Focused on the existing diagnosis methods of Parkinson's Disease （PD）， the auxiliary diagnosis methods of PD based on gait analysis was reviewed. In clinical practice， the common diagnosis method of gait assessment for PD is based on scales， which is simple and convenient， but is highly subjective and requires well-experienced clinical doctors. With the development of computer technology， more methods of gait analysis are provided. Firstly， PD and its abnormal manifestations in gait were summarized. Then， the common methods of auxiliary diagnosis for PD based on gait analysis were reviewed. These methods were able to be roughly divided into two types： methods based on wearable or non-wearable devices. Wearable devices are small and have high accuracy for diagnosis， and with the use of them， the gait status of patients can be monitored for a long time. With the use of non-wearable devices， human gait data is captured through video sensors such as Microsoft Kinect， without wearing related devices and restricting patients' movements. Finally， the deficiencies in the existing gait analysis methods were pointed out， and the possible development trends in the future were discussed.

Convolutional Neural Network （CNN） is one of the important research directions in the field of computer vision based on deep learning at present. It performs well in applications such as image classification and segmentation， target detection. Its powerful feature learning and feature representation capability are admired by researchers increasingly. However， CNN still has problems such as incomplete feature extraction and overfitting of sample training. Aiming at these issues， the development of CNN， classical CNN network models and their components were introduced， and the methods to solve the above issues were provided. By reviewing the current status of research on CNN models in image classification， the suggestions were provided for further development and research directions of CNN.

In view of the difficulty of traditional mechanical fault diagnosis methods to solve the problem of the uncertainty of manual extraction， a large number of deep learning feature extraction methods have been proposed， which greatly promotes the development of mechanical fault diagnosis. As a typical representative of deep learning， convolution neural networks have made significant developments in image classification， target detection， image semantic segmentation and other fields. There is also a lot of literature in the field of mechanical fault diagnosis. In view of the published literature， in order to further understand the problem of mechanical fault diagnosis by using the method of convolutional neural network， on the basis of a brief introduction to the relevant theories of convolution neural network， and then from the aspects such as data input type， transfer learning， and prediction， the applications of convolution neural network in mechanical fault diagnosis were summarized. Finally， the development directions of convolution neural network and its applications in mechanical fault diagnosis were prospected.

Wearable ElectroEncephaloGram （EEG） device is a wireless EEG system to daily real-time monitoring. It is developed rapidly and widely applied because of its portability， real-time performance， non-invasiveness， and low-cost advantages. This system is mainly composed of hardware parts such as signal acquisition module， signal processing module， micro-control module， communication module and power supply module， and software parts such as mobile terminal module and cloud storage module. The key technologies of wearable EEG devices were discussed. First， the improvement of EEG signal acquisition module was explained. In addition， the comparisons of wearable EEG device signal preprocessing module， signal noise reduction， artifact processing and feature extraction technology were performed. Then， the advantages and disadvantages of machine learning and deep learning classification algorithms were analyzed， and the application fields of wearable EEG device were summarized. Finally， future development trends of the key technologies of wearable EEG device were proposed.