Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data | Paper Citation Record

Observation 3a90c8bc-637d-4d0a-bd40-d51401b0d619 · outbound

This paper cites British journal of hospital medicine81(2), 1–9 (2020).

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data British journal of hospital medicine81(2), 1–9 (2020)

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:37.072290Z

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 ec746651-b853-4165-9df3-83d9007a06aa · outbound

This paper cites Gait & posture26(2), 194–199 (2007).

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data Gait & posture26(2), 194–199 (2007)

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:37.051673Z

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 f7f09aec-06d5-4f55-8b90-823885cac3c8 · outbound

This paper cites ´A., Barros, L.M.: Impact of educational intervention in the perception of hospitalised patients about the risk of falling and associated factors.

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data ´A., Barros, L.M.: Impact of educational intervention in the perception of hospitalised patients about the risk of falling and associated factors

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:37.039753Z

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 4586bcfa-0dd0-4d12-b5b3-ddae66f6cab6 · outbound

This paper cites In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:37.028057Z

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 d2448127-825a-4808-8e58-b502a7977258 · outbound

This paper cites Neurocomputing100, 144–152 (2013).

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data Neurocomputing100, 144–152 (2013)

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:37.015714Z

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 c6c464e7-9618-4073-89db-cfab80ccba9f · outbound

This paper cites IEEE Sensors Journal (2023).

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data IEEE Sensors Journal (2023)

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:37.004995Z

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 dfd0eb64-5a4e-4042-b613-493caf11956d · outbound

This paper cites Measurement192, 34 110870 (2022).

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data Measurement192, 34 110870 (2022)

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:36.992922Z

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 21d74da1-2223-41c0-8b60-1de7cfe9e2ee · outbound

This paper cites an unresolved cited work.

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-15T15:29:36.983436Z

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 d45536c6-ad42-4e36-bb23-ca0526f14ed7 · outbound

This paper cites IEEE Access7, 77702–77722 (2019).

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data IEEE Access7, 77702–77722 (2019)

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:36.973550Z

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 11b3b062-cc18-40ca-aa05-613836f59142 · outbound

This paper cites IEEE transactions on cognitive and developmental systems12(3), 588–600 (2019).

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data IEEE transactions on cognitive and developmental systems12(3), 588–600 (2019)

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:36.962586Z

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 4f9efdce-e2dc-4f4d-a772-293dde6ae026 · outbound

This paper cites Challenges and Trends in Multimodal Fall Detection for Healthcare, 97–120 (2020).

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data Challenges and Trends in Multimodal Fall Detection for Healthcare, 97–120 (2020)

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:36.949976Z

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 7a055333-f995-49e7-9d9a-97d1d8277916 · outbound

This paper cites Mathematics12(24), 3896 (2024).

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data Mathematics12(24), 3896 (2024)

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:36.938255Z

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 ac0466f2-901b-4171-994c-059923a0872e · outbound

This paper cites Sensors24(24), 8051 (2024).

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data Sensors24(24), 8051 (2024)

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:36.926401Z

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 bd5b8804-1399-4a23-9e61-2f5fe6e35f22 · outbound

This paper cites International Journal of Computer Information Systems and Industrial Management Applications8, 195–204 (2016).

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data International Journal of Computer Information Systems and Industrial Management Applications8, 195–204 (2016)

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:36.914217Z

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 a724e950-22f5-4626-b513-1308bcdaff28 · outbound

This paper cites In: Complex Networks & Their Applications IX: Volume 1, Proceedings of the Ninth International Con- ference on Complex Networks and Their Applications COMPLEX NETWORKS 2020, pp.

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data In: Complex Networks & Their Applications IX: Volume 1, Proceedings of the Ninth International Con- ference on Complex Networks and Their Applications COMPLEX NETWORKS 2020, pp

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:36.903575Z

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 97aa237d-e9b8-48bb-bd32-1322ed9472e7 · outbound

This paper cites In: Proceedings of Complex Networks 2017 The Sixth Interna- tional Conference on Complex Networks and Their Applications.

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data In: Proceedings of Complex Networks 2017 The Sixth Interna- tional Conference on Complex Networks and Their Applications

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:36.892884Z

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 731a40df-e710-4aff-8f0f-d5b094b71654 · outbound

This paper cites Computers in Biology and Medicine165, 107420 (2023).

