Freezing of Gait (FoG) is a common condition in patients with Parkinson's disease (PD). It often leads to falls, and it severely affects the patient's quality of life. Although the neural mechanism of FoG is not well-known, wearable sensorbased assistive systems have been shown to effectively monitor FoG and help patients resume walking through rhythmic auditory cues when FoG is detected in real-time. With the development of technologies based on wearable sensors, accurate detection of FoG events is important for resume walking, clinical diagnosis, and treatment. Here, we propose a deep residual network to detect FoG. Offline analysis performed on a publicly available dataset with 10 patients shows the superiority of the proposed approach compared to traditional method (Moore's algorithm) and several deep learning techniques. Under is window size, the proposed method can achieve 85.7% sensitivity and 94.0% specificity. The geometric mean of the proposed method is 37.4% ahead of Moore's algorithm. Our approach can help improve the patients with PD quality of life and evaluate symptoms of FoG.