Paper Citation Record · LEDGER

Sedentary Behavior Classification for Wearable Sensors with a CNN-BiLSTM Model

As of 20 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2608.02946.

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

pith.paper-citation-record.v1
2608.02946 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T14:57:53.117435Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

25 of 25 outbound references displayed

verified exact2
verified fuzzy17
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ba4e4af3-7994-48c8-850e-e8abab2db128 · outbound

This paper cites Sedentary time and its association with risk for disease incidence, mortality, and hospitalization in adults: A systematic review and meta-analysis,.

Sedentary Behavior Classification for Wearable Sensors with a CNN-BiLSTM Model Sedentary time and its association with risk for disease incidence, mortality, and hospitalization in adults: A systematic review and meta-analysis,

Reference 1

Resolution: verified exact; doi, observed 2026-08-15T14:57:53.166023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9a7f38dc-b93d-4b7a-8135-4174c08f58cd · outbound

This paper cites Sit less and move more for cardiovascular health: emerging insights and opportunities,.

Sedentary Behavior Classification for Wearable Sensors with a CNN-BiLSTM Model Sit less and move more for cardiovascular health: emerging insights and opportunities,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:57:53.657101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 85769f4d-934b-4301-bc31-8960bbd0ee05 · outbound

This paper cites Breaks in sedentary time: beneficial associations with metabolic risk,.

Sedentary Behavior Classification for Wearable Sensors with a CNN-BiLSTM Model Breaks in sedentary time: beneficial associations with metabolic risk,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:57:53.643155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation bfa80de0-8a10-4506-923f-06fcf74176bd · outbound

This paper cites an unresolved cited work.

Sedentary Behavior Classification for Wearable Sensors with a CNN-BiLSTM Model Unresolved cited work

Reference 4

Resolution: verified exact; raw_fallback, observed 2026-08-15T14:57:53.391455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f4fc392d-bd6a-45f3-adbe-72cd1f0a4ef6 · outbound

This paper cites Agreement of sedentary behaviour metrics derived from hip worn and thigh worn accelerometers among older adults with implications for studying physical and cognitive health,.

Sedentary Behavior Classification for Wearable Sensors with a CNN-BiLSTM Model Agreement of sedentary behaviour metrics derived from hip worn and thigh worn accelerometers among older adults with implications for studying physical and cognitive health,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:57:53.628561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 406c2763-8866-40a7-9a21-cb947f2779b1 · outbound

This paper cites Physical activity recognition using posterior-adapted class-based fusion of multiaccelerometer data,.

Sedentary Behavior Classification for Wearable Sensors with a CNN-BiLSTM Model Physical activity recognition using posterior-adapted class-based fusion of multiaccelerometer data,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:57:53.614098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 31034c25-0ad3-4480-8c89-25c9eea749f1 · outbound

This paper cites Activity recognition using a single accelerometer placed at the wrist or ankle,.

Sedentary Behavior Classification for Wearable Sensors with a CNN-BiLSTM Model Activity recognition using a single accelerometer placed at the wrist or ankle,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:57:53.598239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0de43876-322b-4fab-8632-7da1a40c18a0 · outbound

This paper cites The CNN Hip Accelerometer Posture (CHAP) method for classifying sitting patterns from hip accelerometers: A validation study,.

Sedentary Behavior Classification for Wearable Sensors with a CNN-BiLSTM Model The CNN Hip Accelerometer Posture (CHAP) method for classifying sitting patterns from hip accelerometers: A validation study,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:57:53.583483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fb69d40f-e257-4a26-8c93-f76be5751c99 · outbound

This paper cites Accelerometry data in health research: challenges and opportunities,.

Sedentary Behavior Classification for Wearable Sensors with a CNN-BiLSTM Model Accelerometry data in health research: challenges and opportunities,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:57:53.568837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 80f7694c-4de8-4678-aaeb-9151c8a69e8a · outbound

This paper cites Transfer learning improves accelerometer-based child activity recognition via subject-independent adult-domain adaption,.

Sedentary Behavior Classification for Wearable Sensors with a CNN-BiLSTM Model Transfer learning improves accelerometer-based child activity recognition via subject-independent adult-domain adaption,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:57:53.553996Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0768d6b8-75f1-4781-b746-fe2b8ae90700 · outbound

This paper cites Automatically assisting human memory: a SenseCam browser,.