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data Computers in Biology and Medicine165, 107420 (2023)

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:36.879806Z

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 83194714-f15b-4274-81c4-548e3a16f9c6 · outbound

This paper cites Symmetry12(5), 744 (2020).

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data Symmetry12(5), 744 (2020)

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:36.867387Z

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 49b6729c-54d8-4de8-b8f2-dcbf0497afd6 · outbound

This paper cites IEEE Access8, 166117–166137 (2020).

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data IEEE Access8, 166117–166137 (2020)

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:36.855596Z

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 5431e815-a1e8-4100-b81f-44c1f07a8080 · outbound

This paper cites Medical engineering & physics30(1), 84–90 (2008).

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data Medical engineering & physics30(1), 84–90 (2008)

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:36.843565Z

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 aafd6dd8-b47a-4d08-be19-460f3b5ca8bc · outbound

This paper cites In: 2022 6th International Conference on Information Technology (InCIT), pp.

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data In: 2022 6th International Conference on Information Technology (InCIT), pp

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:36.833335Z

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 60efce9f-5859-4acd-9e78-30eff2a33fa2 · outbound

This paper cites In: Proceedings of the 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks (2009).

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data In: Proceedings of the 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks (2009)

Reference 22

Resolution: verified exact; doi, observed 2026-08-15T15:29:36.263563Z

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 efb71279-43c6-4840-8d62-59128ba56111 · outbound

This paper cites Digital Health8, 20552076221074128 (2022) https://doi.org/10.1177/20552076221074128.

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data Digital Health8, 20552076221074128 (2022) https://doi.org/10.1177/20552076221074128

Reference 23

Resolution: verified exact; doi, observed 2026-08-15T15:29:36.251019Z

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 3820e078-2f92-4857-9dcc-65aa117d173f · outbound

This paper cites an unresolved cited work.

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-08-15T15:29:36.821050Z

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 91fdd490-b53b-4de2-9505-588c8131823d · outbound

This paper cites Journal of Real-Time Image Processing9(4), 635–646 (2014) https://doi.org/10.

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data Journal of Real-Time Image Processing9(4), 635–646 (2014) https://doi.org/10

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:36.809959Z

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 04745bf2-1832-48bc-8d98-0ff299eb62a6 · outbound

This paper cites Multime- dia Tools and Applications81(4), 5113–5136 (2022) https://doi.org/10.1007/ s11042-021-11646-w.

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data Multime- dia Tools and Applications81(4), 5113–5136 (2022) https://doi.org/10.1007/ s11042-021-11646-w

Reference 26

Resolution: malformed identifier; raw_fallback, observed 2026-08-15T15:29:36.799200Z

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 ef5f2324-0f71-40d8-9019-da91a0ea5eb4 · outbound

This paper cites IEEE Sensors Journal20(13), 6889–6919 (2020) https://doi.org/10.1109/JSEN.2020.2975522.

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data IEEE Sensors Journal20(13), 6889–6919 (2020) https://doi.org/10.1109/JSEN.2020.2975522

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-15T15:29:36.069842Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca45fe7a-43ac-4a49-91d8-94eee3fd749f · outbound

This paper cites Internet of Things9, 100130 (2020).

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data Internet of Things9, 100130 (2020)

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:36.788486Z

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 bf309669-0d3c-4bb9-8ae2-c5dc093f0cbc · outbound

This paper cites In: 2022 IEEE Symposium on Future Telecommunication Technologies (SOFTT), pp.

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data In: 2022 IEEE Symposium on Future Telecommunication Technologies (SOFTT), pp

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:36.778857Z

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 a18a86d5-4edf-4b41-ba3b-1f8436131efa · outbound

This paper cites Procedia Computer Science203, 16–23 (2022).

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data Procedia Computer Science203, 16–23 (2022)

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:36.769169Z

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 5a5e434c-4ad4-4761-aa36-0d1e3552e45d · outbound

This paper cites Sensors18(1), 20 (2017).

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data Sensors18(1), 20 (2017)

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:36.758224Z

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 ee92bd5d-5320-4528-a90f-bba4b5125edc · outbound

This paper cites Computational intelligence and neuroscience2017(2017).

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data Computational intelligence and neuroscience2017(2017)

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:36.746126Z

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 5e4f4506-9b98-419e-a4ab-3a89f8aae15f · outbound

This paper cites Instrumentation Science & Technology48(1), 22–42 (2020).

