Foundation models for movement data: Are they ready for prime-time? | Paper Citation Record

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This paper cites On the Opportunities and Risks of Foundation Models.

Foundation models for movement data: Are they ready for prime-time? On the Opportunities and Risks of Foundation Models

Reference 1

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This paper cites Emerging properties in self-supervised vision trans- formers,.

Foundation models for movement data: Are they ready for prime-time? Emerging properties in self-supervised vision trans- formers,

Reference 2

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Observation 618a405c-6e4d-427b-ab5c-03ef0ab9db0e · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding,.

Foundation models for movement data: Are they ready for prime-time? Bert: Pre-training of deep bidirectional transformers for language understanding,

Reference 3

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This paper cites Towards a general-purpose foundation model for computational pathology,.

Foundation models for movement data: Are they ready for prime-time? Towards a general-purpose foundation model for computational pathology,

Reference 4

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This paper cites Towards generalist foundation model for radiology by leveraging web-scale 2d&3d medical data,.

Foundation models for movement data: Are they ready for prime-time? Towards generalist foundation model for radiology by leveraging web-scale 2d&3d medical data,

Reference 5

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This paper cites Neuro-gpt: Towards a foundation model for eeg,.

Foundation models for movement data: Are they ready for prime-time? Neuro-gpt: Towards a foundation model for eeg,

Reference 6

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This paper cites Benchmarking ECG foundational models: A reality check across clinical tasks,.

Foundation models for movement data: Are they ready for prime-time? Benchmarking ECG foundational models: A reality check across clinical tasks,

Reference 7

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This paper cites Towards Customizable Foundation Models for Human Activity Recognition with Wearable Devices,.

Foundation models for movement data: Are they ready for prime-time? Towards Customizable Foundation Models for Human Activity Recognition with Wearable Devices,

Reference 8

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Observation 87e7ff7c-0c09-42f6-a9b9-bfe4c7daa62e · outbound

This paper cites Collecting complex activity datasets in highly rich networked sensor environments,.

Foundation models for movement data: Are they ready for prime-time? Collecting complex activity datasets in highly rich networked sensor environments,

Reference 9

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This paper cites Reiss and D.

Foundation models for movement data: Are they ready for prime-time? Reiss and D

Reference 10

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This paper cites On-body localization of wearable devices: An investigation of position-aware activity recognition,.

Foundation models for movement data: Are they ready for prime-time? On-body localization of wearable devices: An investigation of position-aware activity recognition,

Reference 11

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This paper cites SisFall: A Fall and Movement Dataset,.

Foundation models for movement data: Are they ready for prime-time? SisFall: A Fall and Movement Dataset,

Reference 12

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This paper cites A large-scale open motion dataset (kfall) and benchmark algorithms for detecting pre- impact fall of the elderly using wearable inertial sensors,.

Foundation models for movement data: Are they ready for prime-time? A large-scale open motion dataset (kfall) and benchmark algorithms for detecting pre- impact fall of the elderly using wearable inertial sensors,

Reference 13

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This paper cites A Machine Learning Model for Predicting Sleep and Wakefulness Based on Accelerometry, Skin Temperature and Contextual Information,.

Foundation models for movement data: Are they ready for prime-time? A Machine Learning Model for Predicting Sleep and Wakefulness Based on Accelerometry, Skin Temperature and Contextual Information,

Reference 14

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This paper cites Introducing WESAD, a Multimodal Dataset for Wearable Stress and Affect Detection,.

Foundation models for movement data: Are they ready for prime-time? Introducing WESAD, a Multimodal Dataset for Wearable Stress and Affect Detection,

Reference 15

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This paper cites Wearable technologies in clinical trials for drug development: Trends and emerging opportunities,.

Foundation models for movement data: Are they ready for prime-time? Wearable technologies in clinical trials for drug development: Trends and emerging opportunities,

Reference 16

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Observation 50a6511b-742c-4dfe-a22a-678bc2d884b2 · outbound

This paper cites Sensor-based digital health technologies to capture endpoints in recent clinical trials: A scoping review.

Foundation models for movement data: Are they ready for prime-time? Sensor-based digital health technologies to capture endpoints in recent clinical trials: A scoping review

Reference 17

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Observation 1a59f6f3-0643-472b-b951-3ff0bc4dceca · outbound

This paper cites Digital measures of activity and motivation impact depression and anxiety in the real world,.

Foundation models for movement data: Are they ready for prime-time? Digital measures of activity and motivation impact depression and anxiety in the real world,

Reference 18

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Observation e2a50aa9-3aad-43f0-8ad9-85316d6c1408 · outbound

This paper cites Self-supervised Learning for Accelerometer-based Human Activity Recognition: A Survey,.

