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Skeletons Don’t Lie: Can AI Decode Your Moves?

Author: Mihai Nan

Hard
Your best score: N/A
Problem Description

🦴📊 Skeletons Don’t Lie: Can AI Decode Your Moves? 🤖


🧾 Description

We are given a dataset containing sequences of human movements, each composed of multiple frames (FrameNumber).
Each frame includes the 3D positions of 25 body joints, represented by the coordinates:
J1X, J1Y, J1Z, ..., J25X, J25Y, J25Z.

Each frame is associated with a sequence identifier (IDSample) and two labels:

  • Action — the action performed
  • Camera — the camera that recorded the sequence

To better understand the spatial distribution of the 25 joints:

Spatial distribution of the 25 joints


🔢 Possible Actions

  • 0: wear jacket
  • 1: take off a hat/cap
  • 2: hopping (one foot jumping)
  • 3: jump up
  • 4: falling

🎥 Possible Cameras

  • 1: 45° angle view (left or right)
  • 2: front view
  • 3: side view (left or right)

🧮 Subtask 1 — Counting Frames per Sequence (10 points)

For each unique IDSample, determine how many frames are available in the test dataset (test_data.csv).
This analysis helps understand the temporal-spatial distribution of the data for each action.


🧠 Subtask 2 — Sequence-Level Action Classification (40 points)

Using the training data, train a classification model capable of recognizing the action performed in a sequence of frames.
Apply the model to the test data and, for each unique IDSample, predict the most probable action.

📊 Evaluation Metric

The performance of the model will be evaluated using accuracy, defined as:

formula

To obtain full points, the achieved accuracy must be at least 0.965.


🎥 Subtask 3 — Camera Identification (50 points)

Using the training data, train a model capable of predicting which camera recorded a given sequence of frames.
Apply the model to the test set and specify, for each IDSample, the camera predicted as most likely.

📊 Evaluation Metric

The model performance is again evaluated using accuracy, defined as:

formula

To obtain full points, the achieved accuracy must be at least 0.8.


📦 Output Format

The final result must be a CSV file named output.csv, containing exactly 3 columns:

subtaskIDdatapointIDanswer
1IDSample from test_data.csvcorresponding answer for that subtask

🗂️ Useful Resources

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