Indiana University-Purdue University Indianapolis (IUPUI) / 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. / 2023-06-01
| Identifer | oai:union.ndltd.org:IUPUI/oai:scholarworks.iupui.edu:1805/26445 |
| Date | 08 1900 |
| Creators | Gangadharan, Kiirthanaa |
| Contributors | Zhang, Qingxue, King, Brian S., Chien, Yung-Ping S. |
| Source Sets | Indiana University-Purdue University Indianapolis |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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