Paper Citation Record · LEDGER

SensorLM: Learning the Language of Wearable Sensors

As of 17 August 2026, this Paper Citation Record lists 69 of 69 outbound references and 18 inbound Pith citation observations for arXiv:2506.09108.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.09108 v1

Coverage vector

measured 69 of 69 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:04:06.545949Z

measured 87 of 87 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 18 of 18 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T13:52:19.564984Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

69 of 69 outbound references displayed

verified exact0
verified fuzzy19
unresolved47
parse uncertain2
malformed identifier1
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External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation f8e5e55d-5999-4f20-8be1-94b4f091d969 · outbound

This paper cites Large-scale training of foundation models for wearable biosignals.

SensorLM: Learning the Language of Wearable Sensors Large-scale training of foundation models for wearable biosignals

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:04:07.482437Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 841cf905-06c4-4265-a54e-a5280fca9136 · outbound

This paper cites Masked siamese networks for label-efficient learning.

SensorLM: Learning the Language of Wearable Sensors Masked siamese networks for label-efficient learning

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:04:07.470308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 212a12f2-ac58-492b-ba44-f50d301e3f7d · outbound

This paper cites Emerging properties in self-supervised vision transformers.

SensorLM: Learning the Language of Wearable Sensors Emerging properties in self-supervised vision transformers

Reference 3

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Observation 70bf205c-eb92-485b-bdd2-62386b2d55b7 · outbound

This paper cites Bootstrap confidence intervals: when, which, what? a practical guide for medical statisticians.Statistics in medicine, 19(9):1141–1164, 2000.

SensorLM: Learning the Language of Wearable Sensors Bootstrap confidence intervals: when, which, what? a practical guide for medical statisticians.Statistics in medicine, 19(9):1141–1164, 2000

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:04:07.450088Z

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Observation b6870797-181a-4bd6-a87f-e57c53440874 · outbound

This paper cites A simple framework for contrastive learning of visual representations.

SensorLM: Learning the Language of Wearable Sensors A simple framework for contrastive learning of visual representations

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T05:04:06.272709Z

Source-reported events for the cited work

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Observation e8c1bcd4-a48f-4f5e-aeb4-e08dc81d64fc · outbound

This paper cites an unresolved cited work.

SensorLM: Learning the Language of Wearable Sensors Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:04:07.429381Z

Source-reported events for the cited work

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Observation c00a6953-028a-4d2f-b11e-4c50044c755a · outbound

This paper cites Towards a Personal Health Large Language Model.

SensorLM: Learning the Language of Wearable Sensors Towards a Personal Health Large Language Model

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T05:04:06.281116Z

Source-reported events for the cited work

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Observation 833e4c6b-99e4-4aaa-b43f-53e8dea779b3 · outbound

This paper cites Clap learning audio concepts from natural language supervision.

SensorLM: Learning the Language of Wearable Sensors Clap learning audio concepts from natural language supervision

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T05:04:06.285349Z

Source-reported events for the cited work

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Observation f36479a9-99bf-489c-a1d4-145d30dff8cd · outbound

This paper cites A visual– language foundation model for pathology image analysis using medical twitter.Nature medicine, 29(9):2307–2316, 2023.

SensorLM: Learning the Language of Wearable Sensors A visual– language foundation model for pathology image analysis using medical twitter.Nature medicine, 29(9):2307–2316, 2023

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:04:07.408835Z

Source-reported events for the cited work

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Observation d75d8244-0e55-4d06-9797-15064d4bbdb9 · outbound

This paper cites Llasa: A multimodal llm for human activity analysis through wearable and smartphone sensors.arXiv preprint arXiv:2406.14498, 2024.

SensorLM: Learning the Language of Wearable Sensors Llasa: A multimodal llm for human activity analysis through wearable and smartphone sensors.arXiv preprint arXiv:2406.14498, 2024

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T05:04:06.292971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4f23b30-b58e-4397-a119-a9aacc04f6e0 · outbound

This paper cites Neurolm: A universal multi-task foundation model for bridging the gap between language and eeg signals.

SensorLM: Learning the Language of Wearable Sensors Neurolm: A universal multi-task foundation model for bridging the gap between language and eeg signals

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T05:04:06.297198Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1cca1eec-897b-4f4f-9ce9-bb21355ee996 · outbound

This paper cites Scaling Laws for Neural Language Models.

SensorLM: Learning the Language of Wearable Sensors Scaling Laws for Neural Language Models

Reference 12

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Source-reported events for the cited work

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Observation 66227ad1-bd47-4750-8ae9-44025463ab82 · outbound

This paper cites Health-LLM: Large Language Models for Health Prediction via Wearable Sensor Data.

