Finding Features of Actions Efficiently Synchronized with Dishwashing Robot

In this study, we propose a method for extracting the characteristics of body motions that contribute to reducing the takt time in a cooperative task between a dishwashing robot and a human operator. The proposed method collects the takt time and motion data from novice operators until they become experienced using an inexpensive acceleration sensor. The operation data is classified into experienced and novice periods using the variance value of the takt time. In addition, the Hidden Markov Model is generated to classify the motion data into multiple motion phases. The motion features of the operator are extracted for each phase from the generated model. The proposed method finds the motion features whose difference between the experienced and novice periods are similar to the takt time transition. It uses them as important variables. We verified the effectiveness of the proposed method by conducting experiments that simulate actual work at a restaurant. The Hidden Markov Model classified the operation phases into three categories with the AUC of 0.9. In all samples, we were able to extract the motion characteristics of the experienced operators. This study showed the potential to improve the speed of novice's progress by the extracted motion characteristics to improve education guidelines and to show operators how they should physically move.

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Recognition of Badminton Shot Action Based on the Improved Hidden Markov Model

Journal of Healthcare Engineering ◽

10.1155/2021/7892902 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Chao Ma ◽

Dayang Yu ◽

Hao Feng

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Hidden Markov ◽

Rapid Development ◽

Recognition Rate ◽

Sliding Window ◽

Acceleration Sensor ◽

Sports Activity ◽

Average Recognition Rate ◽

Day By Day

In recent years, with the rapid development of sports, the number of people playing various sports is increasing day by day. Among them, badminton has become one of the most popular sports because of the advantages of fewer restrictions on the field and ease of learning. This paper develops a wearable sports activity classification system for accurately recognizing badminton actions. A single acceleration sensor fixed on the end of the badminton racket handle is used to collect the data of the badminton action. The sliding window segmentation technique is used to extract the hitting signal. An improved hidden Markov model (HMM) is developed to identify standard 10 badminton strokes. These include services, forehand chop, backhand chop the goal, the forehand and backhand, forehand drive, backhand push the ball, forehand to pick, pick the ball backhand, and forehand. The experimental results show that the model designed can recognize ten standard strokes in real time. Compared with the traditional HMM, the average recognition rate of the improved HMM is improved by 7.3%. The comprehensive recognition rate of the final strokes can reach up to 95%. Therefore, this model can be used to improve the competitive level of badminton players.

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HMM-BASED MODEL FOR DANCE MOTIONS WITH POSE REPRESENTATION

TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi Kendali Komputer Elektrik dan Elektronika ◽

10.25124/tektrika.v1i2.1744 ◽

2019 ◽

Vol 1 (2) ◽

Author(s):

Nurfitri Anbarsanti

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Hidden Markov ◽

Range Of Movement ◽

Motion Data ◽

Noisy Input ◽

Discrete States ◽

Human Pose

This paper presents a model for human dance motions based on hidden markov model. The whole dance is defined as sequences of several finite distinct gestures. Dance gestures are cast as hidden discrete states and phrase of dance as a sequence of gestures. In order to map the skeleton motion data to a smaller set of features, an angular skeleton representation of the human pose is also designed, for recognition robustness under noisy input of 3D sensor. A pose of dance is defined by this angular skeleton representation which can be quantified based on range of movement for discrete hidden markov model.

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Motion Recognition Based on Spatial Temporal HMM and Improved KPCA

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.556-562.2919 ◽

2014 ◽

Vol 556-562 ◽

pp. 2919-2923

Author(s):

Zhen Yi Wang ◽

Rui Chen ◽

Ao Shuang Dong

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Recognition Accuracy ◽

Hidden Markov ◽

Contribution Rate ◽

Recognition Time ◽

Motion Recognition ◽

Intelligent Surveillance ◽

Motion Features ◽

Monitoring Area

In order to solve the problem that traditional intelligent surveillance is easily influenced by blocking and the capture views is limited, this paper presents a new method with 3 Kinects. Kinects are used to capture the human skeleton data and extract motion features. Principle component of raw data is extracted by using improved KPCA. Classifier is generated by using spatial-temporal Hidden Markov Model. A set of specific motions is analyzed in monitoring area. Experimental results show that this method can efficiently solve the problems that blocking and skeleton data is incomplete. It can also improve the recognition accuracy. The improved KPCA can improve the cumulative contribution rate and reduce the motion recognition time.

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