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research-article

A framework to assess Sun salutation videos

Authors:

Gaurav HaritAuthors Info & Claims

ICVGIP '16: Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing

Article No.: 29, Pages 1 - 8

https://doi.org/10.1145/3009977.3010045

Published: 18 December 2016 Publication History

Abstract

There are many exercises which are repetitive in nature and are required to be done with perfection to derive maximum benefits. Sun Salutation or Surya Namaskar is one of the oldest yoga practice known. It is a sequence of ten actions or 'asanas' where the actions are synchronized with breathing and each action and its transition should be performed with minimal jerks. Essentially, it is important that this yoga practice be performed with Grace and Consistency. In this context, Grace is the ability of a person to perform an exercise with smoothness i.e. without sudden movements or jerks during the posture transition and Consistency measures the repeatability of an exercise in every cycle. We propose an algorithm that assesses how well a person practices Sun Salutation in terms of grace and consistency. Our approach works by training individual HMMs for each asana using STIP features[11] followed by automatic segmentation and labeling of the entire Sun Salutation sequence using a concatenated-HMM. The metric of grace and consistency are then laid down in terms of posture transition times. The assessments made by our system are compared with the assessments of the yoga trainer to derive the accuracy of the system. We introduce a dataset for Sun Salutation videos comprising 30 sequences of perfect Sun Salutation performed by seven experts and used this dataset to train our system. While Sun Salutation can be judged on multiple parameters, we focus mainly on judging Grace and Consistency.

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Cited By

Girase SDutta OMahadar AGhodmare ABedekar M(2023)Identifying Incorrect Postures While Performing Sun Salutation Using MoveNetCommunication and Intelligent Systems10.1007/978-981-99-2100-3_45(575-587)Online publication date: 25-Jul-2023
https://doi.org/10.1007/978-981-99-2100-3_45
Jain HHarit GSharma A(2021)Action Quality Assessment Using Siamese Network-Based Deep Metric LearningIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2020.301772731:6(2260-2273)Online publication date: Jun-2021
https://doi.org/10.1109/TCSVT.2020.3017727
Gupta AGupta H(2021)YogaHelp: Leveraging Motion Sensors for Learning Correct Execution of Yoga With FeedbackIEEE Transactions on Artificial Intelligence10.1109/TAI.2021.30961752:4(362-371)Online publication date: Aug-2021
https://doi.org/10.1109/TAI.2021.3096175
Show More Cited By

Index Terms

A framework to assess Sun salutation videos
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Video segmentation
  2. Machine learning
    1. Machine learning approaches
      1. Learning in probabilistic graphical models

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Information & Contributors

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Published In

cover image ACM Other conferences

ICVGIP '16: Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing

December 2016

743 pages

ISBN:9781450347532

DOI:10.1145/3009977

Program Chairs:
Dhruv Batra
Georgia Tech
,
Michael Brown
York University, Canada
,
Vijay Natarajan
Indian Institute of Science, Bangalore

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Google Inc.
QI: Qualcomm Inc.
Tata Consultancy Services
NVIDIA
MathWorks: The MathWorks, Inc.
Microsoft Research: Microsoft Research

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 December 2016

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ICVGIP '16

Sponsor:

QI
MathWorks
Microsoft Research

ICVGIP '16: Indian Conference on Computer Vision, Graphics and Image Processing

December 18 - 22, 2016

Assam, Guwahati, India

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ICVGIP '16 Paper Acceptance Rate 95 of 286 submissions, 33%;

Overall Acceptance Rate 95 of 286 submissions, 33%

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Citations

Cited By

Girase SDutta OMahadar AGhodmare ABedekar M(2023)Identifying Incorrect Postures While Performing Sun Salutation Using MoveNetCommunication and Intelligent Systems10.1007/978-981-99-2100-3_45(575-587)Online publication date: 25-Jul-2023
https://doi.org/10.1007/978-981-99-2100-3_45
Jain HHarit GSharma A(2021)Action Quality Assessment Using Siamese Network-Based Deep Metric LearningIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2020.301772731:6(2260-2273)Online publication date: Jun-2021
https://doi.org/10.1109/TCSVT.2020.3017727
Gupta AGupta H(2021)YogaHelp: Leveraging Motion Sensors for Learning Correct Execution of Yoga With FeedbackIEEE Transactions on Artificial Intelligence10.1109/TAI.2021.30961752:4(362-371)Online publication date: Aug-2021
https://doi.org/10.1109/TAI.2021.3096175
Nagalakshmi CMukherjee S(2021)Classification of Yoga Asanas from a Single Image by Learning the 3D View of Human PosesDigital Techniques for Heritage Presentation and Preservation10.1007/978-3-030-57907-4_3(37-49)Online publication date: 18-Mar-2021
https://doi.org/10.1007/978-3-030-57907-4_3
Shukla BJain HVijay VYadav SMathur AHewson D(2020)A Comparison of Four Approaches to Evaluate the Sit-to-Stand MovementIEEE Transactions on Neural Systems and Rehabilitation Engineering10.1109/TNSRE.2020.298735728:6(1317-1324)Online publication date: Jun-2020
https://doi.org/10.1109/TNSRE.2020.2987357
Shukla BJain HVijay VYadav SHewson D(2020)A Fusion-Based Approach to Identify the Phases of the Sit-to-Stand Test in Older People2020 National Conference on Communications (NCC)10.1109/NCC48643.2020.9056092(1-6)Online publication date: Feb-2020
https://doi.org/10.1109/NCC48643.2020.9056092
Jain HHarit G(2019)An Unsupervised Sequence-to-Sequence Autoencoder Based Human Action Scoring Model2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)10.1109/GlobalSIP45357.2019.8969424(1-5)Online publication date: Nov-2019
https://doi.org/10.1109/GlobalSIP45357.2019.8969424
Jain HHarit G(2018)Leveraging information from imperfect examplesProceedings of the 11th Indian Conference on Computer Vision, Graphics and Image Processing10.1145/3293353.3293416(1-8)Online publication date: 18-Dec-2018
https://dl.acm.org/doi/10.1145/3293353.3293416
Jain HHarit G(2018)Unsupervised Temporal Segmentation of Human Action Using Community Detection2018 25th IEEE International Conference on Image Processing (ICIP)10.1109/ICIP.2018.8451237(1892-1896)Online publication date: Oct-2018
https://doi.org/10.1109/ICIP.2018.8451237
Jain HHarit G(2018)Detecting Missed and Anomalous Action Segments Using Approximate String Matching AlgorithmComputer Vision, Pattern Recognition, Image Processing, and Graphics10.1007/978-981-13-0020-2_10(101-111)Online publication date: 26-Apr-2018
https://doi.org/10.1007/978-981-13-0020-2_10

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