Sedentary Behavior Classification for Wearable Sensors with a CNN-BiLSTM Model Automatically assisting human memory: a SenseCam browser,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:57:53.538487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f51e87d6-d35c-485d-b87e-5038f9218e7e · outbound

This paper cites Using the SenseCam to improve classifications of sedentary behavior in free-living settings,.

Sedentary Behavior Classification for Wearable Sensors with a CNN-BiLSTM Model Using the SenseCam to improve classifications of sedentary behavior in free-living settings,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:57:53.522381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 93f7bd2a-fa9e-4796-a499-7917bd17bed1 · outbound

This paper cites Using accelerometers in youth physical activity studies: a review of methods,.

Sedentary Behavior Classification for Wearable Sensors with a CNN-BiLSTM Model Using accelerometers in youth physical activity studies: a review of methods,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:57:53.507306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 143bd46c-5710-440d-92c8-e3ff2643883b · outbound

This paper cites Best practices for using physical activity monitors in population based research,.

Sedentary Behavior Classification for Wearable Sensors with a CNN-BiLSTM Model Best practices for using physical activity monitors in population based research,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:57:53.492967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation bd9e18e4-d376-4aa9-b5c8-07fb369a7627 · outbound

This paper cites Application of convolutional neural network algorithms for advancing sedentary and activity bout classification,.

Sedentary Behavior Classification for Wearable Sensors with a CNN-BiLSTM Model Application of convolutional neural network algorithms for advancing sedentary and activity bout classification,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:57:53.477984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e2a60fce-1bc9-41f3-975c-121c162e5378 · outbound

This paper cites Validation of accelerometer wear and nonwear time classification algorithm,.

Sedentary Behavior Classification for Wearable Sensors with a CNN-BiLSTM Model Validation of accelerometer wear and nonwear time classification algorithm,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:57:53.462384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7a83fa0d-6d58-4f3f-a31b-57c18809cbd7 · outbound

This paper cites Attention Is All You Need.

Sedentary Behavior Classification for Wearable Sensors with a CNN-BiLSTM Model Attention Is All You Need

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T14:57:53.079767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 912d04a8-f049-4a7d-9765-3d44a9197efd · outbound

This paper cites Sundial: A Family of Highly Capable Time Series Foundation Models.

Sedentary Behavior Classification for Wearable Sensors with a CNN-BiLSTM Model Sundial: A Family of Highly Capable Time Series Foundation Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T14:57:53.085035Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation abcb7068-1bf9-41eb-8721-e45a259de8af · outbound

This paper cites A Time Series is Worth 64 Words: Long-term Forecasting with Transformers.

Sedentary Behavior Classification for Wearable Sensors with a CNN-BiLSTM Model A Time Series is Worth 64 Words: Long-term Forecasting with Transformers

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T14:57:53.089580Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2de5c63c-0756-4d1d-af4a-b6ee725516a1 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Sedentary Behavior Classification for Wearable Sensors with a CNN-BiLSTM Model An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T14:57:53.094357Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 52a6acab-1ddf-446f-8dd1-db187dafd2ce · outbound

This paper cites Divergence measures based on the Shannon entropy,.

Sedentary Behavior Classification for Wearable Sensors with a CNN-BiLSTM Model Divergence measures based on the Shannon entropy,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T14:57:53.098940Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f157d11a-2361-4bc9-aa6b-a9b32a41e2e3 · outbound

This paper cites Amount of time spent in sedentary behaviors in the United States, 2003-2004.

Sedentary Behavior Classification for Wearable Sensors with a CNN-BiLSTM Model Amount of time spent in sedentary behaviors in the United States, 2003-2004

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:57:53.437699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d5a22e91-cf88-4de6-b5f4-52d50958ec1d · outbound

This paper cites Comparison of sedentary estimates between activPAL and hip- and wrist-worn ActiGraph,.

Sedentary Behavior Classification for Wearable Sensors with a CNN-BiLSTM Model Comparison of sedentary estimates between activPAL and hip- and wrist-worn ActiGraph,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:57:53.422245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8670dd64-d290-4ad3-a274-b9dc74ec3d5f · outbound

This paper cites Scaling Vision Transformers,.

Sedentary Behavior Classification for Wearable Sensors with a CNN-BiLSTM Model Scaling Vision Transformers,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:57:53.407383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a0409029-1f30-4088-ad51-8363a5ab6298 · outbound

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

Sedentary Behavior Classification for Wearable Sensors with a CNN-BiLSTM Model Self-supervised learning for human activity recognition using 700,000 person-days of wearable data,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-15T14:57:53.117435Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.