errors of independent models for the two eyes as shown in D). There was a strong positive correlation for horizontal eye movements suggesting that conjugate eye movements occurred during head free behavior and were not explained by head tilt. Arrows show coupling for left eye rotating in nasal direction. Same data as in B.

Article Snippet: For the experiments in freely moving humans, eye position and head motion were recorded using a commercially-available head-mounted eye tracker with integrated IMU sensor (Tobii Pro Glasses 2, Tobii Pro, Sweden).

Techniques:

Journal: IEEE Open Journal of Engineering in Medicine and Biology

Article Title: Gaitmap—An Open Ecosystem for IMU-Based Human Gait Analysis and Algorithm Benchmarking

doi: 10.1109/OJEMB.2024.3356791

Figure Lengend Snippet: Overview of the Benchmark Challenges Currently Implemented in the Gaitmap-Challenges Package

Article Snippet: , SensorPositionComparison2019 , Algorithms need to estimate the stride length of strides performed at different speeds within the capture volume of a MoCap system. Two synchronized IMU sensors are mounted on the instep of the shoe and predefined stride borders and events are provided..

Techniques: Standard Deviation

Journal: Sensors (Basel, Switzerland)

Article Title: Real-Time Vehicle Roll Angle Estimation Based on Neural Networks in IoT Low-Cost Devices

doi: 10.3390/s18072188

Figure Lengend Snippet: Hardware and connectivity ( 1 ); Racelogic VBOX data logger ( 2 ); van equipped with GPS dual-antenna ( 3 ); low-cost kits and Racelogic VBOX IMU ( 4 , 5 ).

Article Snippet: The VBOX Component is in charge of managing the experiments execution using the Racelogic IMU sensor and GPS dual antenna data.

Techniques: