This paper introduces a new method which can judge the degree of burn scar hypertrophy by analyzing chroma of the burn scar. Its technical schedule is as follows: Firstly, the image of the burn scar is captured by using a digital camera. Then the chroma emendation is performed by using an Artificial Neural Network(ANN). At last, the chroma of burn scar is analyzed and the classification of burn scar hypertrophy is given by using a Support Vector Machine(SVM). Compared with clinical evaluation, the result deduced from this method is proved to be effective.

Emotion recognition will be prosperious in multifarious applications, like distance education, healthcare, and human-computer interactions, etc. Emotions can be recognized from the behavior signals such as speech, facial expressions, gestures or the physiological signals such as electroencephalogram and electrocardiogram. Contrast to other methods, the physiological signals based emotion recognition can achieve more objective and effective results because it is almost impossible to be disguised. This paper introduces recent advancements in emotion research using physiological signals, specified to its emotion model, elicitation stimuli, feature extraction and classification methods. Finally the paper also discusses some research challenges and future developments.
Electrocardiography ; Electroencephalography ; Emotions ; physiology ; Facial Expression ; Gestures ; Humans ; Models, Theoretical

Brain Computer Interface(BCI) is a direct information communication and control channel established between human and computer or other electronics devices and it is a wholly new communication system that does not depend on the brain's normal output pathways of peripheral nerves and muscles.The general constitutions and principles of BCI systems are introduced.In addition,research methods based on electroencephalograph are discussed and the existing problems and future trends of BCI are pointed out.

A system design of brain-computer interface based on the alpha waves in human electroencephalography(EEG) is presented in this paper.With the effects on the alpha wave amplitudes of human eye's open and close involved in,the selection control of four direction targets can be performed on a computer screen.The system speed and accuracy rate are investigated through the experiments involving 5 subjects.It is shown that the system is easy to operate and needs no complex learning and biofeedback training.The studying results provide a good technical foundation for the development of BCI control panel and the realization of the system integration.It has the potential application for clinical engineering and is valuable for further research.

A new method is developed to detect vascular edge-line in coronary angiograms.It firstly searches the path through vessel segment of interest,then detects vascular edges along profiles perpendicular to the path.The experiments demonstrate that the method performs well in coronary angiograms with less nearby or overlapping structures and branching vessels.

Objective To explore a new method for assessing the walking efficiency of the paraplegic patients assisted by functional electrical stimulation (FES). Methods The measurement system based on a standard walker was developed. During FES assisted walking of the paraplegic, the real time of the upper limb support were obtained and transformed into a 3 D center of gravity (CG) motion map with a paraplegic upper body mechanical model to describe the CG motion locus. Then the FES efficiency indicated by walking balance condition was assessed objectively and quantitatively. Results In this design, the pilot study of a paraplegic patient undergoing walking training with FES showed that the force accuracy was better than 1.01%, nonlinearity was less than 0.8%, and crosstalk was less than 3.2%. Conclusion The results showed that this system may be used as 1) an evaluation index of FES assisted paraplegic walking efficiency, 2) a balance control indicator during FES assisted paraplegic walking training and 3) a feed back signal to choose an efficient FES pattern and sequence.

Acoustic analysis is one of the important branches of biometric recognition technology widely used now. The mainly aim of the technology is to recognize the identity of person and judge the content of speech or diagnose the illness automatically according to the features extracted from the speaker's waveforms. All these features are related with the characteristics of speaker's physiological, pathological and psychological action. Speaker recognition study has its 50-year old history already, but acoustic analysis in diagnosing disease has been founded since 1970s. This paper introduces the main concept and research background of this diagnosing system generally and discusses the problems generated during processing. At last the prospect for the applications of acoustic analysis is forecasted.
Humans ; Pattern Recognition, Physiological ; Signal Detection, Psychological ; Speech ; physiology ; Speech Acoustics ; Speech Disorders ; diagnosis ; physiopathology

In order to preprocess mammograms for diagnosing the early cases of breast cancer and improving the computational efficiency in the computer-aided detection of micro-calcifications in mammograms, we have advanced a novel processing technique for the extraction of micro-calcification region of interest (MROI). The proposed method is based on a three-step procedure: (1) the mammogram is divided into sub-images of the same size; (2) the wavelet multi-resolution method is conducted on the sub-images, and the parameters related to wavelet transform and threshold T are discussed according to rho; (3) the classification of sub-images is determined by T. It is tested with 20 mammograms and the results show that the method can achieve a true positive rate as high as 89.7% with a false positive rate as low as 2.1%.
Breast Diseases ; diagnostic imaging ; pathology ; Breast Neoplasms ; diagnostic imaging ; pathology ; Calcinosis ; diagnostic imaging ; Diagnosis, Computer-Assisted ; Humans ; Mammography

Paraplegia is a severe disability of lower limbs resulting from spinal cord injury. Moreover, its incidence has been climbing over the recent years. The most important symptom of paraplegia is the loss of walking ability. Involved researches during recent 40 years have shown that functional electrical stimulation, which could successfully regain some movement function for paraplegic patients, is a new and promising technique in modern rehabilitation engineering. It has been drawing more attention. Here, aiming at this functional electrical stimulation technique for paraplegic walking, we introduce its relevant background knowledge and research progress.
Electric Stimulation Therapy ; instrumentation ; trends ; Humans ; Paraplegia ; rehabilitation ; Walking

Fatigue is an exhaustion state caused by prolonged physical work and mental work, which can reduce working efficiency and even cause industrial accidents. Fatigue is a complex concept involving both physiological and psychological factors. Fatigue can cause a decline of concentration and work performance and induce chronic diseases. Prolonged fatigue may endanger life safety. In most of the scenarios, physical and mental workloads co-lead operator into fatigue state. Thus, it is very important to study the interaction influence and its neural mechanisms between physical and mental fatigues. This paper introduces recent progresses on the interaction effects and discusses some research challenges and future development directions. It is believed that mutual influence between physical fatigue and mental fatigue may occur in the central nervous system. Revealing the basal ganglia function and dopamine release may be important to explore the neural mechanisms between physical fatigue and mental fatigue. Future effort is to optimize fatigue models, to evaluate parameters and to explore the neural mechanisms so as to provide scientific basis and theoretical guidance for complex task designs and fatigue monitoring.
Attention ; Brain ; physiology ; Fatigue ; Humans ; Mental Fatigue ; Workload