标题： 使用智能手表技术识别吸烟动作 显示英文标题

标题： Recognition of Smoking Gesture Using Smart Watch Technology

摘要： 由长期吸烟引起的疾病是当今世界上最常见的可预防死亡原因。 在本报告中，我们研究了利用智能手表中的加速度计传感器来识别吸烟动作的效果。 早期识别吸烟动作有助于启动适当的干预方法并防止戒烟复发。 我们的实验表明，使用人工神经网络（ANNs）在其他类似动作中识别吸烟动作的成功率为85%至95%。 我们的研究得出结论，从加速度计的x维度获得的信息是识别吸烟动作的最佳方法，而y和z维度有助于排除其他动作，例如：进食、饮水和抓鼻子。 我们在训练ANN时使用了Apple Watch的传感器数据。 使用从Pebble Steel收集的另一名参与者的传感器数据，当使用之前从Apple Watch收集的数据训练的ANN时，我们获得了超过90%的吸烟识别准确率。 最后，我们展示了使用智能手表对日常活动进行连续监测的可能性。 显示英文摘要

摘要： Diseases resulting from prolonged smoking are the most common preventable causes of death in the world today. In this report we investigate the success of utilizing accelerometer sensors in smart watches to identify smoking gestures. Early identification of smoking gestures can help to initiate the appropriate intervention method and prevent relapses in smoking. Our experiments indicate 85%-95% success rates in identification of smoking gesture among other similar gestures using Artificial Neural Networks (ANNs). Our investigations concluded that information obtained from the x-dimension of accelerometers is the best means of identifying the smoking gesture, while y and z dimensions are helpful in eliminating other gestures such as: eating, drinking, and scratch of nose. We utilized sensor data from the Apple Watch during the training of the ANN. Using sensor data from another participant collected on Pebble Steel, we obtained a smoking identification accuracy of greater than 90% when using an ANN trained on data previously collected from the Apple Watch. Finally, we have demonstrated the possibility of using smart watches to perform continuous monitoring of daily activities.