A multiuser motion-monitoring system based on MEMS is proposed for fitness movement, it is used to monitor the three important parameters of movement type, movement times, and movement cycle in the body movement and supports the simultaneous use of multiple users. The specific content of the method: (1) In terms of system design, a motion-monitoring system framework based on the Internet of things is proposed considering the motion-monitoring scene oriented to intelligent fitness. (2) In the aspect of algorithm, the relevant research of motion pattern recognition and cycle calculation method is carried out. For action pattern recognition, SVM-based algorithm to adapt to different computing capabilities of the scene is applied. (3) Experiments on 7 kinds of actions show that the proposed deep neural network has a good learning effect on small datasets, the recognition accuracy of the proposed deep neural network reaches 97.61%, and the recognition accuracy of SVM also reaches over 96%. In the 50 times of operation cycle calculation experiments, the frequency statistics algorithm has reached 100% of the calculation accuracy, and the calculation results of the operation cycle are close to the real value, which proves the validity of the method of cycle calculation. The experiment proves that the zero-crossing detection and wavelet analysis methods have a good overall effect and can accurately count and calculate the period when the number of actions is more, improve fitness efficiency, and provide guarantee for human health.

To address the problem of image imbalance in the surface inspection of strip steel, this study proposes a novel anomaly detection method based on multi-scale knowledge distillation (Ms-KD) and a block domain core information module (BDCI) to quickly screen abnormal images. This method utilizes the multi-scale knowledge distillation technique to enable the student network to learn the ability to extract normal image information under the source network pre-trained on ImageNet. At the same time, the optimal storage of block-level features is used to extract low-level and high-level information from intermediate layers and establish a feature bank, which is searched for core subset libraries using a greedy nearest neighbor selection mechanism. By using the Ms-KD module, the student model can understand the abnormal data more comprehensively so as to better capture the information in the data to solve the imbalance of abnormal data. To verify the validity of the proposed method, a completely new dataset called strip steel anomaly detection for few-shot learning (SSAD-FSL) was constructed, which involved image-level and pixel-level annotations of surface defects on cold-rolled and hot-rolled strip steel. By comparing with other state-of-the-art methods, the proposed method performs well on image-level area under the receiver operating characteristic curve (AUROC), reaching a high level of 0.9868, and for pixel-level per region overlap (PRO) indicators, the method also achieves the best score of 0.9896. Through a large number of experiments, the effectiveness of our proposed method in strip steel defect anomaly detection is fully proven.