Guidelines for the design of haptic widgets

Haptic feedback has been shown to improve user performance in Graphical User Interface (GUI) targeting tasks in a number of studies. These studies have typically focused on interactions with individual targets, and it is unclear whether the performance increases reported will generalise to the more realistic situation where multiple targets are presented simultaneously. This paper addresses this issue in two ways. Firstly two empirical studies dealing with groups of haptically augmented widgets are presented. These reveal that haptic augmentations of complex widgets can reduce performance, although carefully designed feedback can result in performance improvements. The results of these studies are then used in conjunction with the previous literature to generate general design guidelines for the creation of haptic widgets

Beyond Robotic Wastelands of Time: Abandoned Pedagogical Agents and New Pedalled Pedagogies

Chatbots, known as pedagogical agents in educational settings, have a long history of use, beginning with Alan Turing’s work. Since then online chatbots have become embedded into the fabric of technology. Yet understandings of these technologies are inchoate and often untheorised. Integration of chatbots into educational settings over the past five years suggests an increase in interest in the ways in which chatbots might be adopted and adapted for teaching and learning. This article draws on historical literature and theories that to date have largely been ignored in order to (re)contextualise two studies that used responsive evaluation to examine the use of pedagogical agents in education. Findings suggest that emotional interactions with pedagogical agents are intrinsic to a user’s sense of trust, and that truthfulness, personalisation and emotional engagement are vital when using pedagogical agents to enhance online learning. Such findings need to be considered in the light of ways in which notions of learning are being redefined in the academy and the extent to which new literacies and new technologies are being pedalled as pedagogies in ways that undermine what higher education is, is for, and what learning means

Computer work and musculoskeletal disorders of the neck and upper extremity: A systematic review

<p>Abstract</p> <p>Background</p> <p>This review examines the evidence for an association between computer work and neck and upper extremity disorders (except carpal tunnel syndrome).</p> <p>Methods</p> <p>A systematic critical review of studies of computer work and musculoskeletal disorders verified by a physical examination was performed.</p> <p>Results</p> <p>A total of 22 studies (26 articles) fulfilled the inclusion criteria. Results show limited evidence for a causal relationship between computer work per se, computer mouse and keyboard time related to a diagnosis of wrist tendonitis, and for an association between computer mouse time and forearm disorders. Limited evidence was also found for a causal relationship between computer work per se and computer mouse time related to tension neck syndrome, but the evidence for keyboard time was insufficient. Insufficient evidence was found for an association between other musculoskeletal diagnoses of the neck and upper extremities, including shoulder tendonitis and epicondylitis, and any aspect of computer work.</p> <p>Conclusions</p> <p>There is limited epidemiological evidence for an association between aspects of computer work and some of the clinical diagnoses studied. None of the evidence was considered as moderate or strong and there is a need for more and better documentation.</p

Effect of walking surface, late-cueing, physiological characteristics of aging, and gait parameters on turn style preference in healthy, older adults.

Turning while walking is a crucial component of locomotion, often performed on irregular surfaces with little planning time. Turns can be difficult for some older adults due to physiological age-related changes. Two different turning strategies have been identified in the literature. During step turns, which are biomechanically stable, the body rotates about the outside limb, while for spin turns, generally performed with closer foot-to-foot distance, the inside limb is the main pivot point. Turning strategy preferences of older adults under challenging conditions remains unclear. The aim of this study was to determine how turning strategy preference in healthy older adults is modulated by surface features, cueing time, physiological characteristics of aging, and gait parameters. Seventeen healthy older adults (71.5 ± 4.2 years) performed 90° turns for two surfaces (flat, uneven) and two cue conditions (pre-planned, late-cue). Gait parameters were identified from kinematic data. Measures of lower-limb strength, balance, and reaction-time were also recorded. Generalized linear (logistic) regression mixed-effects models examined the effect of (1) surface and cuing, (2) physiological characteristics of ageing, and (3) gait parameters on turn strategy preference. Step turns were preferred when the condition was pre-planned (p < 0.001) (model 1) and when the gait parameters of stride regularity and maximum acceleration decreased (p = 0.010 and p = 0.039, respectively) (model 3). Differences in turn strategy selection under dynamic conditions ought to be evaluated in future fall-risk research and rehabilitation utilizing real-world activity monitoring.Published versio

