TipGuard — Bipedal Fall-Risk Detector (ONNX)
TipGuard is a lightweight (4.7 KB) ONNX classifier that flags imminent loss-of-balance on bipedal/humanoid robots from a single IMU window. Input is a 9-feature vector — accel x/y/z (g), gyro x/y/z (deg/s), roll, pitch (deg), and angular-velocity magnitude (deg/s); output is a 2-class softmax probability [stable, fall_risk]. Feature normalization is baked into the graph, so you feed raw IMU readings directly. Trained on the companion Bipedal IMU Fall-Risk Telemetry dataset (2,400 labeled 0.5 s windows) and exported at opset 17. Runs in microseconds on Jetson Orin / any ONNX Runtime target — ideal as a safety reflex node. Validated example: a stable stance returns P(fall)=0.00; a 50+ deg/s tumbling window returns P(fall)=1.00.
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: 369 KB
- Duration: 10s
Tags
Free
MIT license
tipguard-bipedal-fall-risk-detector-onnx-1.zip · 369 KB
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TipGuard — Bipedal Fall-Risk Detector (ONNX)
TipGuard is a lightweight (4.7 KB) ONNX classifier that flags imminent loss-of-balance on bipedal/humanoid robots from a single IMU window. Input is a 9-feature vector — accel x/y/z (g), gyro x/y/z (deg/s), roll, pitch (deg), and angular-velocity magnitude (deg/s); output is a 2-class softmax probability [stable, fall_risk]. Feature normalization is baked into the graph, so you feed raw IMU readings directly. Trained on the companion Bipedal IMU Fall-Risk Telemetry dataset (2,400 labeled 0.5 s windows) and exported at opset 17. Runs in microseconds on Jetson Orin / any ONNX Runtime target — ideal as a safety reflex node. Validated example: a stable stance returns P(fall)=0.00; a 50+ deg/s tumbling window returns P(fall)=1.00.