Foundation models for movement data: Are they ready for prime-time? Self-supervised Learning for Accelerometer-based Human Activity Recognition: A Survey,

Reference 19

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This paper cites SelfHAR: Improving Human Activity Recognition through Self-training with Unlabeled Data,.

Foundation models for movement data: Are they ready for prime-time? SelfHAR: Improving Human Activity Recognition through Self-training with Unlabeled Data,

Reference 20

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

Foundation models for movement data: Are they ready for prime-time? Foundation Models Defining A New Era In Sensor-based Human Activity Recognition: A Survey And Outlook

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This paper cites Benchmarking foundation models as feature extrac- tors for weakly supervised computational pathology,.

Foundation models for movement data: Are they ready for prime-time? Benchmarking foundation models as feature extrac- tors for weakly supervised computational pathology,

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Observation 59d21feb-7a5a-474e-b733-61a69a1e41ad · outbound

This paper cites Beyond scalars: Concept-based alignment analysis in vision transformers,.

Foundation models for movement data: Are they ready for prime-time? Beyond scalars: Concept-based alignment analysis in vision transformers,

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Observation 5ae7ac2b-3c10-4f54-af0c-7d3de5dd13de · outbound

This paper cites Similarity of neural network representations revisited,.

Foundation models for movement data: Are they ready for prime-time? Similarity of neural network representations revisited,

Reference 24

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Observation cefb446a-5685-4ce7-bbdb-b0a133c162a1 · outbound

This paper cites SensorLM: Learning the Language of Wearable Sensors.

Foundation models for movement data: Are they ready for prime-time? SensorLM: Learning the Language of Wearable Sensors

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Observation 23be4478-e51b-40f3-9ced-39d5764a0189 · outbound

This paper cites LSM-2: Learning from Incomplete Wearable Sensor Data.

Foundation models for movement data: Are they ready for prime-time? LSM-2: Learning from Incomplete Wearable Sensor Data

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Observation 6fbc133b-48a1-41b8-87d3-d9033a657444 · outbound

This paper cites Beyond sensor data: Foundation models of behavioral data from wearables improve health predictions,.

Foundation models for movement data: Are they ready for prime-time? Beyond sensor data: Foundation models of behavioral data from wearables improve health predictions,

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Observation 54c3751a-e700-42e6-af1b-6e3f3f6c2253 · outbound

This paper cites Generating Diverse and Natural 3D Human Motions from Text,.

Foundation models for movement data: Are they ready for prime-time? Generating Diverse and Natural 3D Human Motions from Text,

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This paper cites Large scale population assessment of physical activity using wrist worn accelerometers: The uk biobank study,.

Foundation models for movement data: Are they ready for prime-time? Large scale population assessment of physical activity using wrist worn accelerometers: The uk biobank study,

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Observation d37b46bb-65c9-427f-9d3e-07ea61d2ec1a · outbound

This paper cites Overview and Findings from the Rush Memory and Aging Project,.

Foundation models for movement data: Are they ready for prime-time? Overview and Findings from the Rush Memory and Aging Project,

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Observation 539768ad-dfa3-47d7-9679-63b5238c34db · outbound

This paper cites V-jepa: Latent video prediction for visual representation learning,.

Foundation models for movement data: Are they ready for prime-time? V-jepa: Latent video prediction for visual representation learning,

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Observation 48dba912-b75a-4f97-a55c-a58e20398dae · outbound

This paper cites Ptb-xl, a large publicly available electrocardiography dataset,.

Foundation models for movement data: Are they ready for prime-time? Ptb-xl, a large publicly available electrocardiography dataset,

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Observation 5c0dabc6-09b6-4505-a7b3-27617be9d728 · outbound

This paper cites On the Stratification of Multi-label Data,.

Foundation models for movement data: Are they ready for prime-time? On the Stratification of Multi-label Data,

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Observation 4d85f4ce-13e3-442c-b9b3-b8afa8b1c6db · outbound

This paper cites UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction.

Foundation models for movement data: Are they ready for prime-time? UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

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This paper cites hdbscan: Hierarchical density based clustering.

Foundation models for movement data: Are they ready for prime-time? hdbscan: Hierarchical density based clustering

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Observation 4786626f-e7ab-4e87-ace3-c496c9e1ea88 · outbound

This paper cites Self-supervised representation learning from 12-lead ECG data,.

Foundation models for movement data: Are they ready for prime-time? Self-supervised representation learning from 12-lead ECG data,

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Observation c92790a5-fb94-4fa7-9e3c-e56314f43b23 · outbound

This paper cites UniMTS: Unified Pre-training for Motion Time Series.

