Xiangping Gao

Bio: Xiangping Gao is an academic researcher from Anhui University. The author has contributed to research in topics: Electrooculography & Saccadic masking. The author has an hindex of 5, co-authored 8 publications receiving 59 citations.

Design and Implementation of a Reading Auxiliary Apparatus Based on Electrooculography

Abstract: Eye movements have been proven the most frequent of all human activities; therefore, research on a relationship between different eye movement patterns become a hotspot in human–computer interface fields. The motivation of this paper is to develop a reading auxiliary apparatus by measuring and analyzing the electrooculography signals. We first describe the saccade detection algorithm based on the wavelet packet decomposition and the derivation blink detection algorithm. Furthermore, consecutive blinks were used to control the system’s working state and a magnifier, whose position is adjusted according to the results of saccade detection. Experiential results on six participants show that recognition accuracy ratio ( F1 score ) is 90.096%, which reveal that the proposed system has a good recognition performance on reading activity detection and analysis.

Design and implementation of an eye gesture perception system based on electrooculography

Abstract: People with motor diseases have suffered from deprivation of both verbal and non-verbal communication abilities. Fortunately, some of them still retain coordination of brain and eye-motor. To establish a stable communication way for these disabled people, this paper presents an eye gesture perception system based on Electrooculography (EOG). In order to implement a high-accuracy of unit saccadic EOG signals recognition, we propose a new feature extraction algorithm based on Common Spatial Pattern (CSP). We first establish a CSP spatial filter bank corresponding to 8 saccadic tasks (i.e., up, down, left, right, right-up, left-up, right-down, and left-down), then use it to linearly project raw EOG signals and treat the outputs as feature parameters. Furthermore, eye gestures recognition has been carried out by identifying and merging unit saccadic segments in terms of pre-defined time sequences. Experiential results over 10 subjects show that the recognition precision of unit saccadic EOG and eye gesture are 96.8% and 95.0% respectively, which reveal the proposed system has a good performance of eye gestures perception.

Information interaction system and information interaction method for combining brain electricity and eye electricity

Abstract: The invention discloses an information interaction system and an information interaction method for combining brain electricity and eye electricity. A wireless communication module of the information interaction system comprises a plurality of Zigbee communication terminals, wherein one of the Zigbee communication terminals serves as a network coordinator; each information interaction subterminal comprises an information fusion module and a serial communication module, is connected with one of the Zigbee communication terminals by the serial communication module of each information interaction subterminal, and is connected with an eye electricity and brain electricity signal acquisition module; and the eye electricity and brain electricity signal acquisition modules acquire eye electricity signals and brain electricity signals of a user, amplify the acquired eye electricity signals and the acquired brain electricity signals, and send the eye electricity signals and the brain electricity signals to the information fusion modules. The information interaction system and the information interaction method for combining the brain electricity and the eye electricity are in a non-verbal and non-body brand-new information interaction mode, and have the advantages of wide application scope, high expansibility, comfortability in use, good interactivity, high robustness and the like.

A Robust Online Saccadic Eye Movement Recognition Method Combining Electrooculography and Video

Abstract: Eye movement is proven to be the most frequent activities of human beings; as a result research on recognition of unit eye movement has become a hotspot in human activity recognition. In this paper, we propose a robust online saccade recognition algorithm, which integrates electrooculography (EOG) and video together. Initially, EOG signals and video data are collected simultaneously from eight saccadic directions. Then online active eye movement segment detection algorithm is developed to detect the effective saccadic signal from ongoing eyeball activities. Furthermore, we extract features from different modalities and explore two fusion strategies [i.e., feature level fusion (FLF) and decision level fusion (DLF)]. In laboratory environment, the average recognition accuracy of FLF and DLF achieves 89.37% and 89.96%, respectively, which reveals that the proposed method can improve the performance of consecutive saccade recognition in comparison with sole modality.

