Gait recognition is a new technology in biometric recognition and medical treatment which has advantages such as long-distance and non-invasiveness. Depending on the differences between different people's walking postures, we can recognize individuals by characteristics extracted from the images of walking movement. A complete gait recognition process usually includes gait sequence acquisition, gait detection, feature extracting and recognition. In this paper, the commonly used methods of these four processes are introduced, and feature extraction methods are classified from different multi-angle views. And then the new algorithm of multi-view emerged in recent years is highlighted. In addition, this paper summarizes the existing difficulties of gait recognition, and looks into the future development trends of it.
The classification of lung tumor with the help of computer-aided diagnosis system is very important for the early diagnosis and treatment of malignant lung tumors. At present, the main research direction of lung tumor classification is the model fusion technology based on deep learning, which classifies the multiple fusion data of lung tumor with the help of radiomics. This paper summarizes the commonly used research algorithms for lung tumor classification, introduces concepts and technologies of machine learning, radiomics, deep learning and multiple data fusion, points out the existing problems and difficulties in the field of lung tumor classification, and looks forward to the development prospect and future research direction of lung tumor classification.
To achieve non-contact measurement of human heart rate and improve its accuracy, this paper proposes a method for measuring human heart rate based on multi-channel radar data fusion. The radar data were firstly extracted by human body position identification, phase extraction and unwinding, phase difference, band-pass filtering optimized by power spectrum entropy, and fast independent component analysis for each channel data. After overlaying and fusing the four-channel data, the heartbeat signal was separated using frost-optimized variational modal decomposition. Finally, a chirp Z-transform was introduced for heart rate estimation. After validation with 40 sets of data, the average root mean square error of the proposed method was 2.35 beats per minute, with an average error rate of 2.39%, a Pearson correlation coefficient of 0.97, a confidence interval of [–4.78, 4.78] beats per minute, and a consistency error of –0.04. The experimental results show that the proposed measurement method performs well in terms of accuracy, correlation, and consistency, enabling precise measurement of human heart rate.