Research on gesture recognition of smart data fusion features in the IoT

Abstract

With the rapid development of Internet of Things (IoT) technology, the interaction between people and things has become increasingly frequent. Use simple gestures instead of complex operations to interact with the machine, the fusion of smart data feature information and so on has gradually become a research hotspot. Considering that the depth image of the Kinect sensor lacks color information and is susceptible to depth thresholds, this paper proposes a gesture segmentation method based on the fusion of color information and depth information, in order to ensure the complete information of the segmentation image, a gesture feature extraction method based on Hu invariant moment and HOG feature fusion is proposed, by determining the optimal weight parameters, the global and local features are effectively fused. Finally, the SVM classifier is used to classify and identify gestures. The experimental results show that the proposed fusion features method has a higher gesture recognition rate and better robustness than the traditional method.

Human Activity Recognition Based on Gramian Angular Field and Deep Convolutional Neural Network

“…The time series is transformed into a polar coordinate system according to (3). The GASF and GADF images can be obtained by (4) and (5), respectively.…”

“…With the rapid development of the 5th generation (5G) mobile networks, Internet of things (IoT) and artificial intelligence (AI), the technology of human activity recognition (HAR) is becoming more and more important in people's daily lives because of its ability to analyze and recognize human activities by the raw sensor data. It has been widely used in many aspects, such as daily activity analysis [1], video surveillance [2], gait analysis [3] and gesture recognition [4]. At present, HAR is mainly divided into two categories: sensor-based activity recognition [5]- [7] and video-based activity recognition [8]- [10].…”

Numerical simulation of thermal insulation and longevity performance in new lightweight ladle

“…50,51 In the place where the physical quantity changes greatly, the number of grids can be appropriately increased, and the grid density can be appropriately reduced when the physical quantity changes steadily. 52,53 When a material changes in an object, a new grid cell should be divided at the material change. This is done to ensure that only one material appears in each cell.…”

“…The advantage of this method is that on the one hand, it can save the workload and reduce the time spent on mesh drawing, on the other hand, it will not lead to too sparse mesh drawing, resulting in inaccurate results 50,51 . In the place where the physical quantity changes greatly, the number of grids can be appropriately increased, and the grid density can be appropriately reduced when the physical quantity changes steadily 52,53 . When a material changes in an object, a new grid cell should be divided at the material change.…”

IOT Based Gesture Recognition for Smart Controlling

“…This system is required less computational load, so this system operates in real time. Chong Tan proposed feature fusion SVM based gesture classify and identification gesture [22]. SVM technique combines HOG features and Hu invariant movement.…”