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Prolonged lumbar loading, such as prolonged sitting and driving, will lead to muscle fatigue and even spinal disorders. However, commercial electrodes often struggle to stably record subtle EMG signals in lumbar region for the monitoring of lumbar health due to high interface impedance and rapid dehydration. As a result, clinical assessment still mainly relies on medical imaging after symptoms appear, which leads to delayed diagnosis. To address this challenge, this study proposed a flexible EMG sensor (FES) that consists of a body‐temperature‐responsive in situ gelation hydrogel and a flexible mesh electrode for stable recording of subtle EMG signals. With time‐frequency feature extraction and machine‐learning analysis, the FES achieves an accuracy of 81.67% in monitoring muscle fatigue based on the EMG signals. This approach provides a new strategy for continuous lumbar health monitoring and potential solution to early warning of musculoskeletal disorders. image