Exploring user engagement with real-time verbal feedback from an exoskeleton-based virtual exercise coach.

Objective: Engaging users during physical exercise is crucial for fostering long-term commitment, however, sustaining that engagement remains a significant challenge. This study explores the design of a voice-enabled exoskeleton-based virtual exercise coach (VEC) that provides real-time verbal feedback to enhance user engagement. The objectives of this study are twofold: (i) to compare user engagement with real-time verbal feedback from both VEC and human exercise coach (HEC) during physical exercise, and (ii) to understand users' perceptions and gather their recommendations for improving future VEC technologies.

Emerging Telepresence Technologies in Hybrid Learning Environments.

The last several years have seen a strong growth of telerobotic technologies with promising implications for many areas of learning. HCI has contributed to these discussions, mainly with studies on user experiences and user interfaces of telepresence robots. However, only a few telerobot studies have addressed everyday use in real-world learning environments.

Machine Learning for Motor Imagery Wrist Dorsiflexion Prediction in Brain-Computer Interface Assisted Stroke Rehabilitation.

Stroke is a life-changing event that can affect the survivors' physical, cognitive and emotional state. Stroke care focuses on helping the survivors to regain their strength; recover as much functionality as possible and return to independent living through rehabilitation therapies. Automated training protocols have been reported to improve the efficiency of the rehabilitation process.

The impact of visuospatial perception on distance judgment and depth perception in an Augmented Reality environment in patients after stroke: an exploratory study.

Background: Augmented Reality (AR)-based interventions are applied in neurorehabilitation with increasing frequency. Depth perception is required for the intended interaction within AR environments. Until now, however, it is unclear whether patients after stroke with impaired visuospatial perception (VSP) are able to perceive depth in the AR environment.

Mental fatigue measurement using eye metrics: A systematic literature review.

Mental fatigue measurement techniques utilize one or a combination of the cognitive, affective, and behavioral responses of the body. Eye-tracking and electrooculography, which are used to compute eye-based features, have gained momentum with increases in accuracy and robustness of the lightweight equipment emerging in the markets and can be used for objective and continuous assessment of mental fatigue. The main goal of this systematic review was to summarize the various eye-based features that have been used to measure mental fatigue and explore the relation of eye-based features to mental fatigue.

Six Key Advantages and Disadvantages of Working from Home in Europe during COVID-19.

The number of people working from home (WFH) increased radically during the coronavirus (COVID-19) pandemic. The purpose of this study was therefore to investigate people's experiences of WFH during the pandemic and to identify the main factors of advantages and disadvantages of WFH. Data from 29 European countries on the experiences of knowledge workers ( = 5748) WFH during the early stages of lockdown (11 March to 8 May 2020) were collected.

Mental fatigue prediction during eye-typing.

Mental fatigue is a common problem associated with neurological disorders. Until now, there has not been a method to assess mental fatigue on a continuous scale. Camera-based eye-typing is commonly used for communication by people with severe neurological disorders.

Pupillary measurement during an assembly task.

We conducted an empirical study of 57 children using a printed Booklet and a digital Tablet instruction for LEGO construction while they wore a head-mounted gaze tracker. Booklets caused a particularly strong pupil dilation when encountered as the first media. Subjective responses confirmed the booklet to be more difficult to use.

A gaze interactive assembly instruction with pupillometric recording.

This paper presents a study of a gaze interactive digital assembly instruction that provides concurrent logging of pupil data in a realistic task setting. The instruction allows hands-free gaze dwells as a substitute for finger clicks, and supports image rotation as well as image zooming by head movements. A user study in two LEGO toy stores with 72 children showed it to be immediately usable by 64 of them.

Low Cost and Flexible UAV Deployment of Sensors.

This paper presents a platform for airborne sensor applications using low-cost, open-source components carried by an easy-to-fly unmanned aircraft vehicle (UAV). The system, available in open-source , is designed for researchers, students and makers for a broad range of exploration and data-collection needs. The main contribution is the extensible architecture for modularized airborne sensor deployment and real-time data visualisation.

Population control charts for population data.

