Human action recognition is an active research area benefitting many applications. Example applications include human-computer interaction, assistive-living, rehabilitation, and gaming. Action recognition can be broadly categorized into vision-based and inertial sensor-based. Under realistic operating conditions, it is well known that there are recognition rate limitations when using a single modality sensor due to the fact that no single sensor modality can cope with various situations that occur in practice. The hypothesis addressed in this dissertation is that by using and fusing the information from two differing modality sensors that provide 3D data (a Microsoft Kinect depth camera and a wearable inertial sensor), a more robust human action recognition is achievable. More specifically, effective and computationally efficient features have been devised and extracted from depth images. Both feature-level fusion and decision-level fusion approaches have been investigated for a dual-modality sensing incorporating a depth camera and an inertial sensor. Experimental results obtained indicate that the developed fusion approaches generate higher recognition rates compared to the situations when an individual sensor is used. Moreover, an actual working action recognition system using depth and inertial sensing has been devised which runs in real-time on laptop platforms. In addition, the developed fusion framework has been applied to a medical application.
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机译：用等离子体约束实现重力场的动态控制热核聚变（TLTS）方法，通过热辐射等离子体绝缘的壁反应堆防止中子辐射并节省磁场和等离子体的混合，使用旋转磁场的异步磁惯性约束反应堆（AMITYAR和HFM）为实施该方法，在该反应器中点燃热核反应的方法，爆炸式等离子发生器（VIP）的实施方法，以及具有HFM的特立普安瓿，以实现D + T反应和具有超高温热度的HFM D +3НЕ和1Н+11В的高温反应
