Investigation of Inside-Out Tracking Methods for Six Degrees of Freedom Pose Estimation of a Smartphone in Augmented Reality

Six degrees of freedom(6DoF)input interfaces are essential formanipulating virtual objects through translation or rotation in three-dimensional(3D)space.A traditional outside-in tracking controller requires the installation of expensive hardware in advance.While inside-out tracking controllers have been proposed,they often suffer from limitations such as interaction limited to the tracking range of the sensor(e.g.,a sensor on the head-mounted display(HMD))or the need for pose value modification to function as an input interface(e.g.,a sensor on the controller).This study investigates 6DoF pose estimation methods without restricting the tracking range,using a smartphone as a controller in augmented reality(AR)environments.Our approach involves proposing methods for estimating the initial pose of the controller and correcting the pose using an inside-out tracking approach.In addition,seven pose estimation algorithms were presented as candidates depending on the tracking range of the device sensor,the tracking method(e.g.,marker recognition,visual-inertial odometry(VIO)),and whether modification of the initial pose is necessary.Through two experiments(discrete and continuous data),the performance of the algorithms was evaluated.The results demonstrate enhanced final pose accuracy achieved by correcting the initial pose.Furthermore,the importance of selecting the tracking algorithm based on the tracking range of the devices and the actual input value of the 3D interaction was emphasized.

Validity,Reliability,and Measurement Invariance of the Thai Smartphone Application-Based Addiction Scale and Bergen Social Media Addiction Scale

Background:In recent years,there has been increased research interest in both smartphone addiction and social media addiction as well as the development of psychometric instruments to assess these constructs.However,there is a lack of psychometric evaluation for instruments assessing smartphone addiction and social media addiction in Thailand.The present study evaluated the psychometric properties and gender measurement invariance of the Thai version of the Smartphone Application-Based Addiction Scale(SABAS)and Bergen Social Media Addiction Scale(BSMAS).Method:A total of 801 Thai university students participated in an online survey from January 2022 to July 2022 which included demographic information,SABAS,BSMAS,and the Internet Gaming Disorder Scale-Short Form(IGDS9-SF).Results:Confirmatory Factor Analyses(CFAs)found that both the SABAS and BSMAS had a one-factor structure.Findings demonstrated adequate psychometric properties of both instruments and also supported measurement invariance across genders.Moreover,scores on the SABAS and BSMAS were correlated with scores on the IGDS9-SF.Conclusion:The results indicated that the SABAS and BSMAS are useful psychometric instruments for assessing the risk of smartphone addiction and social media addiction among Thai young adults.

Logical Image Acquisition and Analysis of Android Smartphones

Android smartphones largely dominate the smartphone market. For this reason, it is very important to examine these smartphones in terms of digital forensics since they are often used as evidence in trials. It is possible to acquire a physical or logical image of these devices. Acquiring physical and logical images has advantages and disadvantages compared to each other. Creating the logical image is done at the file system level. Analysis can be made on this logical image. Both logical image acquisition and analysis of the image can be done by software tools. In this study, the differences between logical image and physical image acquisition in Android smartphones, their advantages and disadvantages compared to each other, the difficulties that may be encountered in obtaining physical images, which type of image contributes to obtaining more useful and effective data, which one should be preferred for different conditions, and the benefits of having root authority are discussed. The practice of getting the logical image of the Android smartphones and making an analysis on the image is also included. Although root privileges are not required for logical image acquisition, it has been observed that very limited data will be obtained with the logical image created without root privileges. Nevertheless, logical image acquisition has advantages too against physical image acquisition.

Relationship between depression,smartphone addiction,and sleep among Chinese engineering students during the COVID-19 pandemic

