论文标题
使用LSTM复发神经网络对吸烟行为的状态过渡模型
State Transition Modeling of the Smoking Behavior using LSTM Recurrent Neural Networks
论文作者
论文摘要
传感器的使用遍布几种应用中的日常生活，包括人类活动监测，医疗保健和社交网络。在这项研究中，我们专注于使用智能手表传感器来识别吸烟活动。更具体地说，我们已经重新制定了以前检测吸烟的工作，以包括对吸烟的识别。我们将吸烟手势作为一种国家转变模型进行了重新制定，该模型由小型手势，手动唇部和唇部移交的小手势组成，显示出使用常规神经网络接近100％的检测率提高了检测率。此外，我们已经开始利用长期记忆（LSTM）神经网络，以允许在97％的准确性上对手势进行表面检测。
The use of sensors has pervaded everyday life in several applications including human activity monitoring, healthcare, and social networks. In this study, we focus on the use of smartwatch sensors to recognize smoking activity. More specifically, we have reformulated the previous work in detection of smoking to include in-context recognition of smoking. Our presented reformulation of the smoking gesture as a state-transition model that consists of the mini-gestures hand-to-lip, hand-on-lip, and hand-off-lip, has demonstrated improvement in detection rates nearing 100% using conventional neural networks. In addition, we have begun the utilization of Long-Short-Term Memory (LSTM) neural networks to allow for in-context detection of gestures with accuracy nearing 97%.
