Effectiveness of Mobile Technology in Managing Fatigue: Balert App (original) (raw)
Related papers
FATIGUE RISK MANAGEMENT USING MOBILE APPLICATION
Abstract: Safety and productivity in the work-place are intimately related to worker health. A workplace may have chemical, physical, biological, and/or psychosocial hazards that have the potential to impact physical and psychological well-being. If the development of the iPhone application Crew Alert. The application is intended for use by pilots as a tool for assessment, logging and reporting of fatigue, to increase safety in the air. It also keeps track of the user's schedule, and sleep pattern. The application is developed from being only a iPhone application to being a fully universal iOS application supporting all iOS devices and screen orientations. Focus of development lies on usability and feature set, using the Goal Directed Design approach in combination with the Scrum framework. As development progressed the focus was put towards lowering the learning curve and creating more substance for the users in everyday use. Keyword: Crew Alert, iphone, ios, security. Title: FATIGUE RISK MANAGEMENT USING MOBILE APPLICATION Author: Ms. A. Sivasankari, N. Karthika International Journal of Computer Science and Information Technology Research ISSN 2348-120X (online), ISSN 2348-1196 (print) Research Publish Journals
Analysis of Human Performance as a Measure of Mental Fatigue
Lecture Notes in Computer Science, 2014
In our daily life, we often have the feeling of being exhausted due to mental or physical work, and a sense of performance degradation in the execution of simple tasks. The maximum capacity of operation and performance of an individual, whether physical or mental, usually also decreases gradually as the day progresses. The loss of these resources is linked to the onset of fatigue, which is particularly noticeable in long and demanding tasks or repetitive jobs. However, good management of the working time and eort invested in each task, as well as the eect of breaks at work, can result in better performance and better mental health, delaying the eects of fatigue. This paper details a non-invasive approach on the monitoring of fatigue of a human being, based on the analysis of the performance of his interaction with the computer.
Frontiers in Physiology
This study describes a beta version of a mobile application (app) that focuses on preventing chronic fatigue in Czech youth athletes. The first version of the SmartTraining app was developed for athletes as a way to prevent chronic fatigue via alertness and education. For alertness, a multistage process was developed using a combination of parameters about training responses, such as tiredness, well-being, heart rate, energy balance and psychological, and health-related aspects. According to the combination of the multistage parameter outcomes, the algorithm classifies the risk of fatigue based on semaphore light: green corresponds to low, yellow to moderate and red to high risk. The education presented in the app consisted of written and “animated videos” material about the variables involved in training, such as training demands and athletes’ responses, regeneration, nutrition and communication between athletes, coaches, and parents. Subsequently, a beta version of the app was cre...
Journal of Rare Diseases
Purpose Barth syndrome (BTHS) is a rare genetic disorder characterized by skeletal myopathy, cardiomyopathy, and exercise intolerance due to early fatigue. The purpose of this study was to test the feasibility and validity of a new phone application designed to capture multi-dimensional aspects of fatigue across the lifespan. The specific study aims were to (1) assess the feasibility of using the app to record perceived fatigue levels in real-time, (2) evaluate discriminant validity by assessing if the app can differentiate between those with and without BTHS, and (3) content validity by assessing the relationship between perceived energy levels and actual energy expenditure. Methods Eighteen participants with BTHS and 18 age-matched control participants completed the study. The participants wore an activity tracker for 14 days and were prompted to respond to an Android app to report their fatigue levels 6 × /day. Statistical analysis was completed to examine perceived fatigue and t...
