Multimodal object recognition is still an emerging field. Thus, publicly available datasets are still rare and of small size. This dataset was developed to help fill this void and presents multimodal data for 63 objects with some visual and haptic ambiguity. The dataset contains visual, kinesthetic and tactile (audio/vibrations) data. To completely solve sensory ambiguity, sensory integration/fusion would be required. This report describes the creation and structure of the dataset. The first section explains the underlying approach used to capture the visual and haptic properties of the objects. The second section describes the technical aspects (experimental setup) needed for the collection of the data. The third section introduces the objects, while the final section describes the structure and content of the dataset.
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Boombox is a multi-modal dataset for visual reconstruction from acoustic vibrations. Involves dropping objects into a box and capturing resulting images and vibrations. Used for training ML systems that predict images from vibration.
Mudestreda Multimodal Device State Recognition Dataset obtained from real industrial milling device with Time Series and Image Data for Classification, Regression, Anomaly Detection, Remaining Useful Life (RUL) estimation, Signal Drift measurement, Zero Shot Flank Took Wear, and Feature Engineering purposes.
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Human activity recognition and clinical biomechanics are challenging problems in physical telerehabilitation medicine. However, most publicly available datasets on human body movements cannot be used to study both problems in an out-of-the-lab movement acquisition setting. The objective of the VIDIMU dataset is to pave the way towards affordable patient tracking solutions for remote daily life activities recognition and kinematic analysis.