Background: Subtle shifts in gait, such as slower steps or reduced joint angles,often signal fatigue but are difficult to detect without costly sensors or lab-grademotion-capture systems.Objectives: This thesis develops a video-based fatigue detection system using consumer smartphones, aiming to make motion analysis accessible. Specifically, we aimto:• Capture synchronized multi-angle videos using three smartphones;• Extract 2D keypoints with MMPose HRNet and reconstruct 3D poses usingSemGCN;• Compute kinematic features (joint velocities, accelerations, angles);• Train machine learning models (Conv1D-BiLSTM and RandomForest-GradientBoosting)to classify normal vs. fatigued gait and predict fatigue scores.Method: Volunteers walked normally and after fatiguing exercise while three smartphones recorded from different angles. We synchronized the video streams, extracted 2D keypoints using MMPose HRNet, and reconstructed 3D joint tracks withSemGCN. Kinematic features were calculated and segmented into 20-frame windows,feeding into a Conv1D-BiLSTM network and a RandomForest-GradientBoosting ensemble. Models were trained with 4-fold stratified cross-validation, early stopping,and learning-rate scheduling. Performance was evaluated via classification accuracyand mean absolute error (MAE) for fatigue scores, with robustness tested undervarying lighting and camera positions.Results: The Conv1D-BiLSTM model achieved 83.7% accuracy (±1.8%) and MAE= 2.6 on a 0–5 fatigue scale. The RandomForest-GradientBoosting ensemble achieved93.1% accuracy (±1.5%) and MAE = 1.9. Ablation studies showed kinematic features improved accuracy by up to 7%, and performance remained stable (within 7%)under lighting and angle variations.Conclusion: Using only smartphones and open-source tools, our pipeline reliablydetects fatigued gait and quantifies fatigue levels, enabling accessible health trackingfor occupational safety, sports, and rehabilitation.Keywords: 3D pose estimation, fatigue detection, MMPose HRNet, SemGCN, BiLSTM, Random Forest, smartphone video, kinematic features.