Recent days have stamped enormous upsurge about health awareness in society. Self tutoring systems for supervising the performed exercises offer numerous advantages and are therefore emerging as an entity of dire necessity in health-sector. Considering the significantly increasing global acceptance of ‘\({{Yog\bar{a}sana}}\)’ as one of the most preferred exercise, this paper proposes a novel and an intelligent vision-based self-tutoring system for \({{Yog\bar{a}sana}}\). The proposed system, ’e-YogaGuru’ analyzes the body movements while performing \({{Yog\bar{a}sana}}\), provides feedback about its correctness and further, suggests amendment, if required. Incorporation of angle features in the novel state transition-based approach addresses the earlier reported issues raised due to human anthropometry and variance in the execution speed. Consideration of hold time and suggestion of amendment at two levels, abstract level and detailed amendment (sequences of pre-posture, main-posture and post-posture), make the proposed e-YogaGuru unique and efficient. System is trained for 21 postures derived from the skeleton stream of 8 experts exhibiting variations in anthropometry and execution speed (Knowledge base). A dataset composed of 1750 video sequences (7 \({{Yog\bar{a}sana}}\) performed by 25 practitioners) is used to validate the efficacy of the devised approach. The proposed e-YogaGuru achieved 98.29 % accuracy in correctly identifying the \({{Yog\bar{a}sana}}\) and has been able to suggest required amendment in the incorrectly performed \({{Yog\bar{a}sana}}\) with an accuracy of 96.34 %. Proposed ‘e-YogaGuru’ incorporates significant parameters (hold time and amendment) and achieves appreciable accuracies, thus it not only out-performs the earlier reported systems but also marks a long bounce towards practical deployment.

最近几天盖起了社会对健康意识的巨大热潮。用于监督所执行锻炼的自学系统具有许多优势，因此正在成为卫生部门迫切需要的实体。考虑到' \（{{Yog \ bar {a} sana}} \） '作为最受欢迎的练习之一的全球接受度的显着提高，本文提出了一种新颖且基于智能的基于视觉的自学系统，用于\（ {{Yog \ bar {a} sana}} \）。建议的系统“ e-YogaGuru ”在执行\（{{Yog \ bar {a} sana}} \}时分析人体运动，提供有关其正确性的反馈，并进一步提出修改建议（如果需要）。在基于状态转换的新颖方法中引入角度特征可解决由于人体测量法和执行速度差异而引起的早期报道的问题。e-YogaGuru的保留时间和抽象修正和详细修正（姿势前，主体姿势和姿势后的顺序）这两个级别的考虑，使所提出的e-YogaGuru独特而有效。该系统经过培训，可从8位专家的骨骼流中得出21种姿势，这些姿势在人体测量学和执行速度（知识基础）方面表现出变化。由1750个视频序列（7 \（{{Yog \ bar {a} sana}} \）组成的数据集由25位从业人员执行）验证了该方法的有效性。拟议的e-YogaGuru在正确识别\（{{Yog \ bar {a} sana}} \}方面达到了98.29％的准确度，并且能够建议对错误执行的\（{{Yog \ bar {a} sana}} \）的准确性为96.34％。拟议中的“ e-YogaGuru”具有重要的参数（保持时间和修正值），并具有可观的准确性，因此，它不仅跑赢了早先报道的系统，而且标志着向实际部署的漫长反弹。