scholarly journals Machine-Learning-Based Detection of Craving for Gaming Using Multimodal Physiological Signals: Validation of Test-Retest Reliability for Practical Use

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3475
Author(s):  
Hodam Kim ◽  
Laehyun Kim ◽  
Chang-Hwan Im

Internet gaming disorder in adolescents and young adults has become an increasing public concern because of its high prevalence rate and potential risk of alteration of brain functions and organizations. Cue exposure therapy is designed for reducing or maintaining craving, a core factor of relapse of addiction, and is extensively employed in addiction treatment. In a previous study, we proposed a machine-learning-based method to detect craving for gaming using multimodal physiological signals including photoplethysmogram, galvanic skin response, and electrooculogram. Our previous study demonstrated that a craving for gaming could be detected with a fairly high accuracy; however, as the feature vectors for the machine-learning-based detection of the craving of a user were selected based on the physiological data of the user that were recorded on the same day, the effectiveness of the reuse of the machine learning model constructed during the previous experiments, without any further calibration sessions, was still questionable. This “high test-retest reliability” characteristic is of importance for the practical use of the craving detection system because the system needs to be repeatedly applied to the treatment processes as a tool to monitor the efficacy of the treatment. We presented short video clips of three addictive games to nine participants, during which various physiological signals were recorded. This experiment was repeated with different video clips on three different days. Initially, we investigated the test-retest reliability of 14 features used in a craving detection system by computing the intraclass correlation coefficient. Then, we classified whether each participant experienced a craving for gaming in the third experiment using various classifiers—the support vector machine, k-nearest neighbors (kNN), centroid displacement-based kNN, linear discriminant analysis, and random forest—trained with the physiological signals recorded during the first or second experiment. Consequently, the craving/non-craving states in the third experiment were classified with an accuracy that was comparable to that achieved using the data of the same day; thus, demonstrating a high test-retest reliability and the practicality of our craving detection method. In addition, the classification performance was further enhanced by using both datasets of the first and second experiments to train the classifiers, suggesting that an individually customized game craving detection system with high accuracy can be implemented by accumulating datasets recorded on different days under different experimental conditions.

2011 ◽  
Vol 23 (4) ◽  
pp. 549-559 ◽  
Author(s):  
Abdou Temfemo ◽  
Thierry Lelard ◽  
Christopher Carling ◽  
Samuel Honoré Mandengue ◽  
Mehdi Chlif ◽  
...  

This study investigated the feasibility and reliability of a 12 × 25-m repeated sprint test with sprints starting every 25-s in children aged 6–8 years (36 boys, 41 girls). In all subjects, total sprint time (TST) demonstrated high test-retest reliability (ICC: r = .98; CV: 0.7% (95% CI: 0.6–0.9)). While sprint time varied over the 12 sprints in all subjects (p < .001) with a significant increase in time for the third effort onwards compared with the first sprint (p < .001), there was no difference in performance between genders. In all subjects, TST decreased with age (p < .001) and was accompanied by an increase in estimated anaerobic power (p < .001) but also in sprint time decrement percentage (p < .001). Gender did not effect these changes. The present study demonstrates the practicability and reliability of a repeated sprint test with respect to age and gender in young children.


Author(s):  
José Pino-Ortega ◽  
Petrus Gantois ◽  
Amaia Méndez ◽  
Markel Rico-González

Ultra-wide band (UWB) technology has become one of the most promising technologies of the future. It seems that the positioning of the antenna set influences the accuracy of the player’s performance. This study was aimed to assess the influence of two different antenna positioning system shapes: (i) octagonal installation and (ii) circular installation. A UWB technology was used to track a healthy and well-trained athlete’s (age: 38 years, mass: 76.34 kg, and height: 1.75 m) positioning. Overall, the data measured showed high accuracy in both shape setups in all trajectories assessed in static and dynamic conditions for all speed thresholds. However, the distance covered during jogging and sprinting showed poor accuracy for both shape setups. Moreover, the data measured showed high test-retest reliability and inter-device agreement in the static condition, regardless of the antenna setup shape. In conclusion, both the octagonal and circular antenna setup shape provided accurate data, but the measurement error associated within the setup shape seemed slightly different between the two systems.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zahra Barakchian ◽  
Anjali Raja Beharelle ◽  
Todd A. Hare

AbstractFood choice paradigms are commonly used to study decision mechanisms, individual differences, and intervention efficacy. Here, we measured behavior from twenty-three healthy young adults who completed five repetitions of a cued-attribute food choice paradigm over two weeks. This task includes cues prompting participants to explicitly consider the healthiness of the food items before making a selection, or to choose naturally based on whatever freely comes to mind. We found that the average patterns of food choices following both cue types and ratings about the palatability (i.e. taste) and healthiness of the food items were similar across all five repetitions. At the individual level, the test-retest reliability for choices in both conditions and healthiness ratings was excellent. However, test-retest reliability for taste ratings was only fair, suggesting that estimates about palatability may vary more from day to day for the same individual.


2012 ◽  
Vol 112 (8) ◽  
pp. 1248-1257 ◽  
Author(s):  
Lee Friedman ◽  
Thomas E. Dick ◽  
Frank J. Jacono ◽  
Kenneth A. Loparo ◽  
Amir Yeganeh ◽  
...  

