Keyword search (4,164 papers available)

"vision" Keyword-tagged Publications:

Title Authors PubMed ID
1 Attention-Fusion-Based Two-Stream Vision Transformer for Heart Sound Classification Ranipa K; Zhu WP; Swamy MNS; 41155032
ENCS
2 Lung Nodule Malignancy Classification Integrating Deep and Radiomic Features in a Three-Way Attention-Based Fusion Module Khademi S; Heidarian S; Afshar P; Mohammadi A; Sidiqi A; Nguyen ET; Ganeshan B; Oikonomou A; 41150036
ENCS
3 MedCLIP-SAMv2: Towards universal text-driven medical image segmentation Koleilat T; Asgariandehkordi H; Rivaz H; Xiao Y; 40779830
ENCS
4 Inferring concussion history in athletes using pose and ground reaction force estimation and stability analysis of plyometric exercise videos Alves W; Babouras A; Martineau PA; Schutt D; Robbins S; Fevens T; 40632382
ENCS
5 Real-time motion detection using dynamic mode decomposition Mignacca M; Brugiapaglia S; Bramburger JJ; 40421310
MATHSTATS
6 Deep neural network-based robotic visual servoing for satellite target tracking Ghiasvand S; Xie WF; Mohebbi A; 39440297
ENCS
7 Masters students' satisfaction with academic supervision and experiences of mental and emotional distress and wellbeing Nadine S Bekkouche 38848331
EDUCATION
8 Comparing novel smartphone pose estimation frameworks with the Kinect V2 for knee tracking during athletic stress tests Babouras A; Abdelnour P; Fevens T; Martineau PA; 38730186
ENCS
9 Breamy: An augmented reality mHealth prototype for surgical decision-making in breast cancer Najafi N; Addie M; Meterissian S; Kersten-Oertel M; 38638506
ENCS
10 CosSIF: Cosine similarity-based image filtering to overcome low inter-class variation in synthetic medical image datasets Islam M; Zunair H; Mohammed N; 38492455
ENCS
11 Intersection of Intimate Partner Violence, Partner Interference, and Family Supportive Supervision on Victims' Work Withdrawal Isola C; Granger S; Turner N; LeBlanc MM; Barling J; 37359457
JMSB
12 Single Digit Index Finger Amputation-To Replant or Not? Thibedeau M; Ramji M; McKenzie M; Yeung J; Nickerson DA; 36755823
BIOLOGY
13 Who's cooking tonight? A time-use study of coupled adults in Toronto, Canada Liu B; Widener MJ; Smith LG; Farber S; Gesink D; Minaker LM; Patterson Z; Larsen K; Gilliland J; 36339032
ENCS
14 A Newly Identified Impairment in Both Vision and Hearing Increases the Risk of Deterioration in Both Communication and Cognitive Performance Guthrie DM; Williams N; Campos J; Mick P; Orange JB; Pichora-Fuller MK; Savundranayagam MY; Wittich W; Phillips NA; 35859361
PSYCHOLOGY
15 Assessing optimal colour and illumination to facilitate reading: an analysis of print size Morrice E; Murphy C; Soldano V; Addona C; Wittich W; Johnson AP; 34549808
PSYCHOLOGY
16 Assessing optimal colour and illumination to facilitate reading. Morrice E, Murphy C, Soldano V, Addona C, Wittich W, Johnson AP 33533095
PSYCHOLOGY
17 The Relationship Between Cognitive Status and Known Single Nucleotide Polymorphisms in Age-Related Macular Degeneration. Murphy C; Johnson AP; Koenekoop RK; Seiple W; Overbury O; 33178008
PSYCHOLOGY
18 CCCDTD5 recommendations on early non cognitive markers of dementia: A Canadian consensus Montero-Odasso M; Pieruccini-Faria F; Ismail Z; Li K; Lim A; Phillips N; Kamkar N; Sarquis-Adamson Y; Speechley M; Theou O; Verghese J; Wallace L; Camicioli R; 33094146
CRDH
19 The Prevalence of Hearing, Vision, and Dual Sensory Loss in Older Canadians: An Analysis of Data from the Canadian Longitudinal Study on Aging. Mick PT, Hämäläinen A, Kolisang L, Pichora-Fuller MK, Phillips N, Guthrie D, Wittich W 32546290
PSYCHOLOGY
20 Hearing and Cognitive Impairments Increase the Risk of Long-term Care Admissions Williams N; Phillips NA; Wittich W; Campos JL; Mick P; Orange JB; Pichora-Fuller MK; Savundranayagam MY; Guthrie DM; 31911955
PSYCHOLOGY
21 Understanding Events by Eye and Ear: Agent and Verb Drive Non-anticipatory Eye Movements in Dynamic Scenes. de Almeida RG, Di Nardo J, Antal C, von Grünau MW 31649574
PSYCHOLOGY
22 Integration of Growth and Cell Size via the TOR Pathway and the Dot6 Transcription Factor in Candida albicans. Chaillot J, Tebbji F, Mallick J, Sellam A 30593490
BIOLOGY

 

Title:Comparing novel smartphone pose estimation frameworks with the Kinect V2 for knee tracking during athletic stress tests
Authors:Babouras AAbdelnour PFevens TMartineau PA
Link:https://pubmed.ncbi.nlm.nih.gov/38730186/
DOI:10.1007/s11548-024-03156-5
Publication:International journal of computer assisted radiology and surgery
Keywords:Computer visionKnee kinematicsPose estimationSports medicine
PMID:38730186 Category: Date Added:2024-05-11
Dept Affiliation: ENCS
1 Experimental Surgery, McGill University, Montréal, QC, H3A 0G4, Canada. athanasios.babouras@mail.mcgill.ca.
2 Experimental Surgery, McGill University, Montréal, QC, H3A 0G4, Canada.
3 Computer Science and Software Engineering, Concordia University, Montréal, QC, H3G 1M8, Canada.

Description:

Purpose: To compare the accuracy of the Microsoft Kinect V2 with novel pose estimation frameworks, in assessing knee kinematics during athletic stress tests, for fast and portable risk assessment of anterior cruciate ligament (ACL) injury.

Methods: We captured 254 varsity athletes, using the Kinect V2 and a smartphone application utilizing Google's MediaPipe framework. The devices were placed as close as possible and used to capture a person, facing the cameras, performing one of three athletic stress tests at a distance of 2.5 ms. Custom software translated the results from both frameworks to the same format. We then extracted relevant knee angles at key moments of the jump and compared them, using the Kinect V2 as the ground truth.

Results: The results show relatively small angle differences between the two solutions in the coronal plane and moderate angle differences on the sagittal plane. Overall, the MediaPipe framework results seem to underestimate both knee valgus angles and knee sagittal angles compared to the Kinect V2.

Conclusion: This preliminary study demonstrates the potential for Google's MediaPipe framework to be used for calculating lower limb kinematics during athletic stress test motions, which can run on most modern smartphones, as it produces similar results to the Kinect V2. A smartphone application similar to the one developed could potentially be used for low cost and widespread ACL injury prevention.





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