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

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

Objective To explore the nonlinear complexity characteristics of electroencephalogram (EEG) in ischemic stroke patients with different course. Methods Sample entropy of all bands of EEG signals in 20 ischemic stroke patients and 10 healthy controls was extracted and analyzed using statistical analysis methods. Results The full-band EEG in sample entropy of stroke patients was significantly lower than that of healthy controls in most locations. Theα-band sample entropy of different course had significant differences in the frontal, temporal and occipital lobe (P<0.05), and the parameters had significant negative linear correlation with the post-stroke time in some locations. Conclusions There is an abnormal neural electrical activity in post-stroke patients. It is feasible to detect the aberrant EEG complexity using sample entropy, which is worth of further research.

Objective To investigate the cognitive difference between uni-modal (V,A) and bi-modal (VA)target stimuli from both vision and audition,and then to study the neural mechanisms of bi-modal enhancement.Methods This experiment adopted a speeded target stimuli detection task, both behavioral and electroencephalographic responses to uni-modal and bi-modal target stimuli which were combined from visual and auditory target stimuli,were recorded from 14 normal subjects using a 64-channel EEG NeuroScan system.The differences of cognitive between uni-modal and bi-modal stimulus were tested from both behavioral (reaction time (RT) and error rate (ER)) and event-related potentials (ERPs) (P2 latency and amplitude,P3 latency and amplitude)data,and the correlation between behavioral and ERPs results were analyzed.Results As a result,the RT,ER and P3 latency has significant difference between uni-modal and bi-modal target stimuli.In addition,there were significant correlation between behavioral data and P3 latency,especially from the RT and P3 latency.Conclusion By comparing the difference between uni-modal and bi-modal from both behavioral and ERPs results,we could reached the conclusion that the neural mechanism of bi-modal target detection was predominant over that of vision and audition uni-modal target detection,the enhancement take place not only involved in early ERP components (such as P1 and N1),but engaged at the late ERP components (such as P2 and P3).

Brain computer interface (BCI) is an information channel independent of routine brain output ways such as peripheral nerves and muscle organization. As a special human-computer interface mode, it provides a direct communication pathway between the brain and external devices so as to exert control over those devices by ways other than primitive human communication. Controlling over mobile peripheral devices such as intelligent wheelchairs or nursing robots is a very important application of BCI technology in the future. This paper describes the newest progress of the above mentioned technology, analyzes and compares key techniques involved, and forecasts future development in this field.
Algorithms ; Brain Diseases ; rehabilitation ; Communication Aids for Disabled ; Computer Systems ; Electroencephalography ; instrumentation ; Evoked Potentials ; physiology ; Humans ; Neuromuscular Diseases ; physiopathology ; rehabilitation ; Signal Processing, Computer-Assisted ; instrumentation ; User-Computer Interface

Recognition by gait is a new field for the biometric recognition technology. Its aim is to recognize people and detect physiological, pathological and mental characters by their walk style. The use of gait as a biometric for human identification is promising. The technique of gait recognition, as an attractive research area of biomedical information detection, attracts more and more attention. In this paper is introduced a survey of the basic theory, existing gait recognition methods and potential prospects. The latest progress and key factors of research difficulties are analyzed, and future researches are envisaged.
Algorithms ; Artificial Intelligence ; Biometry ; methods ; Gait ; physiology ; Humans ; Models, Biological ; Pattern Recognition, Automated ; methods

This paper suggested a new method of spacial risk-trend trace (SRTT) to assess and monitor the spacial balance condition during paraplegic walking assisted by functional electrical stimulation (FES), which main component was a measurement system of upper limb support based on a standard walker. With the support data, the spacial positions of moving center of gravity could be located through the upper body mechanical model and, combining with the definition of walker rolling index, transmitted into SRTT to describe the balance conditions at different axial space. The experimental and clinical results demonstrated the new SRTT method was reliable and real-time. Its potential clinical usefulness in evaluating and monitoring FES-assisted paraplegic walking ability may provide the foundation to enact the relevant national rehabilitation criterions for effective FES usage.
Electric Stimulation Therapy ; instrumentation ; methods ; Humans ; Monitoring, Physiologic ; methods ; Paraplegia ; rehabilitation ; Postural Balance ; physiology ; Self-Help Devices ; Therapy, Computer-Assisted ; methods ; Walking ; physiology

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

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

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