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data Instrumentation Science & Technology48(1), 22–42 (2020)

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:36.734870Z

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 14613d2e-670b-42cc-bac0-64fb44c97500 · outbound

This paper cites Multimedia Tools and Applications81(18), 26081–26100 (2022) https://doi.org/10.1007/ s11042-022-11914-3.

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data Multimedia Tools and Applications81(18), 26081–26100 (2022) https://doi.org/10.1007/ s11042-022-11914-3

Reference 34

Resolution: malformed identifier; raw_fallback, observed 2026-08-15T15:29:36.722978Z

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 06703f7d-8eaf-49a2-a41f-b2a8c72292cc · outbound

This paper cites IEEE Access (2025).

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data IEEE Access (2025)

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:36.712817Z

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 b24fa7df-05ff-4451-ae66-a01a774adb9a · outbound

This paper cites Artificial Intelligence Review55(4), 3369–3400 (2022) https://doi.org/10.1007/ s10462-021-10093-5.

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data Artificial Intelligence Review55(4), 3369–3400 (2022) https://doi.org/10.1007/ s10462-021-10093-5

Reference 36

Resolution: malformed identifier; raw_fallback, observed 2026-08-15T15:29:36.702551Z

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 726912cf-112e-4ec1-8ee9-f80add36aadc · outbound

This paper cites IEEE Internet of Things Journal (2025).

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data IEEE Internet of Things Journal (2025)

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:36.692119Z

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 37b7baa7-b4da-4896-98e4-477a2e7eae8f · outbound

This paper cites In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp.

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:36.680538Z

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 7d74adbf-1988-4490-aa4e-c2e506614129 · outbound

This paper cites In: Complex, Intelligent, and Software Intensive Systems: Proceedings of the 12th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS-2018), pp.

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data In: Complex, Intelligent, and Software Intensive Systems: Proceedings of the 12th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS-2018), pp

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:36.670910Z

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 185d8d5c-ca17-4c1d-b0e0-20147ff09ee1 · outbound

This paper cites 37 Biomedical Signal Processing and Control71, 103242 (2022).

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data 37 Biomedical Signal Processing and Control71, 103242 (2022)

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:36.660855Z

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 9ccc50ff-5190-460b-b7ae-bd6a528bbd7f · outbound

This paper cites In: Journal of Physics: Conference Series, vol.

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data In: Journal of Physics: Conference Series, vol

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:36.650341Z

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 644eef61-de35-4afa-a164-218a271540ff · outbound

This paper cites Diagnostics 13(17), 2746 (2023).

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data Diagnostics 13(17), 2746 (2023)

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:36.639157Z

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 ca685312-3b6d-41a8-9dd8-61547b390b29 · outbound

This paper cites Scientific Reports14(1), 21537 (2024).

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data Scientific Reports14(1), 21537 (2024)

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:36.627400Z

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 56afd363-b998-4cfe-94bc-2c19c9a6d57b · outbound

This paper cites Journal of biomechanical engineering141(8), 081010 (2019).

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data Journal of biomechanical engineering141(8), 081010 (2019)

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:36.616157Z

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 c15b843c-1071-41da-82f3-2e0ec19ab378 · outbound

This paper cites Frontiers in bioengineering and biotechnology8, 63 (2020).

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data Frontiers in bioengineering and biotechnology8, 63 (2020)

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:36.605423Z

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 b0ae2104-cea6-4e5b-8732-d9fe625328a2 · outbound

This paper cites In: 2025 Design, Automation & Test in Europe Conference (DATE), pp.

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data In: 2025 Design, Automation & Test in Europe Conference (DATE), pp

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:36.593819Z

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 7a9fc7b8-37c3-4f05-b9bd-23ba30e7b223 · outbound

This paper cites In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:36.583124Z

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 b6bd10cf-c58b-42d5-a888-a7571c098140 · outbound

This paper cites Physics Sensor Based Deep Learning Fall Detection System.

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data Physics Sensor Based Deep Learning Fall Detection System

Reference 48

Resolution: metadata mismatch; local_arxiv, observed 2026-08-15T15:29:36.314033Z

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 a8358fd6-0a49-456e-9067-9e81d3cbfbbf · outbound

This paper cites Mathematics11(8), 1965 (2023).

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data Mathematics11(8), 1965 (2023)

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:36.573437Z

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 56c7c0b2-aab7-499d-9a06-fd87faf8c85f · outbound

This paper cites an unresolved cited work.

