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Image Search Results
Journal: Data in Brief
Article Title: A multimodal EMG and IMU dataset for assessing the quality of exercises designed for spatially constrained environments
doi: 10.1016/j.dib.2026.112797
Figure Lengend Snippet: Overview of the dataset file structure for each participant. Raw data are organized into EMG, IMU, and heart rate (HR) folders, while synchronized segmented recordings are divided into non-repetitive and repetitive exercises. A separate folder contains expert-based exercise quality labels.
Article Snippet: C)
Techniques:
Journal: Data in Brief
Article Title: A multimodal EMG and IMU dataset for assessing the quality of exercises designed for spatially constrained environments
doi: 10.1016/j.dib.2026.112797
Figure Lengend Snippet: Experimental setup for data acquisition. A) Frontal and B) posterior view of a participant equipped with surface electromyography and inertial measurement unit sensors. Sensors were applied in stacked configuration and secured with kinesiology tape. C) Xsens IMU sensor with depicted coordinate system, D) Diers surface EMG sensor, and E) detailed view of a Diers EMG sensor applied to the skin using Ag/AgCl electrodes.
Article Snippet: C)
Techniques:
Journal: Sensors (Basel, Switzerland)
Article Title: Stretch-ICP: A Continuous-Trajectory Registration and Deskewing Algorithm in Scenarios of Aggressive Motions
doi: 10.3390/s26082567
Figure Lengend Snippet: Composite image showing our localization system tumbling down a steep hill in one of the 32 runs of the TIGS dataset. The inset photo shows our rugged perception rig, with the numbers in the red circles corresponding to (1) XSens MTi-30 IMU, (2) VectorNav VN-100 IMU, (3) RoboSense RS-16 lidar, and (4) Raspberry Pi 4.
Article Snippet: The table shows that the angular velocities reached in TIGS exceed the operating range of several commonly used sensors, including the
Techniques:
Journal: Sensors (Basel, Switzerland)
Article Title: Stretch-ICP: A Continuous-Trajectory Registration and Deskewing Algorithm in Scenarios of Aggressive Motions
doi: 10.3390/s26082567
Figure Lengend Snippet: Density map of the TIGS dataset. The grayscale represents the number of data points acquired at the specific angular speeds and linear accelerations. The outlines represent the distributions in linear accelerations and angular speeds for similar datasets. The KITTI dataset is shown in green, the Newer College dataset is indicated in orange, and the Hilti-Oxford dataset is illustrated in red. The dashed line represents the manufacturer-specified saturation point of the MTi-30 gyroscope.
Article Snippet: The table shows that the angular velocities reached in TIGS exceed the operating range of several commonly used sensors, including the
Techniques:
Journal: Sensors (Basel, Switzerland)
Article Title: Stretch-ICP: A Continuous-Trajectory Registration and Deskewing Algorithm in Scenarios of Aggressive Motions
doi: 10.3390/s26082567
Figure Lengend Snippet: Qualitative and quantitative results on angular speed estimates for SAAVE. ( Left ) An example of the angular velocity over time for the saturated gyroscope (MTi-30) axis for one run of the TIGS dataset. The measurements from the MTi-30 gyroscope are shown in dashed blue, the ground truth measurements from a VN-100 gyroscope are indicated in dashed orange, and the angular speeds estimated with SAAVE using MTi-30 measurements are illustrated in purple. The purple-shaded area represents three standard deviations above and below the angular velocities estimated by SAAVE. The light gray zone indicates a gyroscope saturation episode. ( Right ) The error in angular velocities without (in blue) and with SAAVE (in purple) during saturation periods for all the runs of the TIGS dataset.
Article Snippet: The table shows that the angular velocities reached in TIGS exceed the operating range of several commonly used sensors, including the
Techniques: