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Pose-based hand gesture recognition has been widely studied in the recent years. Compared with full body action recognition, hand gesture involves joints that are more spatially closely distributed with stronger collaboration. This nature requires a different approach from action recognition to capturing the complex spatial features. Many gesture categories, such as 'Grab' and 'Pinch,' have very similar motion or temporal patterns posing a challenge on temporal processing. To address these challenges, this article proposes a two-stream neural network with one stream being a...

The primary goal of hand gesture recognition with wearables is to facilitate the realization of gestural user interfaces in mobile and ubiquitous environments. A key challenge in wearable-based hand gesture recognition is the fact that a hand gesture can be performed in several ways, with each consisting of its own configuration of motions and their spatio-temporal dependencies. However, the existing methods generally focus on the characteristics of a single point on hand, but ignores the diversity of motion information over hand skeleton, and as a result, they suffer from two key challenges t...

Hand gesture recognition can be exploited to benefit ubiquitous applications using sensors. Currently, the inherent complexity of human physical activities makes it difficult to accurately recognize gestures with wearable sensors, especially in real time. To this end, a real-time hand gesture recognition system is presented in this paper. In particular, sliding window technology and y-axis threshold are used to detect intended gestures from a continuous data stream and then the segmented data are classified by applying a stacked Long Short-Term Memory (LSTM) model. After noise is removed, six-...

The article discusses research revealing the use of an inertial sensor built into smartphones to overcome unhealthy sitting behaviors of the office worker.