BACKGROUND Existing research has demonstrated that depression is positively related to smartphone addiction, but the role of sleep has not been discussed thoroughly, especially among engineering undergraduates affected by the coronavirus disease 2019 pandemic.AIM To evaluate sleep as a mediator of the association between smartphone addiction and depression among engineering undergraduates.METHODS Using a multistage stratified random sampling method, a cross-sectional survey was conducted among 692 engineering undergraduates from a top engineering university in China, and data were collected by self-reported electronic questionnaires. The data included demographic characteristics, such as age, gender, the Smartphone Addiction Scale-Short Version(SAS-SV), the 9-item Patient Health Questionnaire, and the Pittsburgh Sleep Quality Index. Pearson correlation and multiple linear regression analyses were used to examine the association between smartphone addiction and depression, while structural equation models were established to evaluate the possible mediating role of sleep.RESULTS Based on the cutoffs of the SAS-SV, the rate of smartphone addiction was 63.58 percent, with 56.21 percent for women and 65.68 percent for men, among 692 engineering students. The prevalence of depression among students was 14.16 percent, with 17.65 percent for women, and 13.18 percent for men. Smartphone addiction was positively correlated with depression, and sleep played a significant mediating effect between the two, accounting for 42.22 percent of the total effect. In addition, sleep latency, sleep disturbances, and daytime dysfunction significantly mediated the relationship between depression and smartphone addiction. The mediating effect of sleep latency was 0.014 [P < 0.01;95% confidence interval(CI): 0.006-0.027], the mediating effect of sleep disturbances was 0.022(P < 0.01;95%CI: 0.011-0.040), and the mediating effect of daytime dysfunction was 0.040(P < 0.01;95%CI: 0.024-0.059). The influence of sleep latency, sleep disturbances, and daytime dysfunction accounted for 18.42%, 28.95%, and 52.63% of the total mediating effect, respectively.CONCLUSION The results of the study suggest that reducing excessive smartphone use and improving sleep quality can help alleviate depression.

A non-contact measurement method for rock mass discontinuity orientations by smartphone

Smartphones are usually packed with a large number of features.An increasing number of researchers are paying attention to the technological capabilities of smartphones,which is a new topic and research interest.This paper proposes a method using smartphones and digital photogrammetry to measure the discontinuity orientation of a rock mass.Smartphone photos satisfying a certain overlap rate provide an efficient method for generating point cloud models of rock outcrops based on image matching.Using the target and the generated point cloud model allows for determining actual geographic coordinates and the measurement of discontinuity orientations.The method proposed has been applied to two different study areas.The discontinuity orientations measured by the proposed method are compared with those measured by the manual method in two cases.The results show a good agreement,verifying the reliability and accuracy of the proposed method.The main contribution of this paper is to use knowledge of coordinate rotation to determine the actual geographic location of the model through a square target.The equipment used in this study is simple,and photogrammetric field surveys are easy to carry out.

Grey Wolf-Based Method for an Implicit Authentication of Smartphone Users

Smartphones have now become an integral part of our everyday lives.User authentication on smartphones is often accomplished by mechanisms(like face unlock,pattern,or pin password)that authenticate the user’s identity.These technologies are simple,inexpensive,and fast for repeated logins.However,these technologies are still subject to assaults like smudge assaults and shoulder surfing.Users’touch behavior while using their cell phones might be used to authenticate them,which would solve the problem.The performance of the authentication process may be influenced by the attributes chosen(from these behaviors).The purpose of this study is to present an effective authentication technique that implicitly offers a better authentication method for smartphone usage while avoiding the cost of a particular device and considering the constrained capabilities of smartphones.We began by concentrating on feature selection methods utilizing the grey wolf optimization strategy.The random forest classifier is used to evaluate these tactics.The testing findings demonstrated that the grey wolf-based methodology works as a better optimum feature selection for building an implicit authentication mechanism for the smartphone environment when using a public dataset.It achieved a 97.89%accuracy rate while utilizing just 16 of the 53 characteristics like utilizing minimum mobile resources mainly;processing power of the device and memory to validate individuals.Simultaneously,the findings revealed that our approach has a lower equal error rate(EER)of 0.5104,a false acceptance rate(FAR)of 1.00,and a false rejection rate(FRR)of 0.0209 compared to the methods discussed in the literature.These promising results will be used to create a mobile application that enables implicit validation of authorized users yet avoids current identification concerns and requires fewer mobile resources.

Smartphone-based bridge frequency identification using vehicle contact-point response

Bridge frequency(BF)identification using the vehicle scanning method has attracted considerable attention during the last two decades.However,most previous studies have adopted unrealistic vehicle models,thus finding limited practical applications.This study proposes a smartphone-based BF identification method that uses the contact-point acceleration response of a four degree-of-freedom vehicle model.The said response can be inferred from the vehicle body response measured by a smartphone.For realizing practical applications,this method is incorporated into a self-developed smartphone app to obtain data smoothly and identify BFs in a timely manner.Numerical and experimental investigations are performed to verify the effectiveness of the proposed method.In particular,the robustness of this method is investigated numerically against various factors,including the vehicle speed,bridge span,road roughness,and bridge type.Furthermore,laboratory calibration tests are performed to investigate the accuracy of the smartphone gyroscope in measuring the angular velocity,where anomalous data are detected and eliminated.Laboratory experiment results for a simply supported bridge indicate that the proposed method can be used to identify the first two BFs with acceptable accuracy.

