Quality of experience (QoE) measures the overall perceived quality of mobile video delivery from subjective user experience and objective system performance. Current QoE prediction models have two ...main limitations: (1) insufficient consideration of the factors influencing QoE, and (2) limited studies on QoE models for acceptability prediction. In this paper, a set of novel acceptability-based QoE models, denoted as A-QoE, is proposed based on the results of comprehensive user studies on subjective quality acceptance assessments. The models are able to predict users' acceptability and pleasantness in various mobile video usage scenarios. Statistical nonlinear regression analysis has been used to build the models with a group of influencing factors as independent predictors, which include encoding parameters and bitrate, video content characteristics, and mobile device display resolution. The performance of the proposed A-QoE models has been compared with three well-known objective Video Quality Assessment metrics: PSNR, SSIM and VQM. The proposed A-QoE models have high prediction accuracy and usage flexibility. Future user-centred mobile video delivery systems can benefit from applying the proposed QoE-based management to optimize video coding and quality delivery strategies.
The use of mobile apps for health and well being promotion has grown exponentially in recent years. Yet, there is currently no app-quality assessment tool beyond "star"-ratings.
The objective of this ...study was to develop a reliable, multidimensional measure for trialling, classifying, and rating the quality of mobile health apps.
A literature search was conducted to identify articles containing explicit Web or app quality rating criteria published between January 2000 and January 2013. Existing criteria for the assessment of app quality were categorized by an expert panel to develop the new Mobile App Rating Scale (MARS) subscales, items, descriptors, and anchors. There were sixty well being apps that were randomly selected using an iTunes search for MARS rating. There were ten that were used to pilot the rating procedure, and the remaining 50 provided data on interrater reliability.
There were 372 explicit criteria for assessing Web or app quality that were extracted from 25 published papers, conference proceedings, and Internet resources. There were five broad categories of criteria that were identified including four objective quality scales: engagement, functionality, aesthetics, and information quality; and one subjective quality scale; which were refined into the 23-item MARS. The MARS demonstrated excellent internal consistency (alpha = .90) and interrater reliability intraclass correlation coefficient (ICC = .79).
The MARS is a simple, objective, and reliable tool for classifying and assessing the quality of mobile health apps. It can also be used to provide a checklist for the design and development of new high quality health apps.
This study examined the feasibility of a non-laboratory approach that uses machine learning on multimodal sensor data to predict relative physical activity (PA) intensity. A total of 22 participants ...completed up to 7 PA sessions, where each session comprised 5 trials (sitting and standing, comfortable walk, brisk walk, jogging, running). Participants wore a wrist-strapped sensor that recorded heart-rate (HR), electrodermal activity (Eda) and skin temperature (Temp). After each trial, participants provided ratings of perceived exertion (RPE). Three classifiers, including random forest (RF), neural network (NN) and support vector machine (SVM), were applied independently on each feature set to predict relative PA intensity as low (RPE ≤ 11), moderate (RPE 12-14), or high (RPE ≥ 15). Then, both feature fusion and decision fusion of all combinations of sensor modalities were carried out to investigate the best combination. Among the single modality feature sets, HR provided the best performance. The combination of modalities using feature fusion provided a small improvement in performance. Decision fusion did not improve performance over HR features alone. A machine learning approach using features from HR provided acceptable predictions of relative PA intensity. Adding features from other sensing modalities did not significantly improve performance.
This study aimed to identify key drivers behind workers’ satisfaction, perceived productivity, and health in open-plan offices while at the same time understanding design similarities shared by ...high-performance workspaces. Results from a dataset comprising a total of 8827 post-occupancy evaluation (POE) surveys conducted in 61 offices in Australia and a detailed analysis of a subset of 18 workspaces (n = 1949) are reported here. Combined, the database-level enquiry and the subset analysis helped identifying critical physical environment-related features with the highest correlation scores for perceived productivity, health, and overall comfort of the work area. Dataset-level analysis revealed large-size associations with spatial comfort, indoor air quality, building image and maintenance, noise distraction and privacy, visual comfort, personal control, and connection to the outdoor environment. All high-performance, open-plan offices presented a human-centered approach to interior design, purposely allocated spaces to support a variety of work-related tasks, and implemented biophilic design principles. These findings point to the importance of interior design in high-performance workspaces, especially in relation to open-plan offices.
ABSTRACTCommunity food security is vital to creating inclusive, safe, resilient, and sustainable cities with zero hunger, in alignment with the United Nations' Sustainable Development. Local food ...economies are fundamental to community food security, but there is little knowledge of how they adapt to disruptions, such as pandemics and natural disasters. Monitoring local food market access during disasters is essential to ensuring local food economies are not accidentally impacted by policy decisions. This study established a methodological framework to monitor local food market access, integrating secondary mobility data and the novel use of readily available analytical tools. The validity of the approach was tested at two local food markets in Southeast Queensland, Australia, during disruptive events. The results show that the 2019 bushfires and the COVID-19 pandemic heavily impacted the number of visits to both markets. The findings show mobile phone data can act as a useful proxy for directly observing visitation patterns, providing insights that can be combined with information about online purchasing and delivery services. The method can be used immediately to monitor and evaluate local food economies during and after disasters. Telecommunications companies can aid disaster response and recovery by sharing mobility data for policy decision support.
