{"paper":{"title":"MU-SHOT-Fi: Self-Supervised Multi-User Wi-Fi Sensing with Source-free Unsupervised Domain Adaptation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"MU-SHOT-Fi adapts a pre-trained Wi-Fi model to new rooms and frequencies using only unlabeled target CSI and self-supervision.","cross_cats":["cs.AI","cs.LG"],"primary_cat":"eess.SP","authors_text":"Ahmed Y. Radwan, Hina Tabassum","submitted_at":"2026-05-02T10:29:40Z","abstract_excerpt":"Deep learning has been widely adopted for WiFi CSI-based human activity recognition (HAR) due to its ability to learn spatio-temporal features in a privacy-preserving and cost-effective manner. However, DL-based models generalize poorly across environments, a challenge amplified in multi-user settings where overlapping activities cause CSI entanglement and domain shifts. Practical deployments often limit access to labeled source data due to privacy constraints, motivating source-free adaptation using only unlabeled target-domain CSI and a pre-trained source model. In this paper, we propose MU-"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"MU-SHOT-Fi effectively recovers multi-user exact-activity classification performance under large domain shifts while maintaining accurate occupancy estimation and preventing collapse toward dominant classes.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the pre-trained source model provides a sufficiently rich feature backbone that frozen-classifier adaptation plus the proposed self-supervision signals can recover performance without any target labels or source data access.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"MU-SHOT-Fi recovers multi-user activity classification accuracy under domain shifts in WiFi CSI sensing using source-free adaptation with Hungarian matching, occupancy-weighted entropy regularization, and rotation prediction self-supervision.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"MU-SHOT-Fi adapts a pre-trained Wi-Fi model to new rooms and frequencies using only unlabeled target CSI and self-supervision.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"a0a4b92ef26cbd98b4fac6079dee81764581237f31c3babd3b60467ad96e852b"},"source":{"id":"2605.01369","kind":"arxiv","version":2},"verdict":{"id":"85633487-9b57-4685-bc60-cdcde02dd6ac","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-09T18:30:53.813995Z","strongest_claim":"MU-SHOT-Fi effectively recovers multi-user exact-activity classification performance under large domain shifts while maintaining accurate occupancy estimation and preventing collapse toward dominant classes.","one_line_summary":"MU-SHOT-Fi recovers multi-user activity classification accuracy under domain shifts in WiFi CSI sensing using source-free adaptation with Hungarian matching, occupancy-weighted entropy regularization, and rotation prediction self-supervision.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the pre-trained source model provides a sufficiently rich feature backbone that frozen-classifier adaptation plus the proposed self-supervision signals can recover performance without any target labels or source data access.","pith_extraction_headline":"MU-SHOT-Fi adapts a pre-trained Wi-Fi model to new rooms and frequencies using only unlabeled target CSI and self-supervision."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.01369/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"ai_meta_artifact","ran_at":"2026-05-20T18:34:35.230989Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T17:19:52.161541Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"d9e6b8e3d7e17331639e87be08fe9c79bc96bedef8d3b16d350b5e262e097bdc"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}