Foundation models for movement data: Are they ready for prime-time? UniMTS: Unified Pre-training for Motion Time Series

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Observation 663d04d8-2c55-4974-b324-b689c4e0d783 · outbound

This paper cites Toward Foundation Model for Multivariate Wearable Sensing of Physiological Signals,.

Foundation models for movement data: Are they ready for prime-time? Toward Foundation Model for Multivariate Wearable Sensing of Physiological Signals,

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Observation 7d880c18-7bc5-4b5b-aee1-c338df8a1a2f · outbound

This paper cites Self-supervised learning for human activity recognition using 700,000 person-days of wearable data,.

Foundation models for movement data: Are they ready for prime-time? Self-supervised learning for human activity recognition using 700,000 person-days of wearable data,

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Observation 9debe43c-a54b-4b2a-9d7e-2989dd49f26b · outbound

This paper cites Assessing real-world gait with digital technology? validation, insights and recommendations from the mobilise-d consortium,.

Foundation models for movement data: Are they ready for prime-time? Assessing real-world gait with digital technology? validation, insights and recommendations from the mobilise-d consortium,

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correction dated 2024-05-03. Source: crossref record 10.1186/s12984-024-01361-6->10.1186/s12984-023-01198-5:correction, observed 2026-07-11T03:02:21.685474+00:00. This notice travels one citation hop only.

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This paper cites Self-supervised learning of wrist-worn daily living accelerometer data improves the automated detection of gait in older adults,.

Foundation models for movement data: Are they ready for prime-time? Self-supervised learning of wrist-worn daily living accelerometer data improves the automated detection of gait in older adults,

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This paper cites Tinyhar: A lightweight deep learning model designed for human activity recognition,.

Foundation models for movement data: Are they ready for prime-time? Tinyhar: A lightweight deep learning model designed for human activity recognition,

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Observation 9408ad8a-c579-4bbe-bbcc-f2f4a475eed3 · outbound

This paper cites Deep learning for ecg analysis: Benchmarks and insights from ptb-xl,.

Foundation models for movement data: Are they ready for prime-time? Deep learning for ecg analysis: Benchmarks and insights from ptb-xl,

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This paper cites Machine-learning for photoplethysmography analysis: Benchmarking feature, image, and signal-based approaches,.

Foundation models for movement data: Are they ready for prime-time? Machine-learning for photoplethysmography analysis: Benchmarking feature, image, and signal-based approaches,

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This paper cites Inceptiontime: Finding alexnet for time series classification,.

Foundation models for movement data: Are they ready for prime-time? Inceptiontime: Finding alexnet for time series classification,

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This paper cites Efficiently modeling long sequences with structured state spaces,.

Foundation models for movement data: Are they ready for prime-time? Efficiently modeling long sequences with structured state spaces,

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This paper cites S4sleep: Elucidating the design space of deep-learning-based sleep stage classification models,.

Foundation models for movement data: Are they ready for prime-time? S4sleep: Elucidating the design space of deep-learning-based sleep stage classification models,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:52:20.787143Z

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This paper cites Validation of an Activity Type Recognition Model Classifying Daily Physical Behavior in Older Adults: The HAR70+ Model,.

Foundation models for movement data: Are they ready for prime-time? Validation of an Activity Type Recognition Model Classifying Daily Physical Behavior in Older Adults: The HAR70+ Model,

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This paper cites HARTH: A Human Activity Recognition Dataset for Machine Learn- ing,.

Foundation models for movement data: Are they ready for prime-time? HARTH: A Human Activity Recognition Dataset for Machine Learn- ing,

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Observation e8e351b7-0cf9-4ea9-992e-5ffac15a0189 · outbound

This paper cites USC-HAD: A daily activity dataset for ubiquitous activity recognition using wearable sensors,.

Foundation models for movement data: Are they ready for prime-time? USC-HAD: A daily activity dataset for ubiquitous activity recognition using wearable sensors,

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Observation 745c74a8-5a2b-4043-ad2c-bd10e2ddcbbb · outbound

This paper cites Activity recognition using cell phone accelerometers,.

Foundation models for movement data: Are they ready for prime-time? Activity recognition using cell phone accelerometers,

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Observation e24de706-eb80-49ff-b6cd-1f0a8fe3cc2c · outbound

This paper cites WISDM Smartphone and Smartwatch Activity and Biometrics Dataset.

Foundation models for movement data: Are they ready for prime-time? WISDM Smartphone and Smartwatch Activity and Biometrics Dataset

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