Various physiological parameters have been widely used in the prevention and detection of diseases. In particular, the occurrence of cardiovascular diseases can be observed through daily measurement of blood pressure. Currently, the most common blood pressure measurement method records blood pressure on the upper arm. This can lead to the subject feeling uncomfortable and tension in the arm from the stress may lead to measurement errors. An electrocardiogram represents the electrical activity during heart function, but also contains blood pressure–related information. This study is an attempt to extract features related to blood pressure from the electrocardiogram signal using a new non-invasive blood pressure measurement technology that utilizes intelligent neural network algorithms to calculate blood pressure values from electrocardiogram parameters. In this study, the average error rate of the blood pressure measurement was lower than 5% compared to the common blood pressure machine. The proposed approach alleviates the errors caused by discomfort, which provides a more feasible method to continuously monitor blood pressure in less stressful conditions. This technology has significant potential for advancing healthcare.

Subjects with amyotrophic lateral sclerosis (ALS) consistently experience decreasing quality of life because of this distinctive disease. Thus, a practical brain-computer interface (BCI) application can effectively help subjects with ALS to participate in communication or entertainment. In this study, a fuzzy tracking and control algorithm is proposed for developing a BCI remote control system. To represent the characteristics of the measured electroencephalography (EEG) signals after visual stimulation, a fast Fourier transform is applied to extract the EEG features. A self-developed fuzzy tracking algorithm quickly traces the changes of EEG signals. The accuracy and stability of a BCI system can be greatly improved by using a fuzzy control algorithm. Fifteen subjects were asked to attend a performance test of this BCI system. The canonical correlation analysis (CCA) was adopted to compare the proposed approach, and the average recognition rates are 96.97% and 94.49% for proposed approach and CCA, respectively. The experimental results showed that the proposed approach is preferable to CCA. Overall, the proposed fuzzy tracking and control algorithm applied in the BCI system can profoundly help subjects with ALS to control air swimmer drone vehicles for entertainment purposes.

It is generally known that Morse code is an efficient input method for one or two switches and it is made from long and short sounds separated by silence between the sounds. The long-to-short ratio in the definition is always 3 to 1, but the long-to-short ratio variation for a disabled person is so large that it is difficult to recognize. In the last few years, several Morse code recognition methods have been successfully built on the LMS adaptive algorithms and neural network algorithm. But LMS-related adaptive algorithms need mass computation to infer the characteristic of the controller; also the neural network must learn first, by inputting some data before it is used to recognize the Morse code sequence. In this study, two fuzzy algorithms are used to recognize the unstable Morse code sequences and the result demonstrates a significant improvement of recognition for real time signal processing in a single-chip microprocessor.

For subjects with amyotrophic lateral sclerosis (ALS), the verbal and nonverbal communication is greatly impaired. Steady state visually evoked potential (SSVEP)-based brain computer interfaces (BCIs) is one of successful alternative augmentative communications to help subjects with ALS communicate with others or devices. For practical applications, the performance of SSVEP-based BCIs is severely reduced by the effects of noises. Therefore, developing robust SSVEP-based BCIs is very important to help subjects communicate with others or devices. In this study, a noise suppression-based feature extraction and deep neural network are proposed to develop a robust SSVEP-based BCI. To suppress the effects of noises, a denoising autoencoder is proposed to extract the denoising features. To obtain an acceptable recognition result for practical applications, the deep neural network is used to find the decision results of SSVEP-based BCIs. The experimental results showed that the proposed approaches can effectively suppress the effects of noises and the performance of SSVEP-based BCIs can be greatly improved. Besides, the deep neural network outperforms other approaches. Therefore, the proposed robust SSVEP-based BCI is very useful for practical applications.