Yoga Posture Recognition and Quantitative Evaluation with Wearable Sensors Based on Two-Stage Classifier and Prior Bayesian Network

Abstract

Currently, with the satisfaction of people’s material life, sports, like yoga and tai chi, have become essential activities in people’s daily life. For most yoga amateurs, they could only learn yoga by self-study, like mechanically imitating from yoga video. They could not know whether they performed standardly without feedback and guidance. In this paper, we proposed a full-body posture modeling and quantitative evaluation method to recognize and evaluate yoga postures to provide guidance to the learner. Back propagation artificial neural network (BP-ANN) was adopted as the first classifier to divide yoga postures into different categories, and fuzzy C-means (FCM) was utilized as the second classifier to classify the postures in a category. The posture data on each body part was regarded as a multidimensional Gaussian variable to build a Bayesian network. The conditional probability of the Gaussian variable corresponding to each body part relative to the Gaussian variable corresponding to the connected body part was used as criterion to quantitatively evaluate the standard degree of body parts. The angular differences between nonstandard parts and the standard model could be calculated to provide guidance with an easily-accepted language, such as “lift up your left arm”, “straighten your right forearm”. To evaluate our method, a wearable device with 11 inertial measurement units (IMUs) fixed onto the body was designed to measure yoga posture data with quaternion format, and the posture database with a total of 211,643 data frames and 1831 posture instances was collected from 11 subjects. Both the posture recognition test and evaluation test were conducted. In the recognition test, 30% data was randomly picked from the database to train BP-ANN and FCM classifiers, and the recognition accuracy of the remaining 70% data was 95.39%, which is highly competitive with previous posture recognition approaches. In the evaluation test, 30% data were picked randomly from subject three, subject four, and subject six, to train the Bayesian network. The probabilities of nonstandard parts were almost all smaller than 0.3, while the probabilities of standard parts were almost all greater than 0.5, and thus the nonstandard parts of body posture could be effectively separated and picked for guidance. We also tested separately the trainers’ yoga posture performance in the condition of without and with guidance provided by our proposed method. The results showed that with guidance, the joint angle errors significantly decreased.

Evidence for the Effectiveness of Feedback from Wearable Inertial Sensors during Work-Related Activities: A Scoping Review

“…A total of 53 studies were excluded in full-text screening as follows: WIST studies without feedback ( n = 14) [ 55 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 ]; feedback without an inertial sensor (s) [ 94 ]; sensors integrated into equipment ( n = 5), e.g., seat sensors and robotic devices rather than those worn by an individual [ 95 , 96 , 97 , 98 , 99 ]; standing balance and/or lower body sway ( n = 5) [ 100 , 101 , 102 , 103 , 104 ]; abstracts ( n = 7) [ 105 , 106 , 107 , 108 , 109 , 110 , 111 ]; stroke/other neurological rehabilitation studies ( n = 4) [ 112 , 113 , 114 , 115 ]; a non-work setting ( n = 3) [ 116 , 117 , 118 ]; no evaluation of WIST feedback effectiveness ( n = 6) [ 119 , 120 , 121 , 122 , 123 , 124 ]; research proposal ( n = 1) [ 125 ]; and validity and reliability s...…”

Task Analysis on Yoga Poses Toward a Wearable Sensor-based Learning System for Users with Visual Impairment

“…In the future, we plan to evaluate the potential of sensor locations for providing vibro-tactile feedback as well. In previous works, only verbal feedback was given to a user for pos-ture correction (Rector et al, 2013;Wu et al, 2019). In one example yoga pose, Reverse warrior, Eyes-Free Yoga gave 20.7 verbal feedback instructions (e.g., "Lower your arms", 'Bring your arms closer to your head") on average per participant for posture correction (Rector et al, 2013).…”

“…An automatic yoga pose recognition system was also developed using 11 wearable inertial sensors for full-body tracking (Wu, Zhang, Chen, & Fu, 2019). This system was able to recognize 18 different yoga poses with 89.3% accuracy using a backpropagation artificial neural network and fuzzy C-means methods (Wu et al, 2019). In practice, a yoga garment with 11 inertial sensors may be challenging to maintain (e.g., charging, washing) and costly to manufacture.…”

Recognition of Off-line Handwritten Uyghur Words Using Bayesian Networks with Grapheme Nodes

“…A BN, which is also known as a confidence network, is a data model based on probabilistic analysis and graph theory that is used to predict uncertain events. The ability of BN models to synthesize prior information and sample information effectively has led to their increasing application in the field of pattern recognition in recent years [28,29]. To capitalize on these benefits, the present work constructed a hierarchical matching model between words, conjoined segments, and graphemes using a discrete BN based on the grapheme sequences generated by the segmentation of an Uyghur word.…”