Healthcare managers are beginning to collect full population data, rather than sample data, on some patient and performance measures. For example, hospitals and healthcare systems already gather and store comprehensive data on admissions, ambulatory encounters, and other procedures. And as the electronic medical record is more widely used, complete population data will be collected on an even wider range of clinical measures, such as blood pressure and Laboratory values, in both inpatient and outpatient settings.

CAN'T MISS: conquer any number task by making important statistics simple. Part 8. Statistical process control: n, np, c, u control charts.

A p control chart is used to monitor a proportion from a binomial variable that reflects the output of a process. The p control chart is a graph that includes serial sample proportions (p) as the variables of interest, a centerline that represents the overall proportion of the samples (p), and upper control limits (UCLs) and Lower control Limits (LCLs) that represent three standard errors (SEp) above and below the centerline. We use a p control chart to estimate with 99.

CAN'T MISS: conquer any number task by making important statistics simple. Part 7. Statistical process control: x-s control charts.

Statistical process control (SPC) can be thought of as the frequent monitoring of processes using inferential statistics. The feature that distinguishes SPC from the typical use of inferential statistics for analyzing populations is that in the former frequent samples are taken over time, whereas in inferential statistics a single sample is generaLLy taken before and after some intervention or treatment. An x-s control chart is used to monitor a continuous variable that reflects the output of a process.

Can't miss: conquer any number task by making important statistics simple. Part 6. Tests of statistical significance (z test statistic, rejecting the null hypothesis, p value), t test, z test for proportions, statistical significance versus meaningful difference.

Healthcare quality professionals need to understand and use inferential statistics to interpret sample data from their organizations. Since in quality improvement and healthcare research studies all the data from a population often are not available, investigators take samples and make inferences about that population using inferential statistics. This series of six articles will give readers an understanding of the concepts of inferential statistics as well as the specific tools for calculating confidence intervals and tests of statistical significance for samples of data.

Can't miss: conquer any number task by making important statistics simple. Part 5. Comparing two confidence intervals, standard error of the difference between two means and between two proportions.

Healthcare quality professionals need to understand and use inferential statistics to interpret sample data from their organizations. Since in quality improvement and healthcare research studies all the data from a population often are not available, investigators take samples and make inferences about that population using inferential statistics. This series of six articles will give readers an understanding of the concepts of inferential statistics as well as the specific tools for calculating confidence intervals and tests of statistical significance for samples of data.

Can't miss: conquer any number task by making important statistics simple. Part 4. Confidence intervals with t distributions, standard error, and confidence intervals for proportions.

Healthcare quality professionals need to understand and use inferential statistics to interpret sample data from their organizations. Since in quality improvement and healthcare research studies, all the data from a population often are not available, investigators take samples and make inferences about that population using inferential statistics. This series of six articles will give readers an understanding of the concepts of inferential statistics, as well as the specific tools for calculating confidence intervals and tests of statistical significance for samples of data.

CAN'T MISS--conquer any number task by making important statistics simple. Part 3. Standard error, estimation, and confidence intervals.

Healthcare quality improvement professionals need to understand and use inferential statistics to interpret sample data from their organizations. In quality improvement and healthcare research studies all the data from a population often are not available, so investigators take samples and make inferences about the population by using inferential statistics. This three-part series will give readers an understanding of the concepts of inferential statistics as well as the specific tools for calculating confidence intervals for samples of data.

CAN'T MISS--conquer any number task by making important statistics simple. Part 2. Probability, populations, samples, and normal distributions.

Healthcare quality improvement professionals need to understand and use inferential statistics to interpret sample data from their organizations. In quality improvement and healthcare research studies all the data from a population often are not available, so investigators take samples and make inferences about the population by using inferential statistics. This three-part series will give readers an understanding of the concepts of inferential statistics as well as the specific tools for calculating confidence intervals for samples of data.

CAN'T MISS--conquer any number task by making important statistics simple. Part 1. Types of variables, mean, median, variance, and standard deviation.

Healthcare quality improvement professionals need to understand and use inferential statistics to interpret sample data from their organizations. In quality improvement and healthcare research studies all the data from a population often are not available, so investigators take samples and make inferences about the population by using inferential statistics. This three-part series will give readers an understanding of the concepts of inferential statistics as well as the specific tools for calculating confidence intervals for samples of data.