As the intensity of work increases, many of us sit for long hours while working in the office. It is not easy to sit properly at work all the time and sitting for a long time with wrong postures may cause a series of health problems as time goes by. In addition, monitoring the sitting posture of patients with spinal disease would be beneficial for their recovery. Accordingly, this paper designs and implements a sitting posture recognition system from a flexible array pressure sensor, which is used to acquire pressure distribution map of sitting hips in a real-time manner. Moreover, an improved self-organizing map-based classification algorithm for six kinds of sitting posture recognition is proposed to identify whether the current sitting posture is appropriate. The extensive experimental results verify that the performance of ISOM-based sitting posture recognition algorithm (ISOM-SPR) in short outperforms that of four kinds of traditional algorithms including decision tree-based (DT), K-means-based (KM), back propagation neural network-based (BP), self-organizing map-based (SOM) sitting posture recognition algorithms. Finally, it is proven that the proposed system based on ISOM-SPR algorithm has good robustness and high accuracy.

As we all know, target detection and tracking are of great significance for marine exploration and protection. In this paper, we propose one Convolutional-Neural-Network-based target detection method named YOLO-Softer NMS for long-strip target detection on the water, which combines You Only Look Once (YOLO) and Softer NMS algorithms to improve detection accuracy. The traditional YOLO network structure is improved, the prediction scale is increased from threeto four, and a softer NMS strategy is used to select the original output of the original YOLO method. The performance improvement is compared totheFaster-RCNN algorithm and traditional YOLO methodin both mAP and speed, and the proposed YOLO–Softer NMS’s mAP reaches 97.09%while still maintaining the same speed as YOLOv3. In addition, the camera imaging model is used to obtain accurate target coordinate information for target tracking. Finally, using the dicyclic loop PID control diagram, the Autonomous Surface Vehicle is controlled to approach the long-strip target with near-optimal path design. The actual test results verify that our long-strip target detection and tracking method can achieve gratifying long-strip target detection and tracking results.