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| """
论文：From Stress to Affect: Multimodal Deep Learning for Physiological Emotion Recognition
arXiv: 2609.20991
核心方法：BiLSTM / TCN / Transformer 对比评估
"""
import torch
import torch.nn as nn
class PhysiologicalTransformer(nn.Module):
"""Transformer用于生理情感识别"""
def __init__(self,
num_sensors: int = 6,
seq_len: int = 60,
d_model: int = 128,
num_heads: int = 4,
num_layers: int = 4,
num_classes: int = 3):
super().__init__()
self.sensor_embed = nn.Linear(num_sensors, d_model)
self.pos_embed = nn.Parameter(torch.randn(1, seq_len, d_model))
encoder_layer = nn.TransformerEncoderLayer(
d_model=d_model,
nhead=num_heads,
dim_feedforward=d_model*4,
dropout=0.1,
batch_first=True
)
self.encoder = nn.TransformerEncoder(encoder_layer, num_layers)
self.classifier = nn.Sequential(
nn.LayerNorm(d_model),
nn.Linear(d_model, num_classes)
)
def forward(self, x):
"""x: (B, seq_len, num_sensors)"""
x = self.sensor_embed(x) + self.pos_embed[:, :x.size(1)]
x = self.encoder(x)
x = x.mean(dim=1)
return self.classifier(x)
class BilLSTM(nn.Module):
"""双向LSTM (EmoWear最佳模型)"""
def __init__(self, num_sensors=6, hidden=128, num_layers=2, num_classes=3):
super().__init__()
self.lstm = nn.LSTM(num_sensors, hidden, num_layers,
batch_first=True, bidirectional=True, dropout=0.3)
self.classifier = nn.Sequential(
nn.Linear(hidden*2, 64),
nn.ReLU(),
nn.Dropout(0.3),
nn.Linear(64, num_classes)
)
def forward(self, x):
out, _ = self.lstm(x)
last = out[:, -1, :]
return self.classifier(last)
class TCN(nn.Module):
"""时序卷积网络"""
def __init__(self, num_sensors=6, hidden=64, num_classes=3):
super().__init__()
self.tcn = nn.Sequential(
nn.Conv1d(num_sensors, hidden, 3, padding=1),
nn.BatchNorm1d(hidden),
nn.ReLU(),
nn.Conv1d(hidden, hidden*2, 3, dilation=2, padding=2),
nn.BatchNorm1d(hidden*2),
nn.ReLU(),
nn.Conv1d(hidden*2, hidden*4, 3, dilation=4, padding=4),
nn.BatchNorm1d(hidden*4),
nn.ReLU(),
nn.AdaptiveAvgPool1d(1)
)
self.classifier = nn.Linear(hidden*4, num_classes)
def forward(self, x):
x = x.transpose(1, 2)
x = self.tcn(x).squeeze(-1)
return self.classifier(x)
if __name__ == "__main__":
models = {
"Transformer": PhysiologicalTransformer(),
"BiLSTM": BilLSTM(),
"TCN": TCN()
}
x = torch.randn(8, 240, 6)
print("=== 生理情感识别模型对比 ===")
for name, model in models.items():
out = model(x)
params = sum(p.numel() for p in model.parameters())
print(f"{name}: output={out.shape}, params={params:,}")
print(f"\n=== 论文结果 ===")
print("WESAD: Transformer 99.02% ± 0.51% (多模态)")
print("EmoWare Arousal: LSTM 91.80% ± 1.06%")
print("EmoWare Valence: LSTM 89.96% ± 0.36%")
print("采样频率: 4Hz即可")
|