SensorLM: Learning the Language of Wearable Sensors Health-LLM: Large Language Models for Health Prediction via Wearable Sensor Data

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T05:04:06.305716Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1890c741-c2a9-40da-817c-5eb792a19941 · outbound

This paper cites Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models.

SensorLM: Learning the Language of Wearable Sensors Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T05:04:06.310092Z

Source-reported events for the cited work

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Observation fddef3b8-9f05-46f4-aad8-7bd2c13330ad · outbound

This paper cites an unresolved cited work.

SensorLM: Learning the Language of Wearable Sensors Unresolved cited work

Reference 15

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:04:07.379688Z

Source-reported events for the cited work

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Observation 1e12a4c2-2cb4-40ff-8f9a-7651b4f2d1a5 · outbound

This paper cites Large Language Models are Few-Shot Health Learners.

SensorLM: Learning the Language of Wearable Sensors Large Language Models are Few-Shot Health Learners

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T05:04:06.318531Z

Source-reported events for the cited work

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Observation e9a37e22-0cba-491b-8d55-dd79458ea9ca · outbound

This paper cites Guidelines for Augmentation Selection in Contrastive Learning for Time Series Classification.

SensorLM: Learning the Language of Wearable Sensors Guidelines for Augmentation Selection in Contrastive Learning for Time Series Classification

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T05:04:06.324107Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 257360a2-7a7a-4f23-a650-dc136df5b983 · outbound

This paper cites Decoupled weight decay regularization.

SensorLM: Learning the Language of Wearable Sensors Decoupled weight decay regularization

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T05:04:06.329258Z

Source-reported events for the cited work

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Observation ef43d9db-6147-4eb8-8eb4-c2642878ec18 · outbound

This paper cites A visual-language foundation model for computational pathology.Nature Medicine, 30(3):863–874, 2024.

SensorLM: Learning the Language of Wearable Sensors A visual-language foundation model for computational pathology.Nature Medicine, 30(3):863–874, 2024

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:04:07.358951Z

Source-reported events for the cited work

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Observation 62483fdb-c7c3-4703-95c6-f8932b51d0ad · outbound

This paper cites Transforming Wearable Data into Personal Health Insights using Large Language Model Agents.

SensorLM: Learning the Language of Wearable Sensors Transforming Wearable Data into Personal Health Insights using Large Language Model Agents

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T05:04:06.338535Z

Source-reported events for the cited work

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Observation 1c9a3902-d555-41d6-b79d-93072e62237e · outbound

This paper cites Merrill, Mingtian Tan, Vinayak Gupta, Thomas Hartvigsen, and Tim Althoff.

SensorLM: Learning the Language of Wearable Sensors Merrill, Mingtian Tan, Vinayak Gupta, Thomas Hartvigsen, and Tim Althoff

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:04:07.346915Z

Source-reported events for the cited work

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Observation 316694e0-ce39-438b-93e6-22f8c8a282d1 · outbound

This paper cites Imu2clip: language-grounded motion sensor translation with multimodal con- trastive learning.

SensorLM: Learning the Language of Wearable Sensors Imu2clip: language-grounded motion sensor translation with multimodal con- trastive learning

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:04:07.335018Z

Source-reported events for the cited work

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Observation 1009ff39-6c54-42c7-a1f1-4a21911d4217 · outbound

This paper cites Tailor, Jacob Sunshine, Yun Liu, Tim Althoff, Shrikanth Narayanan, Pushmeet Kohli, Jiening Zhan, Mark Malhotra, Shwetak Patel, Samy Abdel-Ghaffar, and Daniel McDuff.

SensorLM: Learning the Language of Wearable Sensors Tailor, Jacob Sunshine, Yun Liu, Tim Althoff, Shrikanth Narayanan, Pushmeet Kohli, Jiening Zhan, Mark Malhotra, Shwetak Patel, Samy Abdel-Ghaffar, and Daniel McDuff

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:04:07.322460Z

Source-reported events for the cited work

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Observation dc687f0e-f5f5-46aa-b276-5308b7d26a1b · outbound

This paper cites Learning transferable visual models from natural language supervision.

SensorLM: Learning the Language of Wearable Sensors Learning transferable visual models from natural language supervision

Reference 24

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Source-reported events for the cited work

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Observation 26b661ad-89d4-4ada-8f94-fc04a91279e2 · outbound

This paper cites Fitbit-based interventions for healthy lifestyle outcomes: systematic review and meta-analysis.Journal of medical Internet research, 22(10):e23954, 2020.