Guidelines for the Design of Haptic Widgets

Haptic feedback has been shown to improve user performance in Graphical User Interface (GUI) targeting tasks in a number of studies. These studies have typically focused on interactions with individual targets, and it is unclear whether the performance increases reported will generalise to the more realistic situation where multiple targets are presented simultaneously. This paper addresses this issue in two ways. Firstly two empirical studies dealing with groups of haptically augmented widgets are presented. These reveal that haptic augmentations of complex widgets can reduce performance, although carefully designed feedback can result in performance improvements. The results of these studies are then used in conjunction with the previous literature to generate general design guidelines for the creation of haptic widgets. Keywords: Haptic, Desktop, GUI, Multi-target, Design guidelines

Predictors of whole-body vibration levels among urban taxi drivers

To identify a set of important WBV predictors that could be used to develop a statistical instrument for exposure assessment in a large epidemiologic study, a total of 432 WBV measures were taken from a sample of 247 male drivers in Taipei City, Taiwan. In accordance with the ISO 2631-1 (1997) methods, we measured the frequency-weighted vertical acceleration (z-axis) over drivers' seat surface, under conditions representing different types of rides (vacant vs. short vs. long) assigned to random destinations. Mixed effect models were used to analyse the WBV data including repeated measures. For this group of urban taxi drivers regularly exposed to WBV of low intensity (mean = 0.31 ms-2, ranging from 0.17 to 0.55 ms -2 r.m.s.), our analyses indicated that average driving speed was the primary predictor (p < 0.0001). As average driving speed increased, measured vertical acceleration increased in a quadratic-linear manner (p < 0.0001). Other WBV predictors, after adjusting for the effects of other covariates, included automobile manufacturer (p = 0.02), engine size (p = 0.04), body weight (p = 0.002), age (p = 0.02), use of seat cushion (p = 0.03), and traffic period (p = 0.02). Our study suggests that a similar statistical approach could be employed in future studies to improve the quality and efficiency of WBV exposure assessment in professional drivers

Machine learning algorithms can classify outdoor terrain types during running using accelerometry data

BACKGROUND: Running is a popular physical activity that benefits health; however, running surface characteristics may influence loading impact and injury risk. Machine learning algorithms could automatically identify running surface from wearable motion sensors to quantify running exposures, and perhaps loading and injury risk for a runner. RESEARCH QUESTION: (1) How accurately can machine learning algorithms identify surface type from three-dimensional accelerometer sensors? (2) Does the sensor count (single or two-sensor setup) affect model accuracy? METHODS: Twenty-nine healthy adults (23.3 ± 3.6 years, 1.8 ± 0.1 m, and 63.6 ± 8.5 kg) participated in this study. Participants ran on three different surfaces (concrete, synthetic, woodchip) while fit with two three-dimensional accelerometers (lower-back and right tibia). Summary features (n = 208) were extracted from the accelerometer signals. Feature-based Gradient Boosting (GB) and signal-based deep learning Convolutional Neural Network (CNN) models were developed. Models were trained on 90% of the data and tested on the remaining 10%. The process was repeated five times, with data randomly shuffled between train-test splits, to quantify model performance variability. RESULTS: All models and configurations achieved greater than 90% average accuracy. The highest performing models were the two-sensor GB and tibia-sensor CNN (average accuracy of 97.0 ± 0.7 and 96.1 ± 2.6%, respectively). SIGNIFICANCE: Machine learning algorithms trained on running data from a single- or dual-sensor accelerometer setup can accurately distinguish between surfaces types. Automatic identification of surfaces encountered during running activities could help runners and coaches better monitor training load, improve performance, and reduce injury rates.status: publishe