• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.219-221
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• 2007

Recent changes to ubiquitous computing requires more natural human-computer(HCI) interfaces that provide high information accessibility. Hand-gesture, i.e., gestures performed by one 'or two hands, is emerging as a viable technology to complement or replace conventional HCI technology. This paper deals with hand-posture recognition. Hand-posture database construction is important in hand-posture recognition. Human hand is composed of 27 bones and the movement of each joint is modeled by 23 degrees of freedom. Even for the same hand-posture,. grabbed images may differ depending on user's characteristic and relative position between the hand and cameras. To solve the difficulty in defining hand-postures and construct database effective in size, we present a method using a 3D hand model. Hand joint angles for each hand-posture and corresponding silhouette images from many viewpoints by projecting the model into image planes are used to construct the ?database. The proposed method does not require additional equations to define movement constraints of each joint. Also using the method, it is easy to get images of one hand-posture from many vi.ewpoints and distances. Hence it is possible to construct database more precisely and concretely. The validity of the method is evaluated by applying it to the hand-posture recognition system.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.4
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• pp.267-273
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• 2016

Hand posture recognition is an important technique to enable a natural and familiar interface in the human-computer interaction (HCI) field. This paper introduces a hand posture recognition method using a depth camera. Moreover, the hand posture recognition method is incorporated with the Moving Picture Experts Group Rich Media User Interface (MPEG-U) Advanced User Interaction (AUI) Interface (MPEG-U part 2), which can provide a natural interface on a variety of devices. The proposed method initially detects positions and lengths of all fingers opened, and then recognizes the hand posture from the pose of one or two hands, as well as the number of fingers folded when a user presents a gesture representing a pattern in the AUI data format specified in MPEG-U part 2. The AUI interface represents a user's hand posture in the compliant MPEG-U schema structure. Experimental results demonstrate the performance of the hand posture recognition system and verified that the AUI interface is compatible with the MPEG-U standard.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.763-774
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• 2015

Hand posture recognition has been a wide region of applications in Human Computer Interaction and Computer Vision for many years. The problem arises mainly due to the high dexterity of hand and self-occlusions created in the limited view of the camera or illumination variations. To remedy these problems, a hand posture recognition method using 3-D point cloud is proposed to explicitly utilize 3-D information from depth maps in this paper. Firstly, hand region is segmented by a set of depth threshold. Next, hand image normalization will be performed to ensure that the extracted feature descriptors are scale and rotation invariant. By robustly coding and pooling 3-D facets, the proposed descriptor can effectively represent the various hand postures. After that, SVM with Gaussian kernel function is used to address the issue of posture recognition. Experimental results based on posture dataset captured by Kinect sensor (from 1 to 10) demonstrate the effectiveness of the proposed approach and the average recognition rate of our method is over 96%.

• PNF and Movement
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• v.10 no.1
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• pp.9-17
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• 2012

Purpose : This study's purpose is consideration about change of the hand grip strength according to different posture and shoulder flexion angle. The shoulder joint permits the greatest mobility and carries out the important function of stabilization for hand use. Hand grip activity is important to evaluate while assessing loads of shoulder in hand mobilities. Methods : Thirty(15 male, 15 female) college students with unknown shoulder dysfunction participated subject in five different positions of elbow extension with sitting and standing posture, different positions is followed : (1) shoulder $0^{\circ}$ flexion (2) shoulder $45^{\circ}$ flexion (3) shoulder $90^{\circ}$ flexion (4) shoulder $135^{\circ}$ flexion (5) shoulder $180^{\circ}$ flexion. Results : On the average, in the hand grip strength, the standing posture is higher than sitting posture. Sitting posture showed a most high level at the man's $0^{\circ}$ and woman's $135^{\circ}$. And standing posture showed a most high level at the man's $135^{\circ}$ and woman's $90^{\circ}$. Conclusion : The paired t-test was used to determine the different in grip strength between sitting and standing posture by shoulder angle change. There was no significant difference between the five position by sitting and standing posture. In man, correlation analysis revealed significant connection for all five position by sitting and standing posture. And in woman, correlation analysis revealed connection for all five position by sitting and standing posture.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.3
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• pp.271-274
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• 2006

This paper deals with a similarity decision method between the shape of hand-postures and their structures to improve performance of the vision-based hand-posture recognition system. Hand-posture recognition by vision sensors has difficulties since the human hand is an object with high degrees of freedom, and hence grabbed images present complex self-occlusion effects and, even for one hand-posture, various appearances according to viewing directions. Therefore many approaches limit the relative angle between cameras and hands or use multiple cameras. The former approach, however, restricts user's operation area. The latter requires additional considerations on the way of merging the results from each camera image to get the final recognition result. To recognize hand-postures, we use both of appearance and structural features and decide the similarity between the two types of features by learning.

