Table 2.

Complete hyperparameter configuration of the multimodal fusion model

Component	Setting
IMU encoder	2-layer bidirectional LSTM, hidden 128 per direction
Scale encoder	3-layer MLP, hidden 64 → 128 → 128, ReLU
Intra-modal attention	4 heads, d_k = 64
Fusion output dimension	256
Classification head	FC 256 → 64 → 4 (cross-entropy)
Regression head	FC 256 → 64 → 1 (mean-squared error)
Dropout	0.5 (after each FC layer)
Optimizer	AdamW (β1 = 0.9, β2 = 0.999, weight decay = 1 × 10⁻⁴)
Initial learning rate / schedule	1 × 10⁻³ / cosine annealing
Batch size	32
Max epochs / early stopping	100 / patience 15 on validation macro-F1
Loss coefficients (α1, α2, α3)	1.0, 0.5, 0.3 (grid search on validation set)
Aggregation λ in Eq. (9)	0.3 for explosive motion; 0.7 for continuous motion
Random seeds	42, 123, 456, 789, 2024, 3407, 7, 1024, 2048, 31,415 (10 runs)
Hardware / framework	NVIDIA RTX 3090, PyTorch 2.1, CUDA 12.1