Prevalence and Factors Associated with Smartphone Addiction among Adolescents–A Nationwide Study in Malaysia

Smartphone ownership among adolescents is getting common in this decade especially in Malaysia;Adolescent are strongly devoted to their smartphone and this may lead to smartphone addiction.Studies have reported that smartphone addiction has become an emerging social and health problem especially among the youth in many countries however there is lack of study among adolescents in Malaysia.This study aimed to examine the prevalence and factors associated with smartphone addiction among adolescents in Malaysia.This was a cross-sectional study involving adolescents from 15 primary care clinics throughout the country.Respondents were assessed on their smartphone activities using the Malaysian short version of the Smartphone addiction scale(SAS-M-SV).Multiple logistic regression was used to determine the predictors of smartphone addiction among adolescents.The study was conducted among 921 adolescents with 49.6%male(n=457).The mean age of adolescents was 16.4±2.4 years.The ethnicity distribution were 74.6%Malay,7.3%Chinese,4.7%Indian and 13.4%other ethnicities.The prevalence of smartphone addiction was 37.1%(342/921);37.4%in male and 36.9%in female.Based on multiple logistic regression analysis,longer duration of smartphone use per week was associated with higher odds of smartphone addiction among adolescent(odd ratio=1.005%,95%confidence interval=1.000–1.009,p-value=0.039).Smartphone addiction is present in nearly four in ten adolescents in Malaysia.Adolescents who spend longer duration in smartphone usage per week were associated with higher odds of having smartphone addiction.Parents should be more alert and vigilant about this finding.Hence,parents should limit their children from spending too much of time with smartphone in order to prevent their children from getting smartphone addiction.

Optimal Unification of Static and Dynamic Features for Smartphone Security Analysis

Android Smartphones are proliferating extensively in the digital world due to their widespread applications in a myriad offields.The increased popularity of the android platform entices malware developers to design malicious apps to achieve their malevolent intents.Also,static analysis approaches fail to detect run-time behaviors of malicious apps.To address these issues,an optimal unification of static and dynamic features for smartphone security analysis is proposed.The proposed solution exploits both static and dynamic features for generating a highly distinct unified feature vector using graph based cross-diffusion strategy.Further,a unified feature is subjected to the fuzzy-based classification model to distinguish benign and malicious applications.The suggested framework is extensively experimentally validated through both qualitative and quantitative analysis and results are compared with the existing solutions.Performance evaluation over benchmarked datasets from Google Play Store,Drebin,Androzoo,AMD,and CICMalDroid2020 revealed that the suggested solution outperforms state-of-the-art methods.We achieve average detection accuracy of 98.62%and F1 Score of 0.9916.

Could Military Commanders’ Good LeadershipInfluence Subordinates’ Smartphone Overdependence? A Serial Mediation Analysis

Owing to the ubiquitous use of smartphones by soldiers, military researchers have an increasing interest in potentially problematicside effects such as smartphone overdependence. This raises a question regarding the psychological mechanisms underlying thepotentially self-damaging use of smartphones. Here, we address this question by analyzing how heterogeneity in commander’sgood leadership explains subordinate soldiers’ differences in self-control and smartphone use. Specifically, we found thatsubordinate soldiers who thought their commander's leadership was good were self-regulated, less dependent on smartphones,less stressed, and finally had good mental health. This result indicates that commander’s good leadership can be used toestimate whether subordinate soldiers exert control over their impulses and use their smartphones properly. Thus, the currentfindings help to identify external factors that lead to a better understanding of problematic smartphone use and can potentiallyhelp to design appropriate preventive mechanisms or interventions that target commander’s good leadership.

Human Personality Assessment Based on Gait Pattern Recognition Using Smartphone Sensors

Human personality assessment using gait pattern recognition is one of the most recent and exciting research domains.Gait is a person’s identity that can reflect reliable information about his mood,emotions,and substantial personality traits under scrutiny.This research focuses on recognizing key personality traits,including neuroticism,extraversion,openness to experience,agreeableness,and conscientiousness,in line with the bigfive model of personality.We inferred personality traits based on the gait pattern recognition of individuals utilizing built-in smartphone sensors.For experimentation,we collected a novel dataset of 22 participants using an android application and further segmented it into six data chunks for a critical evaluation.After data pre-processing,we extracted selected features from each data segment and then applied four multiclass machine learning algorithms for training and classifying the dataset corresponding to the users’Big-Five Personality Traits Profiles(BFPT).Experimental results and performance evaluation of the classifiers revealed the efficacy of the proposed scheme for all big-five traits.

