Nomadic input on mobile devices: the influence of touch input technique and walking speed on performance and offset modeling

In everyday life people use their mobile phones on-the-go with different walking speeds and with different touch input techniques. Unfortunately, much of the published research in mobile interaction does not quantify the influence of these variables. In this paper, we analyze the influence of walking speed, gait pattern and input techniques on commonly used performance parameters like error rate, accuracy and tapping speed, and we compare the results to the static condition. We examine the influence of these factors on the machine learned offset model used to correct user input and we make design recommendations. The results show that all performance parameters degraded when the subject started to move, for all input techniques. Index finger pointing techniques demonstrated overall better performance compared to thumb-pointing techniques. The influence of gait phase on tap event likelihood and accuracy was demonstrated for all input techniques and all walking speeds. Finally, it was shown that the offset model built on static data did not perform as well as models inferred from dynamic data, which indicates the speed-specific nature of the models. Also, models identified using specific input techniques did not perform well when tested in other conditions, demonstrating the limited validity of offset models to a particular input technique. The model was therefore calibrated using data recorded with the appropriate input technique, at 75% of preferred walking speed, which is the speed to which users spontaneously slow down when they use a mobile device and which presents a tradeoff between accuracy and usability. This led to an increase in accuracy compared to models built on static data. The error rate was reduced between 0.05% and 5.3% for landscape-based methods and between 5.3% and 11.9% for portrait-based methods

The Application of a Falls Risk Index

This study examined the prospective use of a falls risk index. The research question was: To what extent do the intrinsic factors identified in the Tinetti et al. falls risk index predict which patients are likely to experience a fall. Hogue\u27s ecological model of falls in late life provided a conceptual framework for the study. Direct observation was used to collect baseline data from a convenience/purposive sample of 26 male patients in a midwest nursing home care unit with a rehabilitation focus. Patients were then assigned to one of three risk groups: yes-fall, 30% chance of fall, no-fall. Reports of patient falls were reviewed during the following four months. Data were analyzed by discriminant analysis and frequency tables. Actual occurrences were demonstrated to be consistent with predicted occurrences in the frequency tabulation, and 23/26 Participants were classified correctly by discriminant analysis. There are several considerations in the interpretation of this data: (1) over half the sample was in the predicted middle—risk group (30% chance of falls) which has limited clinical usefulness, (2) the discriminant analysis equation was developed from study data, and (3) no variable contributed significantly to risk of falling in the stepwise entrance of variables analysis. Nonetheless, predictability of the extremes (yes-fall or no-fall) using reproducible scales to evaluate risk factors was demonstrated, and may be useful clinically as well as in other studies of patient falls

Microlensing Searches for Exoplanets

Gravitational microlensing finds planets through their gravitational influence on the light coming from a more distant background star. The presence of the planet is then inferred from the tell-tale brightness variations of the background star during the lensing event, even if no light is detectable from the planet or the host foreground star. This review covers fundamental theoretical concepts in microlensing, addresses how observations are performed in practice, the~challenges of obtaining accurate measurements, and explains how planets reveal themselves in the data. It~concludes with a presentation of the most important findings to-date, a description of the method's strengths and weaknesses, and a discussion of the future prospects of microlensing.Comment: 35 pages,9 figures, invited review for Geosciences Special Issue "Detection and Characterization of Extrasolar Planets

Quantifying Variability in Oculomotor and Manual Choice Response Times

Response times (RTs) of various motor systems have traditionally been used to characterize aspects of human performance (e.g., fatigue, disease states). However, the properties and sensitivity of different motor systems to detect changes in neural states across multiple timescales remain an open question. In this thesis, we attempt to characterize the difference in sensitivity of the pursuit, saccadic, and manual systems to detect changes in stimulus strength. In Experiment 1, we used a modified Yes-No task to test the effects of contrast (5, 10, 20, 40, 80, 100%) on three pursuit, saccadic, and manual RT’s for three observers. In Experiment 2, we used a 2-AFC task to test the effects of luminance (0-10 d\u27 above background noise) on saccadic and manual RT for five observers. We observed: 1) saccadic RT are better correlated with changes in stimulus strength, 2) manual responses are more variable, 3) trial-by-trial variability is greater than variability across sessions, and 4) each pair of motor systems shows significant shared variability. We conclude that oculomotor and manual responses have different signal processing and RT characteristics, and may have different levels of utility to detect physiological factors that affect performance (e.g., Dinges & Powell, 1985), with the saccadic system being more sensitive to changes in stimulus strength and less variable in the timing of the response

