Hand Posture Detection of Smartphone Users Using LSTM Networks

2019 ◽  
pp. 19-25
Author(s):  
Song Lim Tan ◽  
Hui Fuang Ng ◽  
Boon Yaik Ooi ◽  
Hung Khoon Tan ◽  
Jacqueline Lee Fang Ang
Keyword(s):  
Hand Posture

Interactions Between Hand Posture and Eye Movement

2013 ◽  
Author(s):  
Jihyun Suh ◽  
Richard A. Abrams
Keyword(s):  
Eye Movement ◽  
Hand Posture

Hand posture Recognition for shape variant hands

2011 ◽  
Vol 6 (4) ◽  
pp. 1-6
Author(s):  
Ayesha Butalia ◽  
◽  
A.K. Ramani ◽  
Parag Kulkarni ◽  
Swapnil Patil ◽  
...  
Keyword(s):  
Hand Posture ◽  
Hand Posture Recognition ◽  
Posture Recognition

The Effects of Keyboard Layout and Size on Smartphone Typing Performance

2020 ◽  
Vol 64 (1) ◽  
pp. 985-989
Author(s):  
Colton J. Turner ◽  
Barbara S. Chaparro ◽  
Inga M. Sogaard ◽  
Jibo He
Keyword(s):  
Error Rates ◽  
Hand Posture ◽  
Standard Keyboard ◽  
Keyboard Layout

Usability and typing performance on a smartphone with two unique QWERTY keyboard layouts (standard vs. curved) on two phone sizes (4.0-inch vs. 5.5-inch displays) was investigated in this study. The effect of hand posture was also investigated (one- vs. two-thumbs). Results show users typed the slowest when using one thumb with the curved keyboard on the small phone (15 WPM), and the fastest when using two thumbs with the standard keyboard on the large phone (24 WPM). Typing performance with the curved keyboard on the large phone size (19 WPM) did not differ between typing with one thumb using the standard keyboard on the large or small phone, or with two thumbs using the standard keyboard on the small phone. Error rates were higher when using the curved keyboard, regardless of phone size. Subjectively, the curved keyboard was rated inferior for both phone sizes in comparison to the standard layout.

scholarly journals Approach to hand posture recognition based on hand shape features for human–robot interaction

2021 ◽  
Author(s):  
Jing Qi ◽  
Kun Xu ◽  
Xilun Ding
Keyword(s):  
Gaussian Mixture ◽  
Human Robot Interaction ◽  
Polar Coordinates ◽  
Support Vector ◽  
Hand Posture ◽  
Data Set ◽  
Hand Shape ◽  
Hand Posture Recognition ◽  
Hand Segmentation ◽  
Posture Recognition

AbstractHand segmentation is the initial step for hand posture recognition. To reduce the effect of variable illumination in hand segmentation step, a new CbCr-I component Gaussian mixture model (GMM) is proposed to detect the skin region. The hand region is selected as a region of interest from the image using the skin detection technique based on the presented CbCr-I component GMM and a new adaptive threshold. A new hand shape distribution feature described in polar coordinates is proposed to extract hand contour features to solve the false recognition problem in some shape-based methods and effectively recognize the hand posture in cases when different hand postures have the same number of outstretched fingers. A multiclass support vector machine classifier is utilized to recognize the hand posture. Experiments were carried out on our data set to verify the feasibility of the proposed method. The results showed the effectiveness of the proposed approach compared with other methods.

scholarly journals Hand Detection Using Cascade of Softmax Classifiers

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Yan-Guo Zhao ◽  
Feng Zheng ◽  
Zhan Song
Keyword(s):  
Logistic Regression ◽  
Real Time ◽  
Sliding Window ◽  
Detection Accuracy ◽  
Hand Posture ◽  
Hand Detection ◽  
Cascade Structure ◽  
Real Time Applications ◽  
Binary Classifiers ◽  
Background Pattern

Sliding-window based multiclass hand posture detections are often performed by detecting postures of each predefined category using an independent detector, which makes it lack efficiency and results in high postures confusion rates in real-time applications. To tackle such problems, in this work, an efficient cascade detector that integrates multiple softmax-based binary (SftB) models and a softmax-based multiclass (SftM) model is investigated to perform multiclass posture detection in parallel. The SftB models are used to distinguish the predefined postures from the background regions, and the SftM model is applied to discriminate among all the predefined hand posture categories. Another usage of the cascade structure is that it could effectively decompose the complexity of background pattern space and therefore improve the detection accuracy. In addition, to balance the detection accuracy and efficiency, the HOG features of increasing resolutions will be adopted by classifiers of increasing stage-levels in the cascade structure. The experiments are implemented under various scenarios with complicated background and challenging lightings. Results show the superiority of the proposed SftB classifiers over the traditional binary classifiers such as logistic regression, as well as the accuracy and efficiency improvements brought by the softmax-based cascade architecture compared with the noncascade multiclass softmax detectors.

Hand posture as localizing sign in adult focal epileptic seizures

2019 ◽  
Vol 86 (5) ◽  
pp. 793-800
Author(s):  
Isabella Ferando ◽  
Jason R. Soss ◽  
Christopher Elder ◽  
Vishal Shah ◽  
Giorgio Lo Russo ◽  
...  
Keyword(s):  
Epileptic Seizures ◽  
Hand Posture
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