Robot-aided training strategies that allow functional, assist-as-needed, or challenging training have been widely explored. Accurate activity recognition is the basis for implementing alternative training strategies. However, some obstacles to accurate recognition exist. First, scientists do not yet fully understand some rehabilitation activities, such as abnormal gaits and falls; thus, there is no standardized feature for identifying such activities. Second, during the activity identification process, it is difficult to reasonably balance sensitivity and specificity when setting the threshold. Therefore, we proposed a multisensor fusion system and a two-stage activity recognition classifier. This multisensor system integrates explicit information such as kinematics and spatial distribution information along with implicit information such as kinetics and pulse information. Both the explicit and implicit information are analyzed in one discriminant function to obtain a detailed and accurate recognition result. Then, alternative training strategies can be implemented on this basis. Finally, we conducted experiments to verify the feasibility and efficiency of the multisensor fusion system. The experimental results show that the proposed fusion system achieves an accuracy of 99.37%, and the time required to prejudge a fall is approximately 205 milliseconds faster than the response time of single-sensor systems. Moreover, the proposed system also identifies fall directions and abnormal gait types.

To address the challenge of detecting tiny flaws in tile defect detection, a lightweight algorithm for identifying minor defects in tile images has been developed, referred to as CTDD-YOLO. Firstly, CAACSPELAN is proposed as the core component of the backbone network for extracting features of tile defects; secondly, full-dimensional dynamic convolution ODConv is introduced at the end of the backbone network to enhance the model’s ability to deal with tiny defects; next, a new neck network, CGRFPN, is proposed to improve the model’s ability to represent multi-scale features and enhance the model’s ability to recognize small targets in the context of large formats; finally, MPNWD is proposed to optimize the loss function to improve the model’s detection accuracy further. Experiments on the Ali Tianchi tile defect detection dataset show that the CTDD-YOLO model not only has a lower number of parameters than the original YOLOv8n but also improves the mAP by 7.2 percentage points, i.e., the proposed model can more accurately recognize and deal with minor surface defects of tiles and can significantly improve the detection effect while maintaining the light weight.

PCBs (printed circuit boards) are the core components of modern electronic devices, and inspecting them for defects will have a direct impact on the performance, reliability and cost of the product. However, the performance of current detection algorithms in identifying minor PCB defects (e.g., mouse bite and spur) still requires improvement. This paper presents the MDD-DETR algorithm for detecting minor defects in PCBs. The backbone network, MDDNet, is used to efficiently extract features while significantly reducing the number of parameters. Simultaneously, the HiLo attention mechanism captures both high- and low-frequency features, transmitting a broader range of gradient information to the neck. Additionally, the proposed SOEP neck network effectively fuses scale features, particularly those rich in small targets, while INM-IoU loss function optimization enables more effective distinction between defects and background, further improving detection accuracy. Experimental results on the PCB_DATASET show that MDD-DETR achieves a 99.3% mAP, outperforming RT-DETR by 2.0% and reducing parameters by 32.3%, thus effectively addressing the challenges of detecting minor PCB defects.