---
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](https://huggingface.co/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).