Falls among the elderly and vulnerable populations represent a critical public health challenge, and camera-based visual sensor systems have emerged as a promising non-intrusive solution for continuous fall monitoring. However, deploying accurate fall detection on resource-constrained edge sensor nodes remains difficult due to the trade-off between model complexity and detection performance. In this paper, we propose WAD-YOLO, an efficient and lightweight fall detection algorithm tailored for visual sensor systems, based on wavelet transform and dynamic convolution. First, a wavelet transform convolution (WTConv) module is introduced to expand the receptive field of the visual feature extractor via cascaded wavelet decomposition, enabling the sensor-driven model to better capture low-frequency fall-related patterns without parameter explosion. Second, a dynamic upsample (DySample) operator is incorporated into the detection head to achieve content-aware, flexible upsampling by generating dynamic offsets, maintaining high efficiency suitable for real-time sensor data processing. Third, an adaptive downsampling (ADown) module is integrated to reduce spatial resolution while preserving semantic information, further reducing the computational burden for deployment on embedded sensor platforms. Experiments on the public Fall Detection dataset demonstrate that, compared with the baseline YOLOv11n, the proposed method increases precision P by 3.8%, mAP50 by 3.7%, and reduces the parameter count by 3.0 × 10⁵. The reduced parameter count and matched GFLOPs relative to YOLOv11n suggest that WAD-YOLO is a theoretically promising candidate for lightweight, high-accuracy fall detection on edge sensor platforms.