SensorLM: Learning the Language of Wearable Sensors Fitbit-based interventions for healthy lifestyle outcomes: systematic review and meta-analysis.Journal of medical Internet research, 22(10):e23954, 2020

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:04:07.301815Z

Source-reported events for the cited work

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Observation 0fbbd9b9-6b72-4947-8381-941505f82cd9 · outbound

This paper cites Data augmentation for learning predictive models on eeg: a systematic comparison.Journal of Neural Engineering, 19(6):066020, 2022.

SensorLM: Learning the Language of Wearable Sensors Data augmentation for learning predictive models on eeg: a systematic comparison.Journal of Neural Engineering, 19(6):066020, 2022

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:04:07.289672Z

Source-reported events for the cited work

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Observation 210b99c5-8e4c-40f9-9e74-11bea6c54fb8 · outbound

This paper cites Exploring Contrastive Learning in Human Activity Recognition for Healthcare.

SensorLM: Learning the Language of Wearable Sensors Exploring Contrastive Learning in Human Activity Recognition for Healthcare

Reference 27

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Observation 5975283e-a5de-430b-a0c8-93ad42b058e9 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

SensorLM: Learning the Language of Wearable Sensors Gemini: A Family of Highly Capable Multimodal Models

Reference 28

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Source-reported events for the cited work

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Observation 58596b3b-c450-4eaa-bfa0-ea648fefcfba · outbound

This paper cites Gemma 3 Technical Report.

SensorLM: Learning the Language of Wearable Sensors Gemma 3 Technical Report

Reference 29

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Observation 983645f6-18d3-42d1-a744-82bccbffaf9b · outbound

This paper cites SleepFM: Multi-modal representation learning for sleep across brain activity, ECG and respiratory signals.

SensorLM: Learning the Language of Wearable Sensors SleepFM: Multi-modal representation learning for sleep across brain activity, ECG and respiratory signals

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:04:07.277412Z

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Observation 9958ce9a-cad5-4d49-a379-5206e827f74d · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

SensorLM: Learning the Language of Wearable Sensors Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 31

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Observation 4d3844ff-2c30-4eac-be70-516b14819209 · outbound

This paper cites Image captioners are scalable vision learners too.Advances in Neural Information Processing Systems, 36:46830–46855, 2023.

SensorLM: Learning the Language of Wearable Sensors Image captioners are scalable vision learners too.Advances in Neural Information Processing Systems, 36:46830–46855, 2023

Reference 32

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Observation 37d4ec21-92b9-4400-9990-4009e69ea965 · outbound

This paper cites Visualizing data using t-sne.Journal of machine learning research, 9(11), 2008.

SensorLM: Learning the Language of Wearable Sensors Visualizing data using t-sne.Journal of machine learning research, 9(11), 2008

Reference 33

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Observation 32f16b24-5e42-4983-ad77-b0bbfb6583fc · outbound

This paper cites SimVLM: Simple Visual Language Model Pretraining with Weak Supervision.

SensorLM: Learning the Language of Wearable Sensors SimVLM: Simple Visual Language Model Pretraining with Weak Supervision

Reference 34

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Observation 0f5fd389-71e8-4210-86ae-56438260f9d7 · outbound

This paper cites Deepsqa: Understanding sensor data via question answering.

SensorLM: Learning the Language of Wearable Sensors Deepsqa: Understanding sensor data via question answering

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:04:07.248281Z

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Observation 2fe0f453-ac9c-4e43-ac99-a0f6776a029e · outbound

This paper cites Simper: Simple self-supervised learning of periodic targets.

SensorLM: Learning the Language of Wearable Sensors Simper: Simple self-supervised learning of periodic targets

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:04:07.235854Z

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Observation 6f4b7ac8-44a5-4c43-a168-7a8101efbcac · outbound

This paper cites Artificial intelligence-enabled detection and assessment of parkinson’s disease using nocturnal breathing signals.Nature Medicine, 28(10):2207–2215, 2022.

SensorLM: Learning the Language of Wearable Sensors Artificial intelligence-enabled detection and assessment of parkinson’s disease using nocturnal breathing signals.Nature Medicine, 28(10):2207–2215, 2022

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:04:07.222697Z

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Observation 65d2b6bc-0f91-43a1-a9b2-5acffd1919a4 · outbound

This paper cites CoCa: Contrastive Captioners are Image-Text Foundation Models.

SensorLM: Learning the Language of Wearable Sensors CoCa: Contrastive Captioners are Image-Text Foundation Models

Reference 38

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Observation 540f3066-51d0-4383-985f-fac80ddfa244 · outbound

This paper cites SensorChat: Answering Qualitative and Quantitative Questions during Long-Term Multimodal Sensor Interactions.

