{"ID":2863213,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.24178","arxiv_id":"2509.24178","title":"BladderFormer: A Streaming Transformer for Real-Time Urological State Monitoring","abstract":"Bladder pressure monitoring systems are increasingly vital in diagnosing and managing urinary tract dysfunction. Existing solutions rely heavily on hand-crafted features and shallow classifiers, limiting their adaptability to complex signal dynamics. We propose a one-layer streaming transformer model for real-time classification of bladder pressure states, operating on wavelet-transformed representations of raw time-series data. Our model incorporates temporal multi-head self-attention and state caching, enabling efficient online inference with high adaptability. Trained on a dataset of 91 patients with 20,000-80,000 samples each, our method demonstrates improved accuracy, higher energy- and latency-efficiency. Implementation considerations for edge deployment on low-power hardware, such as edge graphical processing units (GPU) and micro-controllers, are also discussed.","short_abstract":"Bladder pressure monitoring systems are increasingly vital in diagnosing and managing urinary tract dysfunction. Existing solutions rely heavily on hand-crafted features and shallow classifiers, limiting their adaptability to complex signal dynamics. We propose a one-layer streaming transformer model for real-time clas...","url_abs":"https://arxiv.org/abs/2509.24178","url_pdf":"https://arxiv.org/pdf/2509.24178v1","authors":"[\"Chengwei Zhou\",\"Steve Majerus\",\"Gourav Datta\"]","published":"2025-09-29T01:52:10Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[\"Transformer\"]","has_code":false}