Detection of mental fatigue state with wearable ECG devices
International Journal of Medical Informatics, 2018
Overwork-related disorders, such as cerebrovascular/cardiovascular diseases (CCVD) and mental disorders due to overwork, are a major occupational and public health issue worldwide, particularly in East Asian countries. Since wearable smart devices are inexpensive, convenient, popular and widely available today, we were interested in investigating the possibility of using wearable smart electrocardiogram (ECG) devices to detect the mental fatigue state. In total, 35 healthy participants were recruited from a public university in East China. Throughout the entire experiment, each participant wore a wearable device that was further linked to a smartphone to upload the data based on Bluetooth transmission. To manipulate the fatigue state, each participant was asked to finish a quiz, which lasted for approximately 80 min, with 30 logical referential and computing problems and 25 memory tests. Eight heart rate variability (HRV) indicators namely NN.mean (mean of normal to normal interval), rMSSD (root mean square of successive differences), PNN50 (the proportion of NN50 divided by total number of NNs), TP (total spectral power), HF (high frequency from 0.15 Hz to 0.4 Hz), LF (low frequency from 0.04 Hz to 0.15 Hz), VLF (very low frequency from 0.0033 Hz to 0.04 Hz) and the LF/HF ratio were collected at intervals of 5 min throughout the entire experiment. After the feature selection was performed, six indicators remained for further analysis, which were the NN.mean, rMSSD, PNN50, TP, LF, and VLF. Four algorithms, support vector machine (SVM), K-nearest neighbor (KNN), naïve Bayes (NB), and logistic regression (LR), were used to build classifiers that automatically detected the fatigue state. The best performance was achieved by KNN, which had a CV accuracy of 75.5%. The NN.mean, PNN50, TP and LF were the most important HRV indicators for mental fatigue detection. KNN performed the best among the four algorithms and had an average CV accuracy of 65.37% for all of the possible feature combinations.
2011
We developed and tested a neuroergonomic smart phone application called Mind Metrics, where the goal was to evaluate vigilance and working memory capacity under naturalistic conditions. Naturalistic data collection meets a requirement of neuroergonomics practice because such data can make predictions about human performance during work related activities. Yet naturalistic data need to be validated against data obtained in controlled laboratory environments. Accordingly, we tested participants on the same cognitive tasks using both a smart phone and a desktop computer. Tasks included a psychomotor vigilance task (PVT), spatial discrimination vigilance task (SDVT), and two working memory tasks, a color nback task (CNB), and spatial n-back task (SNB). Vigilance decrements were detected for both the simultaneous vigilance task (PVT) and the successive vigilance task (SDVT). Both devices were sensitive to the detection of the vigilance decrement. The results show that the naturalistic pl...
Monitoring Mental Fatigue through the Analysis of Keyboard and Mouse Interaction Patterns
Lecture Notes in Computer Science, 2013
In our living, we often have a sense of being tired due to a mental or physical work, plus a feeling of performance degradation even in the accomplishment of simple tasks. However, these mental states are often not consciously felt or are ignored, an attitude that may result in human failures, errors and even in the occurrence of health problems or on a decrease in the quality of life. States of fatigue may be detected with a close monitoring of some indicators, such as productivity, performance or even the health states. In this work it is proposed a model and a prototype to detect and monitor fatigue based on some of these items. We focus specifically on mental fatigue, a key factor in an individual's performance. With this approach we aim to develop leisure and work context-aware environments that may improve the quality of life and the individual performance of any human being.
Contactless Physiological Assessment of Mental Workload During Teleworking-like Task
Communications in Computer and Information Science, 2020
Human physiological parameters have been proven as reliable and objective indicators of user's mental states, such as the Mental Workload. However, standard methodologies for evaluating physiological parameters generally imply a certain grade of invasiveness. It is largely demonstrated the relevance of monitoring workers to improve their working conditions. A contactless approach to estimate workers' physiological parameters would be highly suitable because it would not interfere with the working activities and comfort of the workers. Additionally, it would be very appropriate for teleworking settings. In this paper, participants' facial videos were recorded while dealing with arithmetic tasks with the aims to 1) evaluate the possibility to estimate their Heart Rate (HR) through facial video analysis, and 2) assess their mental workload under the different experimental conditions. The HR was also estimated through last-generation smartwatches. The results demonstrated that there was no difference between the HR estimated via the contactless technique and smartwatches, and how it was possible to discriminate the two mental workload levels by employing the proposed methodology.