Yu Hang, Kotlyar Michael, Thuras Paul, Dufresne Sheena, Pakhomov Serguei Vs
University of Minnesota, Minneapolis, MN, United States.
AMIA Jt Summits Transl Sci Proc. 2024 May 31;2024:468-477. eCollection 2024.
Consumer-grade heart rate (HR) sensors are widely used for tracking physical and mental health status. We explore the feasibility of using Polar H10 electrocardiogram (ECG) sensor to detect and predict cigarette smoking events in naturalistic settings with several machine learning approaches. We have collected and analyzed data for 28 participants observed over a two-week period. We found that using bidirectional long short-term memory (BiLSTM) with ECG-derived and GPS location input features yielded the highest mean accuracy of 69% for smoking event detection. For predicting smoking events, the highest accuracy of 67% was achieved using the fine-tuned LSTM approach. We also found a significant correlation between accuracy and the number of smoking events available from each participant. Our findings indicate that both detection and prediction of smoking events are feasible but require an individualized approach to training the models, particularly for prediction.
消费级心率（HR）传感器被广泛用于追踪身心健康状况。我们运用多种机器学习方法，探索使用 Polar H10 心电图（ECG）传感器在自然环境中检测和预测吸烟事件的可行性。我们收集并分析了 28 名参与者在两周时间内的观察数据。我们发现，将双向长短期记忆（BiLSTM）与源自心电图的输入特征和全球定位系统（GPS）位置相结合，在吸烟事件检测方面的平均准确率最高，达到了 69%。对于吸烟事件的预测，使用微调后的长短期记忆（LSTM）方法实现了 67%的最高准确率。我们还发现准确率与每个参与者的吸烟事件数量之间存在显著相关性。我们的研究结果表明，吸烟事件的检测和预测都是可行的，但需要采用个性化方法来训练模型，尤其是在预测方面。
