{"id":"https://openalex.org/W4416157222","doi":"https://doi.org/10.48550/arxiv.2511.06231","title":"Synheart Emotion: Privacy-Preserving On-Device Emotion Recognition from Biosignals","display_name":"Synheart Emotion: Privacy-Preserving On-Device Emotion Recognition from Biosignals","publication_year":2025,"publication_date":"2025-11-09","ids":{"openalex":"https://openalex.org/W4416157222","doi":"https://doi.org/10.48550/arxiv.2511.06231"},"language":null,"primary_location":{"id":"pmh:oai:arXiv.org:2511.06231","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.06231","pdf_url":"https://arxiv.org/pdf/2511.06231","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2511.06231","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5099096834","display_name":"Henok Biadglign Ademtew","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ademtew, Henok","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5120595378","display_name":"Israel Goytom","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Goytom, Israel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9343000054359436,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9343000054359436,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10338","display_name":"Advanced Sensor and Energy Harvesting Materials","score":0.0203000009059906,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.009200000204145908,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.659600019454956},{"id":"https://openalex.org/keywords/emotion-recognition","display_name":"Emotion recognition","score":0.652899980545044},{"id":"https://openalex.org/keywords/photoplethysmogram","display_name":"Photoplethysmogram","score":0.5078999996185303},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4754999876022339},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.4178999960422516},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4092000126838684},{"id":"https://openalex.org/keywords/emotion-detection","display_name":"Emotion detection","score":0.3882000148296356},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.3869999945163727}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7231000065803528},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.659600019454956},{"id":"https://openalex.org/C2777438025","wikidata":"https://www.wikidata.org/wiki/Q1339090","display_name":"Emotion recognition","level":2,"score":0.652899980545044},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6050000190734863},{"id":"https://openalex.org/C116390426","wikidata":"https://www.wikidata.org/wiki/Q7187885","display_name":"Photoplethysmogram","level":3,"score":0.5078999996185303},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4754999876022339},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45980000495910645},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.4178999960422516},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4092000126838684},{"id":"https://openalex.org/C2988148770","wikidata":"https://www.wikidata.org/wiki/Q1339090","display_name":"Emotion detection","level":3,"score":0.3882000148296356},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.3869999945163727},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.3806000053882599},{"id":"https://openalex.org/C6438553","wikidata":"https://www.wikidata.org/wiki/Q1185804","display_name":"Affective computing","level":2,"score":0.358599990606308},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.35569998621940613},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3409000039100647},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.32249999046325684},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3215000033378601},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.3140000104904175},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2955000102519989},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.2757999897003174},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2728999853134155},{"id":"https://openalex.org/C206310091","wikidata":"https://www.wikidata.org/wiki/Q750859","display_name":"Emotion classification","level":2,"score":0.26989999413490295},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2533000111579895}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2511.06231","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.06231","pdf_url":"https://arxiv.org/pdf/2511.06231","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2511.06231","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2511.06231","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2511.06231","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.06231","pdf_url":"https://arxiv.org/pdf/2511.06231","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Human-computer":[0],"interaction":[1],"increasingly":[2],"demands":[3],"systems":[4,29],"that":[5,83],"recognize":[6],"not":[7],"only":[8,115],"explicit":[9],"user":[10],"inputs":[11],"but":[12],"also":[13],"implicit":[14],"emotional":[15],"states.":[16],"While":[17],"substantial":[18],"progress":[19],"has":[20],"been":[21],"made":[22],"in":[23],"affective":[24],"computing,":[25],"most":[26],"emotion":[27,56,164],"recognition":[28,57,165],"rely":[30],"on":[31,75,91,101,108],"cloud-based":[32],"inference,":[33],"introducing":[34],"privacy":[35],"vulnerabilities":[36],"and":[37,73,104,138,154],"latency":[38],"constraints":[39],"unsuitable":[40],"for":[41,54,166],"real-time":[42],"applications.":[43],"This":[44],"work":[45],"presents":[46],"a":[47,130,149],"comprehensive":[48],"evaluation":[49],"of":[50,161],"machine":[51],"learning":[52,90],"architectures":[53],"on-device":[55,163],"from":[58],"wrist-based":[59],"photoplethysmography":[60],"(PPG),":[61],"systematically":[62],"comparing":[63],"different":[64],"models":[65],"spanning":[66],"classical":[67,84],"ensemble":[68,85],"methods,":[69],"deep":[70,89],"neural":[71],"networks,":[72],"transformers":[74,113],"the":[76,121,142,159],"WESAD":[77],"stress":[78],"detection":[79],"dataset.":[80],"Results":[81],"demonstrate":[82],"methods":[86],"substantially":[87],"outperform":[88],"small":[92],"physiological":[93],"datasets,":[94],"with":[95],"ExtraTrees":[96,123],"achieving":[97,114,129],"F1":[98,105,116],"=":[99,106,117],"0.826":[100],"combined":[102],"features":[103],"0.623":[107],"wrist-only":[109,122],"features,":[110],"compared":[111],"to":[112],"0.509-0.577.":[118],"We":[119],"deploy":[120],"model":[124],"optimized":[125],"via":[126],"ONNX":[127,146],"conversion,":[128],"4.08":[131],"MB":[132],"footprint,":[133],"0.05":[134],"ms":[135],"inference":[136,156],"latency,":[137],"152x":[139],"speedup":[140],"over":[141],"original":[143],"implementation.":[144],"Furthermore,":[145],"optimization":[147],"yields":[148],"30.5%":[150],"average":[151],"storage":[152],"reduction":[153],"40.1x":[155],"speedup,":[157],"highlighting":[158],"feasibility":[160],"privacy-preserving":[162],"real-world":[167],"wearables.":[168]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-11-12T00:00:00"}