• Journal of the Ergonomics Society of Korea
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• v.26 no.1
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• pp.29-37
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• 2007

This study was performed to evaluate the characteristics of transmitted vibration to hand-arm system under different work posture while operating a light-weighted powered hand grinder. For the experiment, 8 different types of wrist posture (natural, unlar-flexion, radual-flexion, flexion, extension, complex posture, and etc.) and 3 types of feed force (20[N], 50[N], 70[N]) were considered. 10 male subjects were employed to polish metal plate with a hand grinder. All of them were normal and healthy with no history and symptom of the work related musculoskeletal disorders in the dominant hand. Vibration acceleration data were recorded with sampling rate, 2048[Hz]. In addition, unweighted overall R.M.S. acceleration at the tool and wrist, and transmissibility between them were used to evaluate factors from the recorded tri-axial vibration acceleration. The results indicate that transmissibility of natural wrist posture was significantly higher than others. In addition, as the feed force becomes larger, the vibration was transmitted in large quantities to hand-arm system through radius.

• Journal of Broadcast Engineering
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• v.19 no.1
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• pp.83-95
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• 2014

Hand posture recognition is an important technique to enable a natural and familiar interface in HCI(human computer interaction) field. In this paper, we introduce a hand posture recognition method by using a depth camera. Moreover, the hand posture recognition method is incorporated with MPEG-U based advanced user interaction (AUI) interface system, which can provide a natural interface with a variety of devices. The proposed method initially detects positions and lengths of all fingers opened and then it recognizes hand posture from pose of one or two hands and the number of fingers folded when user takes a gesture representing a pattern of AUI data format specified in the MPEG-U part 2. The AUI interface system represents user's hand posture as compliant MPEG-U schema structure. Experimental results show performance of the hand posture recognition and it is verified that the AUI interface system is compatible with the MPEG-U standard.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.3 s.309
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• pp.39-49
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• 2006

This paper proposes a hand-posture recognition method using reinforcement learning for the performance improvement of vision-based hand-posture recognition. The difficulties in vision-based hand-posture recognition lie in viewing direction dependency and self-occlusion problem due to the high degree-of-freedom of human hand. General approaches to deal with these problems include multiple camera approach and methods of limiting the relative angle between cameras and the user's hand. In the case of using multiple cameras, however, fusion techniques to induce the final decision should be considered. Limiting the angle of user's hand restricts the user's freedom. The proposed method combines angular features and appearance features to describe hand-postures by a two-layered data structure and reinforcement learning. The validity of the proposed method is evaluated by appling it to the hand-posture recognition system using three cameras.

• Journal of the Ergonomics Society of Korea
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• v.29 no.5
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• pp.727-734
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• 2010

The objective of this study was to evaluate the resting postures of the fingers and wrist based on the biomechanical model in term of hand posture (neutral, pronation, and supination) and gender (male and female). The finger and wrist joint angles were measured with VICON motion system. The EMG system was used to examine the muscle activity in the resting condition. The participants consisted of twenty male and twenty female students. The angles of the fingers and wrist were analyzed by means of the coordinate system associated with the International Society of Biomechanics. Hand posture was significant for all the joints. The finger and wrist joint flexed in supination more than in neutral and pronation. The hand posture and gender were not significant for the results of muscle activity, but it had larger muscle activities in supination more than in neutral and pronation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2585-2598
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• 2015

Hand posture recognition has played a very important role in Human Computer Interaction (HCI) and Computer Vision (CV) for many years. The challenge arises mainly due to self-occlusions caused by the limited view of the camera. In this paper, a robust hand posture recognition approach based on 3D point cloud from two RGB-D sensors (Kinect) is proposed to make maximum use of 3D information from depth map. Through noise reduction and registering two point sets obtained satisfactory from two views as we designed, a multi-viewed hand posture point cloud with most 3D information can be acquired. Moreover, we utilize the accurate reconstruction and classify each point cloud by directly matching the normalized point set with the templates of different classes from dataset, which can reduce the training time and calculation. Experimental results based on posture dataset captured by Kinect sensors (from digit 1 to 10) demonstrate the effectiveness of the proposed method.