Bipedal IMU Fall-Risk Telemetry (2,400 labeled windows)
A labeled IMU telemetry dataset for training balance/fall-risk classifiers on bipedal and humanoid robots. 2,400 rows, each a 0.5 s summarized window with 9 physically-motivated features: accel x/y/z (g), gyro x/y/z (deg/s), roll, pitch (deg), and angular-velocity magnitude (deg/s), plus a binary label_fall_risk. Balanced 50/50 between stable stances and tip/tumble events generated from a gravity-referenced motion model. CSV with header; drop-in for scikit-learn, PyTorch, or TensorFlow. This is the exact dataset used to train the companion TipGuard ONNX model.
Demonstration clipfree preview
Left: real-world source footage. Right: a signal derived from it (inter-frame motion field or edge/feature map). Source & license are credited in the clip.
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Free preview — no purchase requiredFrameworks
- PyTorchONNX Runtime
Hardware
- Not specified
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- Size: 1.77 MB
- Duration: 10s
Tags
Free
MIT license
bipedal-imu-fall-risk-telemetry-2-400-labeled-windows-1.zip · 1.77 MB
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Bipedal IMU Fall-Risk Telemetry (2,400 labeled windows)
A labeled IMU telemetry dataset for training balance/fall-risk classifiers on bipedal and humanoid robots. 2,400 rows, each a 0.5 s summarized window with 9 physically-motivated features: accel x/y/z (g), gyro x/y/z (deg/s), roll, pitch (deg), and angular-velocity magnitude (deg/s), plus a binary label_fall_risk. Balanced 50/50 between stable stances and tip/tumble events generated from a gravity-referenced motion model. CSV with header; drop-in for scikit-learn, PyTorch, or TensorFlow. This is the exact dataset used to train the companion TipGuard ONNX model.