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引用次数: 0

摘要

尽管在使用机器学习算法从加速度计和其他传感器数据中对用户活动(例如,步行,骑自行车)进行分类方面已经有了大量的工作,但很少有人关注实时中继这些信息以进行远程隐式通信。人类非常熟悉与身边的人进行显性和隐性的交流,例如,通过使用肢体语言和调节声音的音调和音量。同样,远程显式通信也以电话、文本消息和其他远距离显式通信机制的形式存在了很长时间。然而,手机用户之间的远程隐式通信很少被探索,可能是因为在我们的日常生活中,收集这些信息并以有意义的方式呈现这些信息的途径有限。
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Body-worn sensors for remote implicit communication
Although there has been a great deal of work on using machine learning algorithms to categorize user activity (e.g., walking, biking) from accelerometer and other sensor data, less attention has been focused on relaying such information in real-time for remote implicit communication. Humans are very familiar with using both explicit and implicit communication with others physically near to us, for example, by using body language and modulating our voice tone and volume. Likewise, remote explicit communication has also existed for a long time in the form of phone calls, text messages, and other mechanisms for communicating explicitly over great distances. However, remote implicit communication between mobile users has been less explored, likely since there have been limited avenues for collecting such information and rendering it in a meaningful way while we go about our daily lives.
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