README.md

metadata

tags:
  - time-series
  - kinematics
  - biomechanics
  - contrastive-learning
  - gait-analysis
  - transformer
license: apache-2.0
library_name: pytorch
pipeline_tag: time-series-classification

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).