Browsing by Subject "Wearable Computer"

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    Deep Transferable Intelligence for Wearable Big Data Pattern Detection
    (2021-08) Gangadharan, Kiirthanaa; Zhang, Qingxue; King, Brian S.; Chien, Yung-Ping S.
    Biomechanical Big Data is of great significance to precision health applications, among which we take special interest in Physical Activity Detection (PAD). In this study, we have performed extensive research on deep learning-based PAD from biomechanical big data, focusing on the challenges raised by the need for real-time edge inference. First, considering there are many places we can place the motion sensors, we have thoroughly compared and analyzed the location difference in terms of deep learning-based PAD performance. We have further compared the difference among six sensor channels (3-axis accelerometer and 3-axis gyroscope). Second, we have selected the optimal sensor and the optimal sensor channel, which can not only provide sensor usage suggestions but also enable ultra-lowpower application on the edge. Third, we have investigated innovative methods to minimize the training effort of the deep learning model, leveraging the transfer learning strategy. More specifically, we propose to pre-train a transferable deep learning model using the data from other subjects and then fine-tune the model using limited data from the target-user. In such a way, we have found that, for single-channel case, the transfer learning can effectively increase the deep model performance even when the fine-tuning effort is very small. This research, demonstrated by comprehensive experimental evaluation, has shown the potential of ultra-low-power PAD with minimized sensor stream, and minimized training effort.
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    Wearable Big Data Pertinence Learning with Deep Spatiotemporal co-Mining
    (IEEE, 2022-05) Wong, Junhua; Zhang, Qingxue; Electrical and Computer Engineering, School of Engineering and Technology
    Wearable Computers are greatly advancing big data practices, by levering their capabilities of ubiquitous big data capturing and streaming. However, one critical challenge is the amount of data to be transmitted, which consumes too much energy of the battery-constrained wearable devices. Targeting this obstacle, we propose a novel big data pertinence learning approach, which can learn and extract pertinent patterns in wearable big data for redundancy reduction. More specifically, a hybrid deep learning approach based on both Convolutional Autoencoder and Long Short-term Memory is proposed, which can mine both spatial and temporal patterns in the data for key pattern extraction. The achieved spatiotemporal co-mining ability when evaluated on a real- world motion dynamics big data application, demonstrates the attractive potential of pertinence extraction and redundancy minimization. This study is expected to greatly advance wearable big data practices.