Technology Category
- Analytics & Modeling - Machine Learning
- Wearables - Implants
Applicable Industries
- Education
- Transportation
Applicable Functions
- Product Research & Development
- Quality Assurance
Use Cases
- Public Transportation Management
- Time Sensitive Networking
Services
- Testing & Certification
- Training
About The Customer
The Datta Lab at Harvard Medical School is a research lab focused on studying the neural mechanisms associated with behavior in rodents. Their research involves placing mice in an experimental rig, recording each mouse’s behavior using cameras, and measuring each mouse’s neural activity using neural implants. The lab then analyzes this data to examine the link between neural activity and behavior, as well as to advance their understanding of specific brain areas of mice. The lab was previously spending a significant amount of time manually annotating video footage to interpret the behavioral data, which was detracting from the time that could be spent on other aspects of research.
The Challenge
The Datta Lab at Harvard Medical School is engaged in studying the neural mechanisms associated with behavior in rodents. Their research involves recording the behavior of mice using cameras and measuring their neural activity using neural implants. The challenge lies in the analysis of this data, particularly in interpreting the behavioral data. This requires the researchers to label the poses of the mouse over time. While machine learning models can automate this process, a significant amount of video footage needs to be manually annotated first. This annotation process is time-consuming and detracts from the time that researchers could be spending on other aspects of research that require their expertise. The lab was in need of a solution to speed up their data annotation process.
The Solution
The Datta Lab partnered with Scale Rapid to expedite their data annotation process. Instead of annotating their data in-house, they now send their raw videos of mice to Scale Rapid and receive quality annotations in return. These annotations provide the lab with the information they need to train their machine learning models for automated pose tracking. This partnership with Scale Rapid saves the lab weeks of labeling time, allowing researchers to spend their time more effectively. It also enables the lab to acquire a higher volume of annotations than they could ever accomplish in-house. This large volume of high-quality annotations makes it easier for the lab to train high-performing machine learning models, while also freeing up researcher time.
Operational Impact
Quantitative Benefit
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