Purpose
The purpose of this paper is to map and describe findings from research conducted in workspaces designed to support activity-based working (ABW) over the past 10 years (2010–2020) with a view ...of informing post-COVID workplaces of the positive and negative attributes of ABW.
Design/methodology/approach
Scopus was used as the search engine for this review. Papers which reported findings related to ABW and performed field study in ABW workspaces with adult occupants were included. Out of the 442 initial papers, 40 papers were included following iterative title and abstract and full text review process and consideration of inclusion and exclusion criteria. These papers were divided into three groupings (organizational, human and physical environment) based on their major focus. Positive and negative effects of ABW environments on occupants are discussed within these three topics in consideration of the implications for the post-COVID workplace.
Findings
Although the included studies were inclined to be either more positive (i.e. interior design) or negative (i.e. indoor environmental quality, productivity, distraction and privacy) in relation to various attributes of ABW, no single effect of ABW environments on occupants was in full agreement between the studies. The shortcomings of ABW environments are more related to how this way of working is implemented and how occupants use it, rather than the concept itself. A partial uptake of ABW leads to occupants’ dissatisfaction, lower productivity and lower well-being, while a holistic approach increases the chance of success. It is hypothesised that many currently reported negative aspects of the ABW concept might diminish overtime as ABW evolves and as new challenges arise. A continuous post-occupancy evaluation after relocation to an ABW-supportive environment can inform the organization about the changing needs and preference of the occupants; hence, the organization can tailor the ABW solution to the arising needs. The inter-connection between the three key ABW pillars (organizational, human and physical environment) is crucial to the success of this concept specifically in the context of the post-COVID-19 workplace.
Originality/value
This paper highlights the key shortcomings and limitations of studies produced over the past decade and identifies keys gaps in the current body of literature. It provides a new insight on how findings related to open-plan offices designed to support ABW can be categorized on the three big heading of organizational, physical and human-related aspects, and further investigates the positive and negatives outcomes reported on ABW under these headings. It also discusses how the findings arising from this literature review can inform the post-COVID workplace.
Chronic diseases within Indigenous communities constitute the most compelling ill-health burdens and treatment inequalities, particularly in rural and remote Australia. In response to these vital ...issues, a systematic literature review of the adoption of wearable, Artificial Intelligence-driven, electrocardiogram sensors, in a telehealth Internet of Medical Things (IoMT) context was conducted to scale up rural Indigenous health. To this end, four preselected scientific databases were chosen for data extraction to align with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) technique. From the initially collected (n=4436) articles, a total of 32 articles were analysed, being synthesised from the review inclusion criteria, maintaining strict eligibility and eliminating duplicates. None of the various studies found on this innovative healthcare intervention has given a comprehensive picture of how this could be an effective method of care dedicated to rural Indigenous communities with cardiovascular diseases (CVDs). Herein, we presented the unique concepts of IoMT-driven wearable biosensors tailored for rural indigenous cardiac patients, their clinical implications, and cardiovascular disease management within the telehealth domain. This work contributes to understanding the adoption of wearable IoMT sensor-driven telehealth model, highlighting the need for real-time data from First Nations patients in rural and remote areas for CVD prevention. Pertinent implications, research impacts, limitations and future research directions are endorsed, securing long-term Wearable IoMT sensor-driven telehealth sustainability.
As multimedia-enabled mobile devices such as smart phones and tablets are becoming the day-to-day computing device of choice for users of all ages, everyone expects that all mobile multimedia ...applications and services should be as smooth and as high-quality as the desktop experience. The grand challenge in delivering multimedia to mobile devices using the Internet is to ensure the quality of experience that meets the users' expectations, within reasonable costs, while supporting heterogeneous platforms and wireless network conditions. This book aims to provide a holistic overview of the current and future technologies used for delivering high-quality mobile multimedia applications, while focusing on user experience as the key requirement. The book opens with a section dealing with the challenges in mobile video delivery as one of the most bandwidth-intensive media that requires smooth streaming and a user-centric strategy to ensure quality of experience. The second section addresses this challenge by introducing some important concepts for future mobile multimedia coding and the network technologies to deliver quality services. The last section combines the user and technology perspectives by demonstrating how user experience can be measured using case studies on urban community interfaces and Internet telephones.
•A review of state-of-the-art feature extraction methods from electroencephalogram signals.•A new framework using evolutionary algorithms to find the most optimal features set and ...channels.•Comprehensive experimental results based on two public datasets and one newly collected dataset.
There is currently no standard or widely accepted subset of features to effectively classify different emotions based on electroencephalogram (EEG) signals. While combining all possible EEG features may improve the classification performance, it can lead to high dimensionality and worse performance due to redundancy and inefficiency. To solve the high-dimensionality problem, this paper proposes a new framework to automatically search for the optimal subset of EEG features using evolutionary computation (EC) algorithms. The proposed framework has been extensively evaluated using two public datasets (MAHNOB, DEAP) and a new dataset acquired with a mobile EEG sensor. The results confirm that EC algorithms can effectively support feature selection to identify the best EEG features and the best channels to maximize performance over a four-quadrant emotion classification problem. These findings are significant for informing future development of EEG-based emotion classification because low-cost mobile EEG sensors with fewer electrodes are becoming popular for many new applications.
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