Vegetation Dynamics in Ecuador

Global forest cover has suffered a dramatic reduction during recent decades, especially in tropical regions, which is mainly due to human activities caused by enhanced population pressures. Nevertheless, forest ecosystems, especially tropical forests, play an important role in the carbon cycle functioning as carbon stocks and sinks, which is why conservation strategies are of utmost importance respective to ongoing global warming. In South America the highest deforestation rates are observed in Ecuador, but an operational surveillance system for continuous forest monitoring, along with the determination of deforestation rates and the estimation of actual carbon socks is still missing. Therefore, the present investigation provides a functional tool based on remote sensing data to monitor forest stands at local, regional and national scales. To evaluate forest cover and deforestation rates at country level satellite data was used, whereas LiDAR data was utilized to accurately estimate the Above Ground Biomass (AGB; carbon stocks) at catchment level. Furthermore, to provide a cost-effective tool for continuous forest monitoring of the most vulnerable parts, an Unmanned Aerial Vehicle (UAV) was deployed and equipped with various sensors (RBG and multispectral camera). The results showed that in Ecuador total forest cover was reduced by about 24% during the last three decades. Moreover, deforestation rates have increased with the beginning of the new century, especially in the Andean Highland and the Amazon Basin, due to enhanced population pressures and the government supported oil and mining industries, besides illegal timber extractions. The AGB stock estimations at catchment level indicated that most of the carbon is stored in natural ecosystems (forest and páramo; AGB ~98%), whereas areas affected by anthropogenic land use changes (mostly pastureland) lost nearly all their storage capacities (AGB ~2%). Furthermore, the LiDAR data permitted the detection of the forest structure, and therefore the identification of the most vulnerable parts. To monitor these areas, it could be shown that UAVs are useful, particularly when equipped with an RGB camera (AGB correlation: R² > 0.9), because multispectral images suffer saturation of the spectral bands over dense natural forest stands, which results in high overestimations. In summary, the developed operational surveillance systems respective to forest cover at different spatial scales can be implemented in Ecuador to promote conservation/ restoration strategies and to reduce the high deforestation rates. This may also mitigate future greenhouse gas emissions and guarantee functional ecosystem services for local and regional populations

Approach to Financial Decision Making within Quantum Physics and Neurosciences

This paper focuses on the study of the functional relationships between the tools of neuroscience, neurofinance and psychology on the one hand, and quantum physics / quantum mechanics and neurophysiology on the other. Can physics / quantum mechanics help explain / understand human behavior through the Shrödinger cat platform (perhaps we can explain the most mysterious phenomena: human behavior - cerebral secretion?)? The concepts of quantum mechanics allow a good prediction of human decision making within Schrödinger's cat (two particles can talk to each other even at a distance of a galaxy, perhaps in this sense can help explain an extremely complex decision making system), and define the "connection of quantum models with neurophysiological processes in the brain “... which is a very complex problem.” (Haven and Khrennikov) The application of quantum physics and neuroscience in finance allows us to consider the complexity of financial decision making, while the connection between quantum physics and psychology manifests itself as the field of quantum physics seeks to understand the fundamental nature of particles. while the field of psychology seeks to explain human nature along with its inherent misconceptions.If decision-making is a process of gathering evidence in favor of different alternatives over time, the process is discontinued once the decision limit is reached, followed by choice of decision. e activity within the posterior parietal cortex several important questions remain unanswered. Neural mechanisms that support the accumulation of evidence record the activities of individual neurons in different parts of the prefrontal cortex (PFC) and the lateral intraparietal area (LIP)

A Computational Model to Predict In Vivo Lower Limb Kinetics and Assess Total Knee Arthroplasty Design Parameters

Evaluating total knee arthroplasty implant design success generally requires many years of patient follow-up studies which are both inefficient and costly. Although computational modeling is utilized during the implant design phase, it has yet to be fully utilized in order to predict the post-implantation kinetics associated with various design parameters. The objective of this study was to construct a three-dimensional computational model of the human lower limb that could predict in vivo kinetics based upon input subject specific kinematics. The model was constructed utilizing Kane’s theory of dynamics and applied to two clinical sub-studies. Firstly, axial tibiofemoral forces were compared over a deep knee bend between normal knee subjects and those with implanted knees. Secondly, kinematics were obtained for a sample subject undergoing a deep knee bend, and the amount of femoral rollback experienced by the subject (-1.86 mm) was varied in order to evaluate the subsequent change in the axial tibiofemoral contact force and the quadriceps force. The mean axial tibiofemoral contact force was 1.35xBW and 2.99xBW for the normal and implanted subjects, respectively, which was a significant difference (p = 0.0023). The sample subject experienced a decrease in both the axial tibiofemoral contact force (-8.97%) and the quadriceps load (-11.84%) with an increase of femoral rollback to -6 mm. A decrease in rollback to 6 mm led to increases in both the contact force (22.45%) and the quadriceps load (27.14%). These initial studies provide evidence that this model accurately predicts in vivo kinetics and that kinetics depend on implant design and patient kinematics