In this work, cardio-ventilatory coupling (CVC) refers to the statistical relationship between the onset of either inspiration (I) or expiration (E) and the timing of heartbeats (R-waves) before and after these respiratory events. CVC was assessed in healthy, young (<45 yr), resting, supine subjects ( n = 19). Four intervals were analyzed: time from I-onset to both the prior R-wave (R-to-I) and the following R-wave (I-to-R), as well as time from E-onset to both the prior R-wave (R-to-E) and following R-wave (E-to-R). The degree of coupling was quantified in terms of transformed relative Shannon entropy (tRSE), and χ2 tests based on histograms of interval times from 200 breaths. Subjects were studied twice, from 5 to 27 days apart, and the test-retest reliability of CVC measures was computed. Several factors pointed to the relative importance of the R-to-I interval compared with other intervals. Coupling was significantly stronger for the R-to-I interval, coupling reliability was largest for the R-to-I interval, and only tRSE for the R-to-I interval was correlated with height, weight, and body surface area. The high test-retest reliability for CVC in the R-to-I interval provides support for the hypothesis that CVC strength is a subject trait. Across subjects, a peak ∼138 ms prior to I-onset was characteristic of CVC in the R-to-I interval, although individual subjects also had earlier peaks (longer R-to-I intervals). CVC for the R-to-I interval was unrelated to two separate measures of respiratory sinus arrhythmia (RSA), suggesting that these two forms of coupling (CVC and RSA) are independent.


2020 ◽  
Vol 66 (4) ◽  
pp. 411-418
Author(s):  
Srividya N Iyer ◽  
Megan A Pope ◽  
Gerald Jordan ◽  
Greeshma Mohan ◽  
Heleen Loohuis ◽  
...  

Objectives: Views on who bears how much responsibility for supporting individuals with mental health problems may vary across stakeholders (patients, families, clinicians) and cultures. Perceptions about responsibility may influence the extent to which stakeholders get involved in treatment. Our objective was to report on the development, psychometric properties and usability of a first-ever tool of this construct. Methods: We created a visual weighting disk called ‘ShareDisk’, measuring perceived extent of responsibility for supporting persons with mental health problems. It was administered (twice, 2 weeks apart) to patients, family members and clinicians in Chennai, India ( N = 30, 30 and 15, respectively) and Montreal, Canada ( N = 30, 32 and 15, respectively). Feedback regarding its usability was also collected. Results: The English, French and Tamil versions of the ShareDisk demonstrated high test–retest reliability ( rs = .69–.98) and were deemed easy to understand and use. Conclusion: The ShareDisk is a promising measure of a hitherto unmeasured construct that is easily deployable in settings varying in language and literacy levels. Its clinical utility lies in clarifying stakeholder roles. It can help researchers investigate how stakeholders’ roles are perceived and how these perceptions may be shaped by and shape the organization and experience of healthcare across settings.


2010 ◽  
Vol 106 (3) ◽  
pp. 870-874 ◽  
Author(s):  
Paul B. Harris ◽  
John M. Houston

This study examined the reliability of the Revised Competitiveness Index by investigating the test-retest reliability, interitem reliability, and factor structure of the measure based on a sample of 280 undergraduates (200 women, 80 men) ranging in age from 18 to 28 years ( M = 20.1, SD = 2.1). The findings indicate that the Revised Competitiveness Index has high test-retest reliability, high interitem reliability, and a stable factor structure. The results support the assertion that the Revised Competitiveness Index assesses competitiveness as a stable trait rather than a dynamic state.


Author(s):  
Ahmad Iwan Fadli ◽  
Selo Sulistyo ◽  
Sigit Wibowo

Traffic accident is a very difficult problem to handle on a large scale in a country. Indonesia is one of the most populated, developing countries that use vehicles for daily activities as its main transportation.  It is also the country with the largest number of car users in Southeast Asia, so driving safety needs to be considered. Using machine learning classification method to determine whether a driver is driving safely or not can help reduce the risk of driving accidents. We created a detection system to classify whether the driver is driving safely or unsafely using trip sensor data, which include Gyroscope, Acceleration, and GPS. The classification methods used in this study are Random Forest (RF) classification algorithm, Support Vector Machine (SVM), and Multilayer Perceptron (MLP) by improving data preprocessing using feature extraction and oversampling methods. This study shows that RF has the best performance with 98% accuracy, 98% precision, and 97% sensitivity using the proposed preprocessing stages compared to SVM or MLP.


Author(s):  
Iqbal H. Sarker ◽  
Yoosef B. Abushark ◽  
Fawaz Alsolami ◽  
Asif Irshad Khan

Cyber security has recently received enormous attention in today&rsquo;s security concerns, due to the popularity of the Internet-of-Things (IoT), the tremendous growth of computer networks, and the huge number of relevant applications. Thus, detecting various cyber-attacks or anomalies in a network and building an effective intrusion detection system that performs an essential role in today&rsquo;s security is becoming more important. Artificial intelligence, particularly machine learning techniques, can be used for building such a data-driven intelligent intrusion detection system. In order to achieve this goal, in this paper, we present an Intrusion Detection Tree (&ldquo;IntruDTree&rdquo;) machine-learning-based security model that first takes into account the ranking of security features according to their importance and then build a tree-based generalized intrusion detection model based on the selected important features. This model is not only effective in terms of prediction accuracy for unseen test cases but also minimizes the computational complexity of the model by reducing the feature dimensions. Finally, the effectiveness of our IntruDTree model was examined by conducting experiments on cybersecurity datasets and computing the precision, recall, fscore, accuracy, and ROC values to evaluate. We also compare the outcome results of IntruDTree model with several traditional popular machine learning methods such as the naive Bayes classifier, logistic regression, support vector machines, and k-nearest neighbor, to analyze the effectiveness of the resulting security model.


2019 ◽  
Vol 19 (10) ◽  
pp. 136c
Author(s):  
Milena Dzhelyova ◽  
Giulia Dormal ◽  
Corentin Jacques ◽  
Caroline Michel ◽  
Christine Schiltz ◽  
...  

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