Human Activity Recognition: Accelerometers Unveil Your Actions
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چکیده
Wearable devices become more popular and are usually paired with smartphones. In this work, we explore approaches of recognizing human activities using accelerometer data from smartphones and wearable devices. The dataset we use records acceleration signals from four positions that are representable for smartphones and wearable devices. In real world situation, it is more likely that one only takes one smartphone and another wearable device with him/her. So our ultimate goal is to accurately recognize human actions using two accelerometers. We apply machine learning algorithms to train and infer human motion. The input of our algorithm is acceleration data from sensors. We then use GDA and SVM to output a predicted activity class. The experimental results demonstrate the effectiveness of our method.
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تاریخ انتشار 2015