Time Series Classification for Pain Detection

Mon, 15 Jan 2024 00:00:00 +0000

Problem

Autonomous pain classification in time-series data requires robust temporal modeling that can capture both short-term fluctuations and long-term dependencies from 30 joint measurements and 4 pain survey responses.

Architecture

Network Overview

flowchart TD subgraph Input I1[Time Series Input] I2[30 Joint Measurements] I3[4 Pain Surveys] I4[3 Prosthetic Features] end subgraph Preprocessing P1[Sliding Window] P2[Window Size: 10] P3[Stride: 2] end subgraph FeatureSplit F1[Continuous Features] F2[Categorical Features] end subgraph Encoders E1[1D Conv Layer] E2[BatchNorm + ReLU] E3[Dropout 0.6] E4[Embedding Layers] end subgraph RNN R1[GRU Layer 1] R2[GRU Layer 2] R3[GRU Layer 3] R4[Hidden: 64] R5[Dropout: 0.4] end subgraph Attention A1[Attention Weights] A2[Context Vector] end subgraph Output O1[FC Layer] O2[Softmax] O3[3 Classes] end I1 --> P1 I2 --> P1 I3 --> P1 I4 --> P1 P1 --> P2 P2 --> P3 P3 --> FeatureSplit F1 --> E1 E1 --> E2 E2 --> E3 F2 --> E4 E3 --> R1 E4 --> R1 R1 --> R2 R2 --> R3 R3 --> R4 R4 --> R5 R5 --> A1 A1 --> A2 A2 --> O1 O1 --> O2 O2 --> O3

Jupyter |

Time Series Classification for Pain Detection

Problem

Architecture

Network Overview