{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:CEJH7PNXYBREYFE52WQVND3YRK","short_pith_number":"pith:CEJH7PNX","schema_version":"1.0","canonical_sha256":"11127fbdb7c0624c149dd5a1568f788abd93f8969d03e8d5b1431a37f8a0ddef","source":{"kind":"arxiv","id":"2410.10624","version":4},"attestation_state":"computed","paper":{"title":"SensorLLM: Aligning Large Language Models with Motion Sensors for Human Activity Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Flora D. Salim, Hao Xue, Linyao Chen, Shohreh Deldari, Zechen Li","submitted_at":"2024-10-14T15:30:41Z","abstract_excerpt":"We introduce SensorLLM, a two-stage framework that enables Large Language Models (LLMs) to perform human activity recognition (HAR) from sensor time-series data. Despite their strong reasoning and generalization capabilities, LLMs remain underutilized for motion sensor data due to the lack of semantic context in time-series, computational constraints, and challenges in processing numerical inputs. SensorLLM addresses these limitations through a Sensor-Language Alignment stage, where the model aligns sensor inputs with trend descriptions. Special tokens are introduced to mark channel boundaries"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2410.10624","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-10-14T15:30:41Z","cross_cats_sorted":[],"title_canon_sha256":"eab3955362a63c77275a363cae3d10484f3b91e3a03df73ba01b5a46c50bdd28","abstract_canon_sha256":"b55acfc9acb7918e45cf595d7fa4b23316b2af499b36704db7d6824baea3a439"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:58:21.160095Z","signature_b64":"1i+E6sYVTnMi1bdP+Lq9Ic+CmrsMSvgNZltNIsx07STX7H7O7fWzaY2VvcGmpQu3hMCoeuK2n6FqH8XVtfq9Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"11127fbdb7c0624c149dd5a1568f788abd93f8969d03e8d5b1431a37f8a0ddef","last_reissued_at":"2026-07-05T11:58:21.159635Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:58:21.159635Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SensorLLM: Aligning Large Language Models with Motion Sensors for Human Activity Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Flora D. Salim, Hao Xue, Linyao Chen, Shohreh Deldari, Zechen Li","submitted_at":"2024-10-14T15:30:41Z","abstract_excerpt":"We introduce SensorLLM, a two-stage framework that enables Large Language Models (LLMs) to perform human activity recognition (HAR) from sensor time-series data. Despite their strong reasoning and generalization capabilities, LLMs remain underutilized for motion sensor data due to the lack of semantic context in time-series, computational constraints, and challenges in processing numerical inputs. SensorLLM addresses these limitations through a Sensor-Language Alignment stage, where the model aligns sensor inputs with trend descriptions. Special tokens are introduced to mark channel boundaries"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.10624","kind":"arxiv","version":4},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2410.10624/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2410.10624","created_at":"2026-07-05T11:58:21.159683+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.10624v4","created_at":"2026-07-05T11:58:21.159683+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.10624","created_at":"2026-07-05T11:58:21.159683+00:00"},{"alias_kind":"pith_short_12","alias_value":"CEJH7PNXYBRE","created_at":"2026-07-05T11:58:21.159683+00:00"},{"alias_kind":"pith_short_16","alias_value":"CEJH7PNXYBREYFE5","created_at":"2026-07-05T11:58:21.159683+00:00"},{"alias_kind":"pith_short_8","alias_value":"CEJH7PNX","created_at":"2026-07-05T11:58:21.159683+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.22496","citing_title":"Enabling Cloud-Level Accuracy in Edge AI through IoT Data Preprocessing","ref_index":19,"is_internal_anchor":false},{"citing_arxiv_id":"2606.10789","citing_title":"Closing the Modality Gap in Zero-Shot HAR: Contrastive Training and Separability-Optimized Prototypes on IMU Data","ref_index":7,"is_internal_anchor":false},{"citing_arxiv_id":"2503.07259","citing_title":"COMODO: Cross-Modal Video-to-IMU Distillation for Efficient Egocentric Human Activity Recognition","ref_index":28,"is_internal_anchor":false},{"citing_arxiv_id":"2605.21295","citing_title":"TimeSRL: Generalizable Time-Series Behavioral Modeling via Semantic RL-Tuned LLMs -- A Case Study in Mental Health","ref_index":31,"is_internal_anchor":false},{"citing_arxiv_id":"2506.11512","citing_title":"From Time Series Analysis to Question Answering: A Survey in the LLM Era","ref_index":51,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CEJH7PNXYBREYFE52WQVND3YRK","json":"https://pith.science/pith/CEJH7PNXYBREYFE52WQVND3YRK.json","graph_json":"https://pith.science/api/pith-number/CEJH7PNXYBREYFE52WQVND3YRK/graph.json","events_json":"https://pith.science/api/pith-number/CEJH7PNXYBREYFE52WQVND3YRK/events.json","paper":"https://pith.science/paper/CEJH7PNX"},"agent_actions":{"view_html":"https://pith.science/pith/CEJH7PNXYBREYFE52WQVND3YRK","download_json":"https://pith.science/pith/CEJH7PNXYBREYFE52WQVND3YRK.json","view_paper":"https://pith.science/paper/CEJH7PNX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.10624&json=true","fetch_graph":"https://pith.science/api/pith-number/CEJH7PNXYBREYFE52WQVND3YRK/graph.json","fetch_events":"https://pith.science/api/pith-number/CEJH7PNXYBREYFE52WQVND3YRK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CEJH7PNXYBREYFE52WQVND3YRK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CEJH7PNXYBREYFE52WQVND3YRK/action/storage_attestation","attest_author":"https://pith.science/pith/CEJH7PNXYBREYFE52WQVND3YRK/action/author_attestation","sign_citation":"https://pith.science/pith/CEJH7PNXYBREYFE52WQVND3YRK/action/citation_signature","submit_replication":"https://pith.science/pith/CEJH7PNXYBREYFE52WQVND3YRK/action/replication_record"}},"created_at":"2026-07-05T11:58:21.159683+00:00","updated_at":"2026-07-05T11:58:21.159683+00:00"}