Prevalence and predictors of smartphone addiction and insomnia among nurses working in the outpatient department after the second wave of the coronavirus disease 2019 pandemic

Objectives:The present study aims to assess the prevalence and predictors of smartphone addiction and insomnia among nurses working in the outpatient department(OPD)after the second wave of the coronavirus disease 2019(COVID-19)pandemic.Materials and Methods:A descriptive,cross-sectional study was carried out among 117 OPD nurses between October and December 2021 using a purposive sampling technique.Two self-reported standardized scales,the Smartphone Addiction Scale-ShortVersion and Insomnia Severity Index were used.Kolmogorov–Smirnov test,Mann–Whitney U,and Kruskal–Wallis Htest were used.Pearson’s correlation and Scatter plot were used to determine the relationship between the study variables.A stepwise multiple linear regression analysis was also performed.Results:The majority of participants had slight smartphone addiction(78.6%)and suffered from sub-threshold to severe forms of insomnia(73.5%).A significant mild positive correlation was found between smartphone addiction and insomnia(r=0.195,P<0.05).Stepwise multivariate logistic regression analysis predicted factors such as female gender and exposure to smartphones for more than 5 years influencing smartphone addiction.A strong influence of exposure to the smartphone for more than 5 years was found on insomnia severity.Conclusion:Smartphone addiction and insomnia were identified problems among nurses working in the OPD after the second wave of the COVID-19 pandemic,requiring an urgent need to identify and manage various factors responsible for smartphone addiction and insomnia such as female gender and years of exposure to smartphones.

Systematic Review of Literature for Smartphones Technology Acceptance Using Unified Theory of Acceptance and Use of Technology Model (UTAUT)

Systematic Review and Meta-analysis are techniques which attempt to associate the findings from similar studies and deliver quantitative summaries of the research literature [1]. The Systematic review of research literature identifies the common research methods, research design, sample size, parameters used, survey instruments, etc. used by the group of researchers. This study intends to fulfill this purpose in order to identify common research mythologies, dependent variables, sample sizes, moderators and mediators used in the field of analysing technology adoption based studies that utilizes the UTAUT2 model. This research collected over 59 published articles and conducted descriptive analytics. The results have revealed performance expectancy/perceived usefulness, trust and habit as the best predictors of consumer behavioural intentions towards the adoption of mobile application. Behavioural intention was the best predictor of use behaviour among the 57 articles selected. 274 was the mean sample size of research with 25 mean questionnaire items. SPSS and AMOS were the most common softwares used in all 57 studies, and 32 of those studies used UTAUT1 model while 14 researches incorporated the UTAUT2 model. There were also two promising predictors such as perceived risk on behavioural intention and habit on use behaviour.

Measurement of cervical range of motion using smartphone compass in the healthy people:a cross-sectional pilot study

Objective:This study evaluates the reliability of smartphone compass software in measuring the cervical range of motion in healthy people.Methods:We selected 40 healthy intern college students from Tianjin Hospital from June to August 2022 to participate in this study.Two physiotherapists used a smartphone(iPhone 11256 Gb(model A2223))compass software to measure six directions of motion of the cervical spine in 40 subjects in a total of 3 rounds each.The intraclass correlation coefficient was used to compare the reliability intra-group,and the Pearson correlation coefficient was also used to compare the correlation between groups,with P<0.05 being statistically significant.Results:The intraclass correlation coefficient showed good reliability(>0.5)in cervical range of motion(CROM),especially in cervical flexion and right rotation(>0.9).In the correlation comparison between the two groups,the Spearman comparison was used,and the six directions of the cervical spine were significantly correlated(P<0.05).Conclusion:The built-in compass software in smartphones has good reliability in measuring CROM in healthy people.

