Predicting Sensory Data and Extending Battery Life for Wearable Devices

Songchun Fan, Qiuyun Llull, Benjamin C. Lee
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Abstract

Telepath is a framework that supports communication-free offloading for wearable devices. With offline training, activity recognition tasks can be offloaded from the wearable to the user's phone, without transferring raw sensing data. The key observation is that when the user is carrying both devices, the sensing streams on the two devices are highly correlated. By exploiting the correlation, the phone can estimate the wearable's sensing data and emulate the watch. Our evaluations shows that with Telepath, the phone performs accurately on activity recognition tasks that are designed for smart watches, achieving on average 87% of the watch's accuracy while extending the watch's battery life by 2.1x.
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预测传感数据和延长可穿戴设备的电池寿命
Telepath是一个支持可穿戴设备无通信卸载的框架。通过离线训练,活动识别任务可以从可穿戴设备转移到用户的手机上,而无需传输原始传感数据。关键的观察是,当用户同时携带两个设备时,两个设备上的传感流是高度相关的。通过利用这种相关性,手机可以估计可穿戴设备的传感数据并模拟手表。我们的评估显示,有了Telepath,手机在为智能手表设计的活动识别任务上表现准确,平均达到了手表准确率的87%,同时将手表的电池寿命延长了2.1倍。
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