AI Human Activity Recognition for On-board BAS Experiments
Indian Space Research Organisation(ISRO)
- Organisation
- Indian Space Research Organisation(ISRO)
- Department
- Department of Space / Indian Space Research Organisation
- Category
- Software
- Theme
- Miscellaneous
- Submission deadline
- 20 September 20262026-09-20
- Ideas submitted
- 0/500
- Serial number
- 174
- Data captured on
- 2026-08-23
- Dataset link
- This problem requires synthetic dataset generation. Teams have to build a custom, highly focused local dataset (even just using a webcam) replicating a specific experiment. For this particular problem, following is the sequence of steps in a sample experiment:
- Sample Experiment You are given a box that contains two smaller boxes of color red and
- Contact info
- No contact published
- Youtube link
- No video published
Background As humanity aims for space missions such as BAS and lunar missions, real-time ground support becomes impossible due to communication delays. An AI-based HAR system acts as an on-board assistant that supports the execution of scientific experiments, ensuring the success of science beyond Earth's orbit. In the space environment, AI-based HAR system may act as mission-critical support for astronauts. By tracking astronaut movements and activities in real time, HAR ensures scientific experiments and related protocols are executed flawlessly without requiring constant, high-bandwidth communication with mission control. Description Challenge is to design and train an AI model that recognizes and validates the sequence of a pre-defined experiment using human activity recognition techniques. Standalone operation: Space stations operate on restricted data bandwidth to Earth. Rather than streaming raw video to ground control, data is processed locally at the 'edge.' Inputs are given from fixed-payload cameras. Dataset generation to train model for object detection, pose estimation and hand-object interaction based on the steps of the experiment. Optional: Another challenge is that Standard 2D or ground-based 3D posture models fail because astronauts do not have a fixed 'up' or 'down' orientation. The AI model should use orientation-agnostic 3D Human Mesh Recovery (HMR) to track the astronaut’s body relative to the payload rack, not the floor. Expected Solution
- The software should continuously process local video feeds to track the sequence of experiment.
- At the start or after each step, the model should suggest the next step to be performed.
- It should alert when a step is skipped or an out of sequence step is added. It should be a voice based alert.
- Using the live video, it should generate a timestamped and structured lightweight text file of the conducted steps with outcomes/ status.
- Stream the video of the experiment to specific IP and also store the video locally.
- A graphical user interface for monitoring the above activities.
- Deliverable: A trained AI model that runs on offline standalone system