A5040254312- Phonocardiography and Auscultation Techniques
- Emotion and Mood Recognition
- Music and Audio Processing
- ECG Monitoring and Analysis
- Face and Expression Recognition
- Advanced Scientific and Engineering Studies
- Psychosomatic Disorders and Their Treatments
- Infant Health and Development
- Image and Signal Denoising Methods
- Hydraulic and Pneumatic Systems
- Flow Measurement and Analysis
- EEG and Brain-Computer Interfaces
- Advanced Sensor and Control Systems
- Speech and Audio Processing
- Time Series Analysis and Forecasting
- Face recognition and analysis
- Obstructive Sleep Apnea Research
- Innovative Energy Harvesting Technologies
- Modular Robots and Swarm Intelligence
- Machine Fault Diagnosis Techniques
Beijing Institute of Education
2023-2025
Beijing Institute of Technology
2013-2025
Sichuan Normal University
2022
Recent evidence have demonstrated that facial expressions could be a valid and important aspect for depression recognition.Although various works been achieved in automatic recognition, it is challenge to explore the inherent nuances of might reveal underlying differences between depressed patients healthy subjects under different stimuli.There lack an undisturbed system monitors depressive patients' mental states free-living scenarios, so this paper steps towards building classification...
Objective Speech recognition technology is widely used as a mature technical approach in many fields.In the study of depression recognition, speech signals are commonly due to their convenience and ease acquisition.Though popular research field it has been little studied somatisation disorder recognition.The reason for this lack publicly accessible database relevant benchmark studies.To end, we introduce our give results.Methods By collecting samples patients, cooperation with Shenzhen...
Automatic classification of heart sounds has been studied for many years, because computer-aided auscultation can help doctors make a preliminary diagnosis. We propose method that uses fractional Fourier transformation entropy (FRFE) as the features and support vector machine (SVM) model. The process whole is cutting sounds, feature extraction, classification. compare FRFE different signal orders, finally evaluate fused multiple orders according to better results. These are used input SVM,...
The development of affective computing and medical electronic technologies has led to the emergence Artificial Intelligence (AI)-based methods for early detection depression. However, previous studies have often overlooked necessity AI-assisted diagnosis system be wearable accessible in practical scenarios depression recognition. In this work, we present an on-board executable multi-feature transfer-enhanced fusion model our custom-designed three-lead Electroencephalogram (EEG) sensor, based...
Cardiovascular diseases (CVDs) are the leading cause of death globally. Heart sound signal analysis plays an important role in clinical detection and physical examination CVDs. In recent years, auxiliary diagnosis technology CVDs based on heart signals has become a research hotspot. The abnormal sounds can provide information to help doctors diagnose treat disease. We propose new set fractal features – dimension (FD) as representation for classification Support Vector Machine (SVM) model....
A low frequency energy harvesting structure was studied, describing the process of its manufacture. The natural obtained by modal analysis structure. It relationship between magnitudes voltage generated from piezoelectric device and size parameters structure, so that can operate at resonant in order to obtain maximum energy.