| Budget Amount *help |
¥17,030,000 (Direct Cost: ¥13,100,000、Indirect Cost: ¥3,930,000)
Fiscal Year 2018: ¥2,210,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥510,000)
Fiscal Year 2017: ¥3,120,000 (Direct Cost: ¥2,400,000、Indirect Cost: ¥720,000)
Fiscal Year 2016: ¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
Fiscal Year 2015: ¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2014: ¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
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| Outline of Final Research Achievements |
In this research, we researched the technology to efficiently recognize and estimate human activity and placed situation using sensor devices from two layers of physical and semantic viewpoints. As a result, we achieved many results such as action recognition methods for unknown action classes, future prediction methods related to actions, machine learning and feature quantity optimization considering data loss, utilization of crowdsourcing and gamification in data collection, medical facilities and nursing facilities, large-scale activity data collection by nurses and caregivers, activity recognition using LoraWAN sensor, activity recognition considering time lag in activity labels, and transfer learning considering differences between users and homes. We received 22 awards for excellent dissertation.
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