sEMGCareHCI presents a low-cost, non-invasive surface electromyography (sEMG)-based system for recognizing finger positions and gestures from forearm muscle activity. The proposed “Spatio-Temporal Attention Model (STAM)” combines handcrafted time-domain features, autoencoder-derived latent representations, and continuo...
Rebakah Geddam, Muhammad Awais, Vibha Tiwari et al.· npj Biomedical Innovations· 0 citations
Semi-supervised 3D medical image segmentation under low-label settings often suffers from noisy pseudo labels and anatomically inconsistent predictions, which limit segmentation reliability. Existing methods primarily emphasize training strategies or loss engineering, but they still fail to sufficiently exploit structu...
Si-Bo Qiao, Chao-Ying Yang, Zhi-Yuan Zhao et al.· IEEE journal of biomedical a...· 0 citations
An implementation of federated learning is presented that addresses challenges of collaborative model training among simulated distributed client nodes while ensuring that raw patient data remains confidential by enabling collaborative model training among simulated distributed client nodes while ensuring that raw pati...
B. Shrimali, Rebekah Geddam, H. Ghayvat et al.· Healthcare technology letter...· 0 citations
A large language model (LLM) enhanced differentiable trajectory planning framework for IoT-enabled autonomous driving is proposed and a surrounding agent centric data augmentation strategy is introduced to reorganize sur rounding agent trajectories as additional planning supervision, thereby improving the training dist...
Shihao Zhang, Jing Yang, Ziyu Song et al.· IEEE Internet of Things Jour...· 1 citation
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