| Budget Amount *help |
¥24,570,000 (Direct Cost: ¥18,900,000、Indirect Cost: ¥5,670,000)
Fiscal Year 2020: ¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2019: ¥5,720,000 (Direct Cost: ¥4,400,000、Indirect Cost: ¥1,320,000)
Fiscal Year 2018: ¥6,890,000 (Direct Cost: ¥5,300,000、Indirect Cost: ¥1,590,000)
Fiscal Year 2017: ¥7,800,000 (Direct Cost: ¥6,000,000、Indirect Cost: ¥1,800,000)
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| Outline of Final Research Achievements |
This study developed techniques for predicting semantic labels to various indoor locations. Specifically, we developed methods for estimating static and dynamic labels for indoor locations. When a user is in a bedroom at time t, for example, we estimate a class label for "bedroom" using sensor data at time t observed by a sensor device possessed by the user. We also proposed a method for estimating dynamic labels of indoor objects whose states change dynamically, such as doors and windows, using information such as the Doppler effect obtained from active sound sensing. These results were accepted as several full papers in UbiComp (ACM IMWUT), the top international conference in the field, and IEEE Sensors Journal.
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