YAML Metadata Warning:The pipeline tag "time-series-classification" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
PoseT5: Biomechanical Gait Kinematics & Keystroke Predictor (CAL Trained)
PoseT5 is a lightweight (~430K parameter) time-series transformer architecture trained with Supervised Contrastive Alignment Loss (CAL) to predict inverse keystrokes from 14-channel invariant biomechanical joint angles and velocities.
Organization
Trained and hosted under Learningbase.
Performance Metrics
- Validation Macro F1: 0.7378
- Hamming Loss: 0.0317
| Keystroke | Action | F1 Score | Precision | Recall |
|---|---|---|---|---|
W |
Forward Walk/Run | 0.970 | 0.965 | 0.976 |
A |
Yaw Turn Left | 0.881 | 0.839 | 0.929 |
S |
Backward Move | 1.000 | 1.000 | 1.000 |
D |
Yaw Turn Right | 0.837 | 0.947 | 0.750 |
Space |
Jump Dynamics | 0.000 | 0.000 | 0.000 |
Architecture & Loss
- Input Channels: 14 invariant biomechanical joint angles and angular velocities.
- Instance Normalization: Reversible Instance Normalization (RevIN).
- Temporal Patching: Slices 32 frames into 4 patches (P=8) projected into d_model = 128.
- Contrastive Alignment Loss (CAL): InfoNCE loss aligning character gait embeddings across different rigs and morphological dimensions.
- Backbone: 2-layer Multi-Head Self-Attention Transformer Encoder (~430K parameters).
- Latency: Sub-millisecond CPU inference (< 2ms).
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