Visual analysis of users’ performance data in fitness activities

Nadalutti, Daniele; Chittaro, Luca

doi:10.1016/j.cag.2007.01.032

Cited by 11 publications

(7 citation statements)

References 1 publication

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“…The application itself was targeted to outdoor physical activity and jogging. They found that gentler and softened motivation was the most effective [26]. This application is more suited towards users who want and intend to develop exercise routines.…”

Section: Previous Workmentioning

confidence: 86%

Mobile system to motivate teenagers' physical activity

Arteaga

Kudeki

Woodworth

et al. 2010

Proceedings of the 9th International Conference on Interaction Design and Children

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This paper reports a mobile persuasive application to motivate teenagers to start and continue being physically active. Being physically active can lead to reduced risks of having weight and cardiovascular problems; however efforts in this direction had variable success. Designing technology that will be engaging and motivating for teenagers requires an understanding of the factors that contribute to behavior adoption in teenagers. To understand these, we approach the design from several theoretical models: Theory of Planned Behavior, Theory of Meaning Behavior, and Personality Theory. We found that 1) Personality traits affect perceptions on physical activities and the usefulness of devices that motivate them; 2) Favored motivational phrases are universal across traits; 3) Those who tried our prototype was generally positive and stated that they would use it on their own; 5) The characteristics of games that are desired are: social or competitive, outdoor, simple to learn and with large variations.

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Section: Previous Workmentioning

confidence: 86%

Mobile system to motivate teenagers' physical activity

Arteaga

Kudeki

Woodworth

et al. 2010

Proceedings of the 9th International Conference on Interaction Design and Children

View full text Add to dashboard Cite

show abstract

“…Instead of using position to encode the range of values over time (as is done in line charts), these visualizations use vertical color strips, whose color saturation or brightness encodes value. This approach is seen in many systems [2,15,45,53] and scales well as multiple such sequences of small height can be stacked together [37,59]. As remarked by Javed et al [34], in order to represent multiple time series, the above representations split the space (mainly vertically) and attempt to optimize the vertical footprint of each individual time series.…”

Section: Time Series Visualizationmentioning

confidence: 99%

“…In such scenarios, the use of visualization techniques that accurately and effectively communicate similar patterns between time series becomes important. Times series are commonly represented as line charts, but a considerable amount of work in Information Visualization has examined alternative visual encodings, such as horizon graphs [29,34,47,50,53] and colorfields [2,15,45,53,59]. This literature has focused on elementary visual tasks that require estimation, e.g., estimation of averages, or point comparison and discrimination tasks.…”

Section: Introductionmentioning

confidence: 99%

Comparing Similarity Perception in Time Series Visualizations

Gogolou

Tsandilas

Palpanas

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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A common challenge faced by many domain experts working with time series data is how to identify and compare similar patterns. This operation is fundamental in high-level tasks, such as detecting recurring phenomena or creating clusters of similar temporal sequences. While automatic measures exist to compute time series similarity, human intervention is often required to visually inspect these automatically generated results. The visualization literature has examined similarity perception and its relation to automatic similarity measures for line charts, but has not yet considered if alternative visual representations, such as horizon graphs and colorfields, alter this perception. Motivated by how neuroscientists evaluate epileptiform patterns, we conducted two experiments that study how these three visualization techniques affect similarity perception in EEG signals. We seek to understand if the time series results returned from automatic similarity measures are perceived in a similar manner, irrespective of the visualization technique; and if what people perceive as similar with each visualization aligns with different automatic measures and their similarity constraints. Our findings indicate that horizon graphs align with similarity measures that allow local variations in temporal position or speed (i.e., dynamic time warping) more than the two other techniques. On the other hand, horizon graphs do not align with measures that are insensitive to amplitude and y-offset scaling (i.e., measures based on z-normalization), but the inverse seems to be the case for line charts and colorfields. Overall, our work indicates that the choice of visualization affects what temporal patterns we consider as similar, i.e., the notion of similarity in time series is not visualization independent.

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“…For example, analysis tools of usage data could automatically log all user data exchanged through the various modalities and then present the designer with informative visualizations of that data at different levels of detail. Tools of such kind that we developed in our work allow us to log data such as user interface actions on the touchscreen of the mobile device [5], position of the user in the environment [7], physiological parameters [20], and then study all this data on a desktop or laptop system using detailed (e.g., VCR-like replay) or abstract (e.g., heat maps) visual analytics techniques.…”

Section: Final Remarksmentioning

confidence: 99%

Distinctive aspects of mobile interaction and their implications for the design of multimodal interfaces

Chittaro

2010

J Multimodal User Interfaces

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People want to do more with their mobile phones, but their desire is frustrated by two classes of limitations. One is related to the device, its hardware and software. The other is related to the context, and comprises perceptual, motor, cognitive and social aspects. This paper will discuss some of the opportunities and challenges that this complex scenario presents to multimodality, which can be a key factor for a better design of mobile interfaces to help people do more on their mobile phones, requiring less time and attention.

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Visual analysis of users’ performance data in fitness activities

Cited by 11 publications

References 1 publication

Mobile system to motivate teenagers' physical activity

Mobile system to motivate teenagers' physical activity

Comparing Similarity Perception in Time Series Visualizations

Distinctive aspects of mobile interaction and their implications for the design of multimodal interfaces

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