Smartphone-Based Brunnstrom Stage Classification of Hemiparetic Gait via Skeleton-Attention-LSTM-Inception Network

The accurate assessment of hemiparetic gait after stroke was essential for understanding the extent of motor impairment and for guiding rehabilitation efforts. In this study, a kinematic based deep learning classifier was proposed to eliminate inter-rater variability in lower-limb Brunnstrom Recovery Stage (BRS) assessment and enable quantitative longitudinal tracking of post-stroke motor recovery.

Forty healthy adults and fifty-one hemiparetic stroke patients (BRS III-VI) were recruited, and video recorded kinematic data were collected during their standardized walking tasks. Anatomical keypoint coordinates were extracted from the video recordings via advanced pose estimation algorithms for fine-grained kinematic analysis. A novel hybrid Skeleton-Attention long short-term memory (LSTM)-Inception architecture was then developed for BRS stage classification using three-dimensional keypoint coordinate data. The architecture synergistically integrated LSTM layers for temporal sequence modeling with Inception modules for multi-scale spatial feature extraction. The proposed model's performance was systematically compared against conventional deep learning benchmarks, including convolutional neural networks (CNNs) and coupled LSTM-CNN hybrid models.

Our experimental results revealed that the proposed framework achieved superior classification accuracy compared to alternatives (97.3% vs. 87.7-95.2%).

These findings demonstrated the clinical potential of deep learning-driven motion analytics to eliminate assessment subjectivity in BRS staging.

This approach is expected to facilitate quantitative longitudinal monitoring of neurorehabilitation progress and support the development of data-driven personalized therapeutic strategies, potentially reducing reliance on clinician expertise.