轻量级视频动作识别模型，适配边缘设备且可量化视觉退化影响。

Resource-constrained assistive monitoring requires compact local video perception and an explicit understanding of how recognition reliability changes under common visual degradation. We present a compact RGB-only action-recognition network that combines an X3D-style hierarchy with temporal shift, a 3D Universal Inverted Bottleneck, Ghost pointwise convolutions, factorized depthwise operators, selective temporal adaptation, and parameter-free attention. The model attains 95.10/98.31% on NTU RGB+D 60 (X-Sub/X-View) and 90.88/92.66% on NTU RGB+D 120 (X-Sub/X-Set) with only 0.96-0.99M parameters. To connect compact deployment with assistive use, we evaluate frozen checkpoints without retraining on the datasets' official Medical Conditions groups while retaining the original 60-/120-way decision spaces. Medical macro recall reaches 93.19/97.93% on NTU60 and 91.47/91.48% on NTU120. Under controlled Level-2 visual stress on NTU60 X-Sub, medical recall drops by 1.77 points under low light, 2.23 under motion blur, 4.87 under crop perturbation, and 19.05 under occlusion; among the four selected Level-2 perturbations, occlusion causes the largest observed degradation. On Jetson Orin Nano, the TensorRT FP16 engine occupies 5.30 MiB, demonstrating strong static compactness but not throughput superiority. The results position the model as a compact perception component for lower-rate assistive monitoring, rather than as a complete clinical or robotic system.