• The Journal of the Acoustical Society of Korea
• /
• v.19 no.2
• /
• pp.90-94
• /
• 2000

In this paper, we propose an efficient method of estimating a critical threshold which is used to reject unreliable patterns in a HMM based recognition system. The rejection methods based on the anti-models which are formulated as the statistical hypothesis determine whether or not to accept an input pattern by comparing the likelihood ratio of HMM and anti-models to a critical threshold. It is quite difficult to fix a threshold for the probability of a HMM because the range of such probabilities varies severely depending on the chosen class model. We estimate the critical threshold, which is very class-dependent, using the likelihood scores for the training database. In our experiments, we applied the proposed estimating method of the threshold to the HMM based 3D hand gesture recognition system. We found that this method can be used successfully for rejecting unreliable input gestures regardless of the types of anti-models.

• Journal of KIISE:Software and Applications
• /
• v.36 no.10
• /
• pp.836-850
• /
• 2009

Human posture recognition has attracted tremendous attention in ubiquitous environment, performing arts and robot control so that, recently, many researchers in pattern recognition and computer vision are working to make efficient posture recognition system. However the most of existing studies is very sensitive to human variations such as the rotation or the translation of body. This is why the feature, which is extracted from the feature extraction part as the first step of general posture recognition system, is influenced by these variations. To alleviate these human variations and improve the posture recognition result, this paper presents 3D Star Skeleton and Principle Component Analysis (PCA) based feature extraction methods in the multi-view environment. The proposed system use the 8 projection maps, a kind of depth map, as an input data. And the projection maps are extracted from the visual hull generation process. Though these data, the system constructs 3D Star Skeleton and extracts the rotation invariant feature using PCA. In experimental result, we extract the feature from the 3D Star Skeleton and recognize the human posture using the feature. Finally we prove that the proposed method is robust to human variations.

• Journal of rehabilitation welfare engineering & assistive technology
• /
• v.11 no.4
• /
• pp.371-376
• /
• 2017

In this paper, we propose an algorithm that can recognize the pattern regardless of the sensor position when performing EMG pattern recognition using circular EMG system equipment. Fourteen features were extracted by using the data obtained by measuring the eight channel EMG signals of six motions for 1 second. In addition, 112 features extracted from 8 channels were analyzed to perform principal component analysis, and only the data with high influence was cut out to 8 input signals. All experiments were performed using k-NN classifier and data was verified using 5-fold cross validation. When learning data in machine learning, the results vary greatly depending on what data is learned. EMG Accuracy of 99.3% was confirmed when using the learning data used in the previous studies. However, even if the position of the sensor was changed by only 22.5 degrees, it was clearly dropped to 67.28% accuracy. The accuracy of the proposed method is 98% and the accuracy of the proposed method is about 98% even if the sensor position is changed. Using these results, it is expected that the convenience of the users using the circular EMG system can be greatly increased.

• Korean Parent-Child Health Journal
• /
• v.1
• /
• pp.56-74
• /
• 1998

The language of the newborn, like that of adults, is one of gesture, posture, and expression(Lewis, 1980). Helping parents understand and respond to their newborn's cues will make caring for their baby more enjoyable and may well provide the foundation for a communicative bond that will last lifetime. Infant state provides a dynamic pattern reflecting the full behavioral repertoire of the healthy infant(Brazelton, 1973, 1984). States are organized in a predictable emporal sequence and provide a basic classification of conditions that occur over and over again(Wolff, 1987). They are recognized by characteristic behavioral patterns, physiological changes, and infants' level of responsiveness. Most inportantly, however, states provide caregivers a framework for observing and understanding infants' behavior. When parents know how to determine whether their infant is sleep, awake, or drowsy, and they know the implications, recognition of states has for both the infant's behavior and for their caregiving, then a lot of hings about taking care of a newborn become much easier and more rewarding. Most parents have the skills and desire to do what is best for their infant. The skills 7373parents bring to the interaction are: the ability to read their infant's cues: to stimulate the baby through touch, movement, talking, and looking at: and to respond in a contingent manner to the infant's signals. Among the crucial skills infants bring to the interaction are perceptual abilities: hearing and seeing, the capacity to look at another for a period of time, the ability to smile, be consoled, adapt their body to holding or movement, and be regular and predictable in responding. Research demonstrates that the absence of these skills by either partner adversely affects parent-infant interaction and later development. Observing early parent-infant interactions during the hospital stay is important in order to identify parent-infant pairs in need of continued monitoring(Barnard, et al., 1989).