MotionScope AI: Comprehensive Human Activity Recognition Through Integrated Pose Analysis and Temporal Modeling

This paper introduces a practical deep learning framework for recognizing human activities in indoor environments using MediaPipe pose estimation with multi-branch bidirectional LSTM architecture. We extract comprehensive features—including 3D pose landmarks, hand gestures, velocity, acceleration, and joint angles—resulting in 685-dimensional vectors per frame. Our multi-branch design processes each feature type through specialized BiLSTM pathways with attention mechanisms, enabling the model to learn distinct spatial, temporal, and structural patterns. To ensure robust performance in real-world scenarios, we incorporate label smoothing, gradient clipping, and adaptive learning strategies. Evaluated on the IndoorActionDataset with 8 activity classes, our approach achieves 95.6% test accuracy, significantly outperforming OpenPose-based methods (87.2%) and 3D-CNN with Transformer architectures (75.1%). With only 23ms inference latency, the system demonstrates practical viability for real-time deployment on resource-constrained devices. The results confirm that thoughtful feature engineering combined with attention-driven temporal modeling can deliver both accuracy and efficiency for activity recognition tasks.