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手机驾驶检测的深度学习

Deep learning for driving detection on mobile phones
课程网址: http://videolectures.net/kdd2016_tran_mobile_phones/  
主讲教师: Allen Tran
开课单位: Metromile股份有限公司
开课时间: 2016-10-12
课程语种: 英语
中文简介:
基于传感器的活动识别是基于移动电话的应用程序的关键组成部分,旨在进行驾驶检测。目前的方法包括将人工设计的特征输入到判别模型中,迄今为止的实验仅限于对O(10)用户的小规模研究。在这里,我们展示了卷积神经网络如何用于从从手机加速计和陀螺仪收集的原始和频谱图传感器时间序列中学习特征。虽然在有限的训练数据下,这种方法的性能低于现有模型,但我们表明,当训练数据集大小足够大时,卷积神经网络优于当前使用的判别模型。我们还测试了在Android平台上实现的模型的性能,并使用从2000多名手机用户收集的传感器数据验证了我们的方法。
课程简介: Sensor based activity recognition is a critical component of mobile phone based applications aimed at driving detection. Current methodologies consist of hand-engineered features input into discriminative models, and experiments to date have been restricted to small scale studies of O(10) users. Here we show how convolutional neural networks can be used to learn features from raw and spectrogram sensor time series collected from the phone accelerometer and gyroscope. While with limited training data such an approach under performs existing models, we show that convolutional neural networks outperform currently used discriminative models when the training dataset size is sufficiently large. We also test performance of the model implemented on the Android platform and we validate our methodology using sensor data collected from over 2000 mobile phone users.
关 键 词: 活动识别; 应用程序; 判别模型
课程来源: 视频讲座网
数据采集: 2022-12-20:chenjy
最后编审: 2023-05-11:chenjy
阅读次数: 12