Explainable AI Framework for 3D Vision-Based Classification of Adhesive Capsulitis Severity

Adhesive capsulitis (AC) is a musculoskeletal disorder that causes significant shoulder pain and stiffness. However, timely assessment of severity is often hindered by labor-intensive clinical procedures and relies largely on subjective judgment. In this study, we propose an explainable AI system for automated classification of AC severity using a single Azure Kinect 3D depth camera. Marker less 3D skeletal data were collected from 221 participants while they performed key shoulder movements, including abduction, flexion, and internal/external rotation. The proposed framework integrates a Temporal Convolutional Network (TCN) to model local temporal structure and cycle-to-cycle variation, followed by Transformer encoders to capture global contextual relationships across movement cycles. In addition, attention-based Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), and Gated Recurrent Unit (GRU) networks were implemented as deep sequential baselines. For abduction, flexion, and external rotation, all architectures achieved test accuracies of 0.88-0.92 and macro-F1 scores ≥ 0.84. Notably, for internal rotation, the best-performing model (Attention-GRU) achieved an accuracy of 0.81 and macro-F1 of 0.77. Attention weights and SHAP analysis provided complementary interpretability, highlighting discriminative kinematic patterns between healthy and AC groups. The proposed system demonstrates the feasibility of automated AC severity stratification and supports clinician-facing reporting for rehabilitation-oriented assessment.