Hand Gesture Recognition Based Omnidirectional Wheelchair Control Using IMU and EMG Sensors

Kundu, Ananda Sankar; Mazumder, Oishee; Lenka, Prasanna Kumar; Bhaumik, Subhasis

doi:10.1007/s10846-017-0725-0

Cited by 98 publications

(54 citation statements)

References 25 publications

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“…The conversion of YCbCr and RGB color space is linear, and the calculation is relatively easy. In addition, the clustering effect of skin color in the YCbCr color space is more compact and easier to segment than the HSV color space [21][22][23]. So this paper chooses to segment the gesture image in the YCbCr color space.…”

Section: Selection Of Color Spacementioning

confidence: 99%

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

Tan

Sun

et al. 2019

Neural Comput & Applic

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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.

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Section: Selection Of Color Spacementioning

confidence: 99%

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

Tan

Sun

et al. 2019

Neural Comput & Applic

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“…The CAMShift algorithm is available in MATLAB Library and has been widely used in various types of research due to its ease of applying algorithms [17]. Some studies use it for learning local features of face recognition online [18], hand gesture recognition [19], and player tracking system [20]. Figure 3 is an example of face detection in video using CAMShift.…”

Section: Face Detectionmentioning

confidence: 99%

Combination of fast hybrid classification and k value optimization in k-nn for video face recognition

Septiana

Suciati

2020

register. jurnal. ilm. teknologi. sistem. inf.

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Nowadays, the need for face recognition is no longer include images only but also videos. However, there are some challenges associated with the addition of this new technique such as how to determine the right pre-processing, feature extraction, and classification methods to obtain excellent performance. Although nowadays the k-Nearest Neighbor (k-NN) is widely used, high computational costs due to numerous features of the dataset and large amount of training data makes adequate processing difficult. Several studies have been conducted to improve the performance of k-NN using the FHC (Fast Hybrid Classification) method by optimizing the local k values. One of the disadvantages of the FHC Method is that the k value used is still in the default form. Therefore, this research proposes the use of k-NN value optimization methods in FHC, thereby, increasing its accuracy. The Fast Hybrid Classification which combines the k-means clustering with k-NN, groups the training data into several prototypes called TLDS (Two Level Data Structure). Furthermore, two classification levels are applied to label test data, with the first used to determine the n number of prototypes with the same class in the test data. The second classification using the optimized k value in the k-NN method, is employed to sharpen the accuracy, when the same number of prototypes does not reach n. The evaluation results show that this method provides 86% accuracy and time performance of 3.3 seconds.

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“…Thus, EMG profile can be regarded as a representation to indicate the human control intention [18]. The EMGbased methods can be integrated with Kinect sensor [19] [20] and inertial measure unit sensor to achieve human control of mobile robots or omnidirectional wheelchairs [21] [22]. However, these approaches are developed based on machine learning, so it is hard to use them for control of mobile robots in real time.…”

Section: Introductionmentioning

confidence: 99%