A gesture recognition interface with upper body model-based pose tracking

Oh, Chi-Min; Islam, Zahidul; Park, Jae-Wan; Lee, Chil-Woo

doi:10.1109/iccet.2010.5485583

Cited by 7 publications

(4 citation statements)

References 8 publications

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“…Elmezain et al [7] proposed the usage of HMM that has a classifier for the gesture spotting approach for recognizing Arabic alphabets and numbers. In Oh et al's research [3], they came up with 2 layered gesture recognition models to recognize arm movements.…”

Section: Introductionmentioning

confidence: 99%

Design of body gesture recognition system for regularity and repeatability gestures

Choi

Ko²,

Kim

et al. 2014

2014 14th International Conference on Control, Automation and Systems (ICCAS 2014)

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People often use non-linguistic communication methods such as physical gestures, which compose over 70% of our overall interaction with others. These physical gestures have regularity and repeatability as their common traits. This study is mainly discussing gesture recognition system for regular and repeated gestures. Torso PCA frame method is applied to angle transformation for such gesture recognition. In addition, a feature set is defined through extracting the patterns using envelope detection method and is applied to multi-layer perceptron for gesture recognition. For our experiment, skeletal structure was collected using Kinect, and 8 gestures were selected that people regularly use in real life. The recognition system was confirmed based on variety of people as our sample and the average accuracy was 89%.

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Section: Introductionmentioning

confidence: 99%

Design of body gesture recognition system for regularity and repeatability gestures

Choi

Ko²,

Kim

et al. 2014

2014 14th International Conference on Control, Automation and Systems (ICCAS 2014)

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show abstract

“…Hidden Markov model (HMM), support vector machine (SVM), decision tree (DT), and template map are the most widely used methods. Yamato et al [15] and Oh et al [16] adopted HMM to model tennis batting postures and upper-body posture, respectively. Good average recognition accuracy was achieved.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Chen

et al. 2019

Sensors

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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.

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“…For instance, Lee et al detected head and shoulder contours using Maximum Posteriori Probability from RGB images and estimated the pose using a body outline model [1]. Oh et al proposed upper body pose estimation using a distance transform from human silhouettes in RGB images [2]. Their proposed method worked under a restricted environment with sufficient light.…”

Section: Introductionmentioning

confidence: 99%

Upper Body Pose Recognition with Labeled Depth Body Parts via Random Forests and Support Vector Machines

Lim¹,

Cho²,

Han³

et al. 2013

IJIET

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Human pose recognition has become an active research topic lately in the field of human computer interface (HCI). However it presents technical challenges due to the complexity of human motion. In this paper, we propose a novel methodology for human upper body pose recognition using labeled (i.e., recognized) human body parts in depth silhouettes. Our proposed method performs human upper body parts labeling using trained random forests (RFs) and utilizes support vector machines (SVMs) to recognize various upper body poses. To train RFs, we create a database of synthetic depth silhouettes of the upper body and their corresponding upper body parts labeled maps using a commercial computer graphics package. Once the body parts get labeled with the trained RFs, a skeletal upper body model is generated from the labeled body parts. Then, SVMs are trained with a set of joint angle features to recognize seven upper body poses. The experimental results show the mean recognition rate of 97.62%. Our proposed method should be useful as a near field HCI technique to be used in applications such as smart computer interfaces

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A gesture recognition interface with upper body model-based pose tracking

Cited by 7 publications

References 8 publications

Design of body gesture recognition system for regularity and repeatability gestures

Design of body gesture recognition system for regularity and repeatability gestures

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

Upper Body Pose Recognition with Labeled Depth Body Parts via Random Forests and Support Vector Machines

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