SensorLM: Learning the Language of Wearable Sensors SensorChat: Answering Qualitative and Quantitative Questions during Long-Term Multimodal Sensor Interactions

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T05:04:06.416028Z

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Observation 2f528a76-0fae-48c7-9c0e-9d39ae8eb196 · outbound

This paper cites Self-supervised learning for human activity recognition using 700,000 person-days of wearable data.NPJ digital medicine, 7(1):91, 2024.

SensorLM: Learning the Language of Wearable Sensors Self-supervised learning for human activity recognition using 700,000 person-days of wearable data.NPJ digital medicine, 7(1):91, 2024

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T05:04:06.420069Z

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Observation 64b80bf5-45a5-45ad-ab54-b25b0abadfba · outbound

This paper cites Self-supervised contrastive pre-training for time series via time-frequency consistency.Advances in neural information processing systems, 35:3988–4003, 2022.

SensorLM: Learning the Language of Wearable Sensors Self-supervised contrastive pre-training for time series via time-frequency consistency.Advances in neural information processing systems, 35:3988–4003, 2022

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:04:07.201104Z

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Observation e17bfd02-cfd6-4e60-b257-9cf5ddeb4e45 · outbound

This paper cites Unimts: Unified pre-training for motion time series.Advances in Neural Information Processing Systems, 37:107469–107493, 2024.

SensorLM: Learning the Language of Wearable Sensors Unimts: Unified pre-training for motion time series.Advances in Neural Information Processing Systems, 37:107469–107493, 2024

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:04:07.189177Z

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This paper cites ECG-Chat: A Large ECG-Language Model for Cardiac Disease Diagnosis.

SensorLM: Learning the Language of Wearable Sensors ECG-Chat: A Large ECG-Language Model for Cardiac Disease Diagnosis

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T05:04:06.432569Z

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SensorLM: Learning the Language of Wearable Sensors Heart rate

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:04:07.177450Z

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SensorLM: Learning the Language of Wearable Sensors Unresolved cited work

Reference 45

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Observation c9faa8d7-a89a-488d-b622-bd613a967ad4 · outbound

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SensorLM: Learning the Language of Wearable Sensors Unresolved cited work

Reference 46

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SensorLM: Learning the Language of Wearable Sensors Unresolved cited work

Reference 47

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Observation 2570d5c2-a934-4a56-9155-d6f7ffdafa71 · outbound

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SensorLM: Learning the Language of Wearable Sensors Unresolved cited work

Reference 48

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Observation 525f199e-1e1a-4291-9819-353145113977 · outbound

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SensorLM: Learning the Language of Wearable Sensors Unresolved cited work

Reference 49

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Observation 5cb75f77-6d89-4d2f-9fc7-2db97de16ebe · outbound

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SensorLM: Learning the Language of Wearable Sensors Unresolved cited work

Reference 51

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Observation 85c61af9-0577-4872-8d21-d41152512446 · outbound

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SensorLM: Learning the Language of Wearable Sensors Unresolved cited work

Reference 52

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Observation 37ad6251-b7cd-4482-8912-d006f054043b · outbound

This paper cites an unresolved cited work.

SensorLM: Learning the Language of Wearable Sensors Unresolved cited work

Reference 53

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SensorLM: Learning the Language of Wearable Sensors Unresolved cited work

Reference 54

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Reference 55

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Reference 56

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Reference 57

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Reference 58

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Reference 59

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Reference 60

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Reference 61

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Reference 62

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Reference 63

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SensorLM: Learning the Language of Wearable Sensors Unresolved cited work

Reference 65

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Reference 66

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SensorLM: Learning the Language of Wearable Sensors Unresolved cited work

Reference 67

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Reference 68

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Reference 69

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SensorLM: Learning the Language of Wearable Sensors Anxiety” and “Hypertension

Reference 730

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Pith citing papers

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SleepLM: Natural-Language Intelligence for Human Sleep SensorLM: Learning the Language of Wearable Sensors

Reference 46

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Reference 45

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Reference 50

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Reference 89

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Foundation Models Defining A New Era In Sensor-based Human Activity Recognition: A Survey And Outlook cites this paper.

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Reference 174

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Reference 41

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Reference 73

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Sonata: A Hybrid World Model for Inertial Kinematics under Clinical Data Scarcity cites this paper.

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Reference 29

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Seeing Without Eyes: 4D Human-Scene Understanding from Wearable IMUs SensorLM: Learning the Language of Wearable Sensors

Reference 117

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Reference 53

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Reference 79

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Reference 43

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Reference 7

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Reference 19

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Reference 13

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Signal or Noise? Understanding Generative Models for Real-World Sensor Time Series cites this paper.

Signal or Noise? Understanding Generative Models for Real-World Sensor Time Series SensorLM: Learning the Language of Wearable Sensors

Reference 46

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Reference 25

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