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data Unresolved cited work

Reference 50

Resolution: unresolved; raw_fallback, observed 2026-08-15T15:29:36.564042Z

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 c22152d7-8fb9-4aec-aadf-b96e6f2838d4 · outbound

This paper cites BlazePose GHUM Holistic: Real-time 3D Human Landmarks and Pose Estimation.

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data BlazePose GHUM Holistic: Real-time 3D Human Landmarks and Pose Estimation

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-15T15:29:36.158414Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 870a30b0-7a30-45b6-97fa-732b5e79af87 · outbound

This paper cites MediaPipe: A Framework for Building Perception Pipelines.

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data MediaPipe: A Framework for Building Perception Pipelines

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-15T15:29:36.162564Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 120aff9b-dff4-4521-bb13-96c9b3b1e5ac · outbound

This paper cites BlazePose: On-device Real-time Body Pose tracking.

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data BlazePose: On-device Real-time Body Pose tracking

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-15T15:29:36.166156Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c324f36-5c65-4638-996a-b7d8884cda27 · outbound

This paper cites PhD thesis, Carnegie Mellon University Pittsburgh, PA, USA (2019).

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data PhD thesis, Carnegie Mellon University Pittsburgh, PA, USA (2019)

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:36.552883Z

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 f8ff84c3-f9e2-4904-82a8-c03064edc865 · outbound

This paper cites IEEE Sensors Letters4(6), 1–4 (2020).

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data IEEE Sensors Letters4(6), 1–4 (2020)

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:36.540077Z

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 9ac9a1f8-7fc6-445b-bd2e-7352e0b50265 · outbound

This paper cites Applied Sciences12(21), 11031 (2022).

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data Applied Sciences12(21), 11031 (2022)

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:36.527221Z

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 e1f64c87-c84f-4290-9526-0582af34b21b · outbound

This paper cites an unresolved cited work.

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data Unresolved cited work

Reference 57

Resolution: unresolved; raw_fallback, observed 2026-08-15T15:29:36.515190Z

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 1dd2e4a1-5361-4e87-9920-e3d7c5f55ed2 · outbound

This paper cites IEEE Access 9, 28224–28236 (2021).

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data IEEE Access 9, 28224–28236 (2021)

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:36.502915Z

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 89586fb4-8b7a-4830-b2cc-f5716c600638 · outbound

This paper cites Sensors19(9), 1988 (2019).

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data Sensors19(9), 1988 (2019)

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:36.490936Z

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 3c73dee8-3e1f-4e40-a8c9-f3d8b737ac5e · outbound

This paper cites Procedia Computer Science 110, 32–39 (2017).

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data Procedia Computer Science 110, 32–39 (2017)

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:36.475856Z

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 a45fb332-291d-40b8-b302-4b2a64ff2daf · outbound

This paper cites International Journal of Advanced Computer Science and Applications12(6), 599–606 (2021).

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data International Journal of Advanced Computer Science and Applications12(6), 599–606 (2021)

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-15T15:29:36.196541Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08f347d9-d5e6-4010-98c2-340e3e839a5a · outbound

This paper cites BMC genomics21, 1–13 (2020).

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data BMC genomics21, 1–13 (2020)

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:36.455549Z

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 36a5985b-97af-4df8-8c03-bffc5c1ccc2c · outbound

This paper cites Multimedia Tools and Applications83(6), 18091–18118 (2024).

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data Multimedia Tools and Applications83(6), 18091–18118 (2024)

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:36.443217Z

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 1392b3d5-ae04-4cbf-8d59-34374deb207b · outbound

This paper cites In: WAMWB@ MobileHCI, pp.

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data In: WAMWB@ MobileHCI, pp

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:36.431381Z

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 ae1a994a-e93c-4c70-9a8a-56eb957177d4 · outbound

This paper cites Computers, Materials 39 & Continua75(2) (2023).

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data Computers, Materials 39 & Continua75(2) (2023)

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:36.419155Z

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 c196f5c8-e087-4d1c-8022-25518a714437 · outbound

This paper cites International Journal of Technology13(6), 1173–1182 (2022).

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data International Journal of Technology13(6), 1173–1182 (2022)

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:36.406697Z

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 889f6098-c4cc-44b7-80c0-af44a0fb7676 · outbound

This paper cites In: International Conference on Machine Learning, pp.

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data In: International Conference on Machine Learning, pp

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:36.394472Z

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.

Coverage vector

Reference resolution

Outbound references

Pith citing papers