Freezing of Gait (FOG) is a common motor symptom in patients with Parkinson's disease (PD). During episodes of FOG, patients suddenly lose their ability to stride as intended. Patient-worn accelerometers can capture information on the patient's movement during these episodes and machine learning algorithms can potentially classify this data. The combination therefore holds the potential to detect FOG in real-time. In this work I present a simple 1-D convolutional neural network that was trained to detect FOG events in accelerometer data. Model performance was assessed by measuring the success of the model to discriminate normal movement from FOG episodes and resulted in a mean average precision of 0.356 on the private leaderboard on Kaggle. Ultimately, the model ranked 8th out of 1379 teams in the Parkinson's Freezing of Gait Prediction competition. The results underscore the potential of Deep Learning-based solutions in advancing the field of FOG detection, contributing to improved interventions and management strategies for PD patients.

翻译：冻结步态（FOG）是帕金森病（PD）患者常见的运动症状。在FOG发作期间，患者会突然丧失正常迈步能力。患者佩戴的加速度计可捕获发作期间的肢体运动信息，而机器学习算法则能对此类数据进行分类。因此，两者的结合具有实时监测FOG的潜力。本文提出一个简单的一维卷积神经网络，用于训练检测加速度计数据中的FOG事件。模型性能通过区分正常运动与FOG发作的成功率进行评估，在Kaggle非公开排行榜上获得0.356的平均精度均值。最终，该模型在帕金森病冻结步态预测竞赛的1379支参赛队伍中排名第八。研究结果凸显了基于深度学习的解决方案在推进FOG检测领域的潜力，有助于改善PD患者的干预措施与疾病管理策略。