信息技术推动乡村绿色发展的要求、实践及趋势

信息成为数字、数据和产生价值是由信息技术的应用来实现的。我国乡村应用信息技术的硬体条件已经初步具备,应用信息技术的软体条件尚需作较大的改进。政府的主要职责是为乡村绿色发展和乡村生态系统保护应用信息技术提供更好的宏观政策环境和更多的信息技术产品,使信息技术成为乡村振兴、农业绿色发展的推动力,成为规范各利益相关者行为的评价工具,让农民手中的智能手机在推动乡村绿色发展方面发挥更大的作用。乡村应用信息技术的重点要由协调政府与农民的关系拓展到协调农民和市场的关系。信息技术的突破和信息技术服务的创新,是一个不断有新起点,不断出新人才的领域,它是年轻科技人员,包括大学生、研究生施展才华,为推动中国乡村和农业绿色发展作贡献的重要平台。

青少年智能手机成瘾对无意识拖延的影响:注意控制的中介作用和手机自我扩展的调节作用

采用问卷调查法,以武汉市两所高中866名学生为研究对象,考察青少年智能手机成瘾对其无意识拖延的影响及作用机制。结果发现:(1)智能手机成瘾正向预测无意识拖延;(2)注意控制在智能手机成瘾和无意识拖延间起部分中介作用;(3)手机自我扩展调节了智能手机成瘾对注意控制的影响。具体而言,智能手机成瘾对高手机自我扩展的青少年分心抵抗的负面影响更大。结果支持拖延的元认知模型,提供了一定的理论和现实启发。

智能手机GNSS数据质量分析与随机模型建立

为提高移动智能终端定位精度,利用小米8和华为Mate20手机采集多个卫星系统的观测数据并进行质量分析,通过不同卫星系统的伪距残差分别对信噪比模型参数进行估计,最后进行静态伪距单点定位实验。结果表明,手机端观测的BDS和GPS伪距精度接近,且高于GLONASS;相较于高度角,伪距残差与信噪比的相关性更强;在信噪比模型参数估计中,小米8拟合效果优于华为Mate20,BDS拟合效果差于其他卫星系统;参数拟合后的信噪比模型在水平和高程方向的定位精度分别为2.37 m和8.38 m,相较于高度角模型,定位精度提升约10%。

GNSS基准站信噪比联合卫星系统综合定权的手机RTK定位

随着智能手机芯片和天线性能的不断提升,基于手机的高精度定位逐渐引起学术界和工业界的广泛关注.相较于单点定位,实时动态(real-time kinematic,RTK)定位通常表现出更高的定位性能.然而,目前对于手机RTK定位的随机模型的研究仍相对较少.因此,文中以小米8为例,重点探讨了智能手机在多系统RTK定位情境下的随机模型.研究结果表明:不同系统的卫星伪距噪声存在显著差异,而相位噪声的差异则较小.基于此,本文提出了一种基站信噪比联合卫星系统间综合定权的随机模型.相对于传统的信噪比模型更准确地考虑了不同系统之间的权重分配.并在开阔和遮挡条件下进行了静态和动态实验.结果表明:相比于传统的信噪比模型,新模型在静态开阔、静态遮挡、动态开阔和动态遮挡下三维方向的RTK定位精度分别提升了18.7%、18.3%、3.3%和4.6%.

一种Wi-Fi RTT/数据驱动惯性导航行人室内定位方法

为了研究基于智能手机的行人室内定位方法,并提高其精度,本文提出了一种基于Wi-Fi往返时间(RTT)、惯性测量单元(IMU)的定位系统。该方法主要包括3部分:(1)使用扩展卡尔曼滤波融合测距信息的Wi-Fi RTT室内定位方法;(2)适用于多手机使用模式的航位推算方法,该方法基于长短时记忆模型(LSTM)建立神经网络模型,预测行人运动速度及航向;(3)基于误差状态卡尔曼滤波的Wi-Fi RTT/数据驱动惯性导航融合定位方法,进一步提高定位精度。试验结果表明,与单一的基于Wi-Fi RTT方法和数据驱动惯性导航方法相比,本文方法的平均定位精度提升了10%~20%。

荧光探针合成与可视化检测三硝基苯酚综合实验开发

根据待测物2,4,6-三硝基苯酚(TNP)的性质,以香豆素为荧光团、噻二唑为识别基团,调整共轭体系和电子流向,设计具有绿色荧光的有机小分子探针,实现特异性检测与可视化识别。引导学生深入探索化学学科的内在联系,掌握基础知识的同时了解学科领域发展。设置智能手机检测模块,将科学研究与日常生活紧密联接,拉近学生与科学研究的距离,激发学生探索创新的意识。