The College Students’ Behavioral Intention to Use Mobile Reading Apps in Sichuan, China

Purpose: The purpose of this study is to investigate the college students’ behavioral intention to use mobile reading applications in Sichuan, China. The key variables include perceived usefulness, perceived ease of use, perceived value, perceived enjoyment, attitude, social influence, and behavioral intention. Research design, data, and methodology: The target population is 500 students from three universities in Sichuan. The quantitative research method used in this study was based on a questionnaire. The sampling technique contains judgmental, stratified random and convenience sampling. The content validity was confirmed by the index of item-objective congruence (IOC). The pilot test involves 50 participants to ensure reliability by Cronbach’s alpha. The data were analyzed by Confirmatory factor analysis (CFA) and Structural equation modeling (SEM). Results: The social influence presented the strongest effect on behavioral intention and proved that attitude directly influenced behavioral intention. The significant influences that support attitude are perceived ease of use, usefulness, value, and enjoyment. Conclusions: The research can help developers to develop effective mobile reading apps related to excellent traditional Chinese cultural knowledge. Educators can promote the dissemination of excellent traditional Chinese cultural knowledge can consider improving the influence of mobile phone reading content and software in society to help college students improve their learning efficiency

The Determinants of Behavioral Intention to Use Mobile Reading Apps of Collage Students in Chongqing, China

Purpose: This research examined the determinants of behavioral intention of college students in Chongqing who have mobile reading experience of excellent Chinese traditional culture. The conceptual framework proposed causal relationships among perceived usefulness, perceived ease of use, perceived value, perceived enjoyment, attitude, social influence, and behavioral intention. Research design, data, and methodology: 500 students from three universities in Chongqing were selected. The researcher used the questionnaire as a tool. The sampling technique contains judgmental, stratified random and convenience sampling. The content validity was confirmed by the index of item-objective congruence (IOC). The pilot test involves 50 participants to ensure reliability by Cronbach’s alpha. Results: The social influence presented the strongest effect on behavioral intention and proved that attitude directly influenced behavioral intention. The significant influences that support attitude were perceived value and perceived enjoyment. Nevertheless, perceived ease of use and perceived usefulness had no significant influence on attitude. The factors of perceived value and perceived enjoyment indirectly impacted behavioral intention. Conclusions: The research can help developers to consider these factors that affect users more when developing mobile reading apps related to excellent traditional Chinese cultural knowledge

The Need for marker-less computer vision techniques for human gait analysis on video surveillance to detect concealed firearms

Crimes involving the use of firearms have been on the increase in the past few years. One of the measures adopted to prevent these crimes is the use of CCTV operators at video surveillance centers to detect persons carrying concealed firearms on their bodies by monitoring their behavior. This paper has found that this technique has challenges associated with human weaknesses and errors. A review of the current attempts to automate video surveillance for concealed firearm detection has found that they have the limitation that the techniques can only be employed on stationary and cooperative persons. This makes them inappropriate for real-life surveillance. This paper highlights the need for automated video surveillance solutions that can detect persons carrying concealed firearms when they are not stationary and aware of the scanning process. We further explore automated behavioral analysis and specifically gait analysis as a possible technique for concealed firearm detection on video surveillance. Lastly, the paper highlights the possibility and viability of human gait analysis using marker-less computer vision techniques for detecting persons carrying firearms on their waist line

The Need for Marker-Less Computer Vision Techniques for Human Gait Analysis on Video Surveillance to Detect Concealed Firearms

Crimes involving the use of firearms have been on the increase in the past few years. One of the measures adopted to prevent these crimes is the use of CCTV operators at video surveillance centers to detect persons carrying concealed firearms on their bodies by monitoring their behavior. This paper has found that this technique has challenges associated with human weaknesses and errors. A review of the current attempts to automate video surveillance for concealed firearm detection has found that they have the limitation that the techniques can only be employed on stationary and cooperative persons. This makes them inappropriate for real-life surveillance. This paper highlights the need for automated video surveillance solutions that can detect persons carrying concealed firearms when they are not stationary and aware of the scanning process. We further explore automated behavioral analysis and specifically gait analysis as a possible technique for concealed firearm detection on video surveillance. Lastly, the paper highlights the possibility and viability of human gait analysis using marker-less computer vision techniques for detecting persons carrying firearms on their waist line

Gait analysis of smart phones with the help of the accelerometer sensor

Spor alanlarında insan hareketlerini ölçme yeteneği performans ölçüm ve gelişimi için önemli konular arasındadır. Bu durum aynı zamanda klinik değerlendirmelerin de önemli bir parçasıdır. Özellikle elektromanyetik sistemler insan hareketlerini değerlendirmek için en yaygın kullanılan yöntemler arasında yer alır. Buradaki çalışmada 100 metre uzunluğunda bir koridorda 50 farklı kişinin yürüme verileri kullanılmıştır. Yürüme verileri akıllı telefon için geliştirilen bir yazılım ile ivmeölçer sensöründen elde edilmiştir. Verilere üç boyutlu Local Binary Pattern (LBP) yöntemi uygulanmış ve toplam 768 öznitelik çıkarılmıştır. Farklı sınıflandırma algoritmaları ile testler yapılmış ve Subspace KNN ile %97,2 başarılı sınıflandırma elde edilmiştir. Cinsiyete göre yapılan sınıflandırmada ise %99,7 başarılı sınıflandırma elde edilmiştir. Bu yöntem ile yürüme bozukluğu tespitinde yüksek maliyetli cihazlar yerine daha ekonomik yöntemler geliştirileceği düşünülmektedir.The ability to measure human movements in sports fields is among the important issues for performance measurement and development. This instance is also an important part of clinical evaluations. Electromagnetic systems are among the most widely used methods to evaluate human movements. In this study, walking data of 50 different people were used in a 100-meter-long corridor. The walking dataset was obtained from the accelerometer sensor with a software developed for the smartphone. Three-dimensional Local Binary Pattern (LBP) method was applied to the dataset and a total of 768 features were generated. Datasets were made with different classification algorithms and 97.2% successful classification was achieved with Subspace KNN. In the classification according to gender, 99.7% successful classification was obtained. With this method, it is thought that more economical methods will be developed instead of high-cost devices in detecting gait disorders