| Budget Amount *help |
¥17,680,000 (Direct Cost: ¥13,600,000、Indirect Cost: ¥4,080,000)
Fiscal Year 2019: ¥6,110,000 (Direct Cost: ¥4,700,000、Indirect Cost: ¥1,410,000)
Fiscal Year 2018: ¥6,240,000 (Direct Cost: ¥4,800,000、Indirect Cost: ¥1,440,000)
Fiscal Year 2017: ¥5,330,000 (Direct Cost: ¥4,100,000、Indirect Cost: ¥1,230,000)
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| Outline of Final Research Achievements |
In this study, we examined a framework for integrating activity recognition and indoor positioning, and in particular, we aimed to make use of deep learning.
In PDR (Pedestrian Dead-Reckoning), we constructed an end-to-end deep learning PDR that directory acquires a relative position change from sensor data. We also propose indoor context estimation using environmentally installed devices. We use noise reduction based on deep learning for BLE (Bluetooth Low Energy) radio strength. Our data obtained through the research activities were made available to the world via http://hub.hasc.jp. In addition, we have developed a system named Synerex for supply and demand exchange as a fundamental platform for data collection.
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