Bounding Box Annotation for Fall Detection AI Model

Client's Challenge & Our Solution

A health-tech company developing a fall detection AI model for elderly care needed precise video annotations to train their algorithms. The objective was to accurately identify and track body movements to detect and predict falls in real time. The client provided 60 hours of video footage featuring elderly individuals in controlled environments simulating falls and daily activities. The primary challenge was annotating keyframes with bounding boxes to highlight movements leading up to and during falls while ensuring no false positives for regular movements.

FutureBeeAI assembled a dedicated team of expert annotators and quality reviewers to handle this sensitive project. Using bounding box annotation, we meticulously labeled body movements, providing data crucial for the AI model’s development. Throughout the project, we maintained strict adherence to privacy standards, ensuring ethical handling of sensitive footage.

Outcome & Features:

Annotated 60 hours of video footage with bounding boxes around individuals to track body movements.

Delivered datasets with accuracy, enabling the client to achieve precise fall detection and prediction.

Completed the project within 10 weeks, exceeding the client’s timeline expectations.