{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/papagei-open-foundation-models-for-optical","title":"PaPaGei: Open Foundation Models for Optical Physiological Signals","arxiv_id":"2410.20542","date":"2024-10-27","proceeding":null,"authors":["Arvind Pillai","Dimitris Spathis","Fahim Kawsar","Mohammad Malekzadeh"],"abstract":"Photoplethysmography (PPG) is the leading non-invasive technique for monitoring biosignals and cardiovascular health, with widespread adoption in both clinical settings and consumer wearable devices. While machine learning models trained on PPG signals have shown promise, they tend to be task-specific and struggle with generalization. Current research is limited by the use of single-device datasets, insufficient exploration of out-of-domain generalization, and a lack of publicly available models, which hampers reproducibility. To address these limitations, we present PaPaGei, the first open foundation model for PPG signals. The model is pre-trained on over 57,000 hours of data, comprising 20 million unlabeled PPG segments from publicly available datasets. We introduce a novel representation learning approach that leverages domain knowledge of PPG signal morphology across individuals, enabling the capture of richer representations compared to traditional contrastive learning methods. We evaluate PaPaGei against state-of-the-art time-series foundation models and self-supervised learning benchmarks across 20 tasks from 10 diverse datasets, spanning cardiovascular health, sleep disorders, pregnancy monitoring, and wellbeing assessment. Our model demonstrates superior performance, improving classification and regression metrics by 6.3% and 2.9% respectively in at least 14 tasks. Notably, PaPaGei achieves these results while being more data- and parameter-efficient, outperforming models that are 70x larger. Beyond accuracy, we examine model robustness across different skin tones, establishing a benchmark for bias evaluation in future models. PaPaGei can serve as both a feature extractor and an encoder for multimodal models, opening up new opportunities for multimodal health monitoring.","url_abs":"https://arxiv.org/abs/2410.20542v2","url_pdf":"https://arxiv.org/pdf/2410.20542v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"papagei-open-foundation-models-for-optical","repo_url":"https://github.com/nokia-bell-labs/papagei-foundation-model","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"contrastive-learning","task_name":"Contrastive Learning"},{"task_slug":"domain-generalization","task_name":"Domain Generalization"},{"task_slug":"photoplethysmography-ppg","task_name":"Photoplethysmography (PPG)"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"self-supervised-learning","task_name":"Self-Supervised Learning"},{"task_slug":"time-series-1","task_name":"Time Series"}],"methods":[{"method_slug":"contrastive-learning","method_name":"Contrastive Learning"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2410.20542","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.20542"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/nokia-bell-labs/papagei-foundation-model","reach":null}],"summary":{"ran":1,"ran_fixture":1,"ran_draft_wrong":2},"by_repo_kind":{"official":{"samples":4,"ran":4,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"6fc0623095469f99","entry":"ResNet1DMoE","repo":"nokia-bell-labs/papagei-foundation-model","repo_kind":"official","path":"models/resnet.py","file_url":"https://github.com/nokia-bell-labs/papagei-foundation-model/blob/HEAD/models/resnet.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"6fc0623095469f99"}},{"code_sha256_prefix":"c1b7b82f4e8d1c33","entry":"extract_labels","repo":"Nokia-Bell-Labs/papagei-foundation-model","repo_kind":"official","path":"linearprobing/utils.py","file_url":"https://github.com/Nokia-Bell-Labs/papagei-foundation-model/blob/HEAD/linearprobing/utils.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"c1b7b82f4e8d1c33"}},{"code_sha256_prefix":"576f145ac4432c5f","entry":"get_data_for_ml","repo":"Nokia-Bell-Labs/papagei-foundation-model","repo_kind":"official","path":"linearprobing/utils.py","file_url":"https://github.com/Nokia-Bell-Labs/papagei-foundation-model/blob/HEAD/linearprobing/utils.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"576f145ac4432c5f"}},{"code_sha256_prefix":"44cee95a33ad89d9","entry":"get_data_for_ml_from_df","repo":"Nokia-Bell-Labs/papagei-foundation-model","repo_kind":"official","path":"linearprobing/utils.py","file_url":"https://github.com/Nokia-Bell-Labs/papagei-foundation-model/blob/HEAD/linearprobing/utils.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"44cee95a33ad89d9"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}