A study on medical Internet of Things and Big Data in personalized healthcare system

Abstract: Personalized healthcare systems deliver e-health services to fulfill the medical and assistive needs of the aging population Internet of Things (IoT) is a significant advancement in the Big Data era, which supports many real-time engineering applications through enhanced services Analytics over data streams from IoT has become a source of user data for the healthcare systems to discover new information, predict early detection, and makes decision over the critical situation for the improvement of the quality of life In this paper, we have made a detailed study on the recent emerging technologies in the personalized healthcare systems with the focus towards cloud computing, fog computing, Big Data analytics, IoT and mobile based applications We have analyzed the challenges in designing a better healthcare system to make early detection and diagnosis of diseases and discussed the possible solutions while providing e-health services in secure manner This paper poses a light on the rapidly growing needs of the better healthcare systems in real-time and provides possible future work guidelines

A Review on Human-Computer Interaction and Intelligent Robots

Abstract: In the field of artificial intelligence, human–computer interaction (HCI) technology and its related intelligent robot technologies are essential and interesting contents of research. From the perspective of software algorithm and hardware system, these above-mentioned technologies study and try to build a natural HCI environment. The purpose of this research is to provide an overview of HCI and intelligent robots. This research highlights the existing technologies of listening, speaking, reading, writing, and other senses, which are widely used in human interaction. Based on these same technologies, this research introduces some intelligent robot systems and platforms. This paper also forecasts some vital challenges of researching HCI and intelligent robots. The authors hope that this work will help researchers in the field to acquire the necessary information and technologies to further conduct more advanced research.

Sleep period separating method and system

Abstract: The invention discloses a sleep period separating method and system. The method is applied to a sleep period separating system. The system comprises a first measuring electrode, a second measuring electrode, a first reference electrode, a second reference electrode, an amplification collector and a period separating device. The first measuring electrode is used for being placed on the left side of the forehead or the temporal hair-free region on the left side of a testee, the second measuring electrode is used for being placed on the right side of the forehead or the temporal hair-free region on the right side of the testee, the first reference electrode is used for being placed on the periphery of the left ear of the testee, and the second reference electrode is used for being placed on the periphery of the right ear of the testee. The method comprises the steps that the first measuring electrode and the second measuring electrode are utilized for collecting electroencephalogram signals and electro-oculogram signals of the testee, the signal amplification collector is used for amplifying the electroencephalogram signals and the electro-oculogram signals and carrying out analog-digital conversion on the amplified signals. The period separating device determines the separated sleep periods according to the analog-digital converted electroencephalogram signals and electro-oculogram signals.

Offline Analysis for Designing Electrooculogram Based Human Computer Interface Control for Paralyzed Patients

Abstract: Currently, majority of persons were immobilized and need aid from caretakers due to disability. To reduce and overcome such problem, there was a need for developing human–computer interface (HCI) with the help of biosignals. In this paper, we propose a two-channel elecctrooculograpy (EOG)-based HCI to encourage the contact ability as well as value of life for paralyzed persons who cannot speak or shift their extremity by using 20 subjects with the help of ADT26 Bio amplifier. EOG signals were collected for 11 tasks from both vertical and horizontal eye movement by using gold-platted electrodes. The extracted EOG signals were processed with convolution and Plancherel theorem to obtain the features. Layered recurrent neural network (LRNN) was implemented to analyze the extracted features and then converted into a sequence of commands to control the HCI. A graphical user interface was developed using MATLAB to help a user to convey their thoughts. This paper shows an average classification accuracy of 90.72% for convolution features and 91.28% for Plancherel features. Off-line single trail analysis was also performed to analyze the recognition accuracy of the proposed HCI system. The off-line analysis displayed that Plancherel features using LRNN were high compared to convolution features using LRNN. From this paper, we found that LRNN architecture using Plancherel features was more suitable for developing EOG-based HCI. Single trail analysis was conducted to identify the recognizing accuracy in offline. The off-line results indicated that in comparison with other EOG-based HCI systems, our system was user friendly and needs minimum training to operate.