Multimodal Data Capture
Multimodal Data Capture - Related Videos
04:47
Multimodal Optical Imaging Platform for Studying Cellular Metabolism
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2025
Utilizing a multimodal platform combining label-free optical imaging modalities, we have developed a protocol for visualizing and quantifying cellular dynamics and metabolism. Through imaging via multiphoton fluorescence, second harmonic generation, and stimulated Raman scattering microscopy, we can generate a holistic overview of the cellular and molecular environment.
07:13
Biomolecular Imaging of Cellular Uptake of Nanoparticles using Multimodal Nonlinear Optical Microscopy
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Cited by 1 •
2022
This article presents the integration of a spectral-focusing module and a dual-output pulse laser, enabling rapid hyperspectral imaging of gold nanoparticles and cancer cells. This work aims to demonstrate the details of multimodal nonlinear optical techniques on a standard laser scanning microscope.
07:13
Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities
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Cited by 3 •
2023
The combination of multiple imaging modalities is often necessary to gain a comprehensive understanding of pathophysiology. This approach utilizes phantoms to generate a differential transformation between the coordinate systems of two modalities, which is then applied for co-registration. This method eliminates the need for fiducials in production scans.
10:37
A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells
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2025
This protocol presents a multimodal approach combining Raman spectroscopy and tomographic phase microscopy for label-free, non-invasive phenotyping of living human breast cancer cells. It includes a user-friendly, human-bias-free analysis pipeline for rapid, quantitative morpho-chemical analysis, providing detailed molecular and structural insights with applications in cancer research and cell biology.
06:05
The Participant-Reported Implementation Update and Score (PRIUS): A Novel Method for Capturing Implementation-Related Data Over Time
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2021
This protocol describes a novel method for collecting and analyzing data related to ongoing implementation called the Participant-Reported Implementation Update and Score (PRIUS). The PRIUS method allows for the efficient and systematic capture of data over time and from multiple viewpoints in healthcare settings.
FAQs
Synchronization establishes when each measurement occurred, while calibration makes readings comparable across sensors and instruments. Without these steps, signals may appear related simply because they were misaligned in time, space, or scale. Proper coordination allows data fusion methods and downstream algorithms to associate physical events correctly, improving pattern identification and the reliability of engineering models.
Different sensors reveal different aspects of the same system, such as visual behavior, sound, applied force, or environmental conditions. Combining these signals can expose relationships that remain invisible in an isolated measurement. This broader evidence supports more complete situational awareness, helping engineering algorithms distinguish meaningful patterns from partial observations and improving the basis for fault detection or control.
The main alignment factors are time, space, and scale. Measurements must correspond to the same event, location, and interpretive range before they can be combined meaningfully. Poor alignment can weaken relationships between signals and reduce model accuracy, whereas coordinated measurements preserve the connections needed for reliable data fusion and clearer interpretation of complex engineered systems.
An engineering workflow begins by selecting complementary information sources, such as cameras, microphones, force sensors, or environmental instruments. The sources then record the system in a coordinated manner. Engineers synchronize the measurements, calibrate their relationships, and fuse the aligned data across relevant time, space, and scale. The resulting integrated dataset can support algorithmic analysis, monitoring, or control.
Applications include robotics, predictive maintenance, digital twins, quality inspection, and human-machine interaction. In robotics and interaction systems, integrated signals can strengthen situational awareness. For maintenance and inspection, combined evidence supports fault detection and evaluation. Digital twins and related models benefit from richer measurements that improve the accuracy of representations used to understand engineered systems.
As engineered systems become more connected, important evidence is distributed across multiple sensing sources rather than contained in one measurement. Coordinating those sources helps engineers evaluate relationships during design, monitoring, and control. The resulting integrated view can improve model accuracy and support decisions that depend on understanding system behavior across different physical signals and measurement scales.