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: Wearable Technologies

Article Title: A wearable gait lab powered by sensor-driven digital twins for quantitative biomechanical analysis post-stroke

doi: 10.1017/wtc.2024.14

Figure Lengend Snippet: Schematics of the IMU-based pipeline to extract joint angles and ankle torques from accelerations and quaternions as well as person-specific anthropometric measures. Using the OpenSim software, the mass and height are used to scale the default musculoskeletal model (a) to the specific participant measures. The optimal fiber length and the tendon slack length of the scaled model are then optimized (b). The IMU are placed on the model (c) and then used to perform inverse kinematics (d) and obtain joint angles. IMU accelerations are used to detect gait phases (e) and estimate the total 3D GRF (f). The 3D GRF is split into right and left GRFs using the STA (g) and the detected gait phases. The inverse kinematics input is used to track the heel, toes, and CoM of the calcaneus position ( x , y , z ). Those together with the detected gat phases are used to estimate the CoP (i). Estimated CoP and right and left GRFs are input to the inverse dynamics tool (j) to finally compute the ankle dorsi-plantar flexion torque.

Article Snippet: Using the OpenSim IMU placer tool, IMU orientations, represented with quaternions, during a static pose were used to register each IMU sensor to a specific body segment of the scaled and optimized model ( ).

Techniques: Software