Technological evolution and the growing massive production of data demand the creation of new mechanisms for processing and analysis in the most different domains of information. The same is true for the domain of sports, which has a great popular appeal worldwide and has increasingly gained the scientific community’s interest. Among the different sports genres, cycling has gained special attention in the information visualization and Visual Analytics community over the years. In general, these works focus on discovering patterns to strategize, display statistical information, or analyze the performance of groups of cyclists. However, there is a gap in works that focus on courses unrelated to professional competitions or considering climate and intensity attributes in their analyses. In this context, this master’s proposal presents a data visualization approach that understands these characteristics in specific recreational races that contain intensity characteristics, such as heart rate, cadence, and elevation, and climatic variables, such as temperature, in addition to the traditional variables related to the route and geolocation. The objective is to find patterns of visual information that provide an understanding of how these variables influence the performance of cyclists and provide a comparison between them. To assist the visual exploration process, multidimensional, temporal, graph, and geolocation visualization techniques will be juxtaposed and coordinated to facilitate user interaction. Additionally, this proposal presents an evaluation methodology based on qualitative and quantitative metrics. Specifically, three types of usability assessments will be carried out, i) the cognitive path to assess effectiveness, ii) heuristic assessment to assess efficiency, and iii) questionnaire using the scale SUS (System Usability Scale) to assess satisfaction. Therefore, this repository stores the dataset used in this project. Access the project at this link:
Link do youtube
Master's student - Prof. Esp. Iury Gomes de Oliveira
Mentor - Prof. Dr. Danilo Coimbra
Each pedal has several trackpoints
.
├── Cyclist (Contains the cyclist's data)
│ ├── all_distances.json (Contains the distances of each pedal)
│ ├── pedal (Contains the ride data)
│ │ ├── distance_history.json (Distance recorded at each trackpoint)
│ │ ├── elevation_google.json (Elevation provided by google for the coordinate latitude,longitude)
│ │ ├── heartrate_history.json (heartrate recorded at each trackpoint)
│ │ ├── latitudes.json (latitude recorded at each trackpoint)
│ │ ├── longitudes.json (longitude recorded at each trackpoint)
│ │ ├── overview.json (Contains general data, e.g., average speed, average heart rate, total trackpoints, etc.)
│ │ ├── speed_history.json (speed recorded at each trackpoint)
│ │ └── time_history.json (time recorded at each trackpoint)\
Additional information about the overview.json file
- pedal: ID
- path: gpx file path,
- creator: Device or platform generating the data
- coordinateInicial: Latitude Initial | Longitude Initial
- coordinateFinal: Latitude Final | Longitude Final
- country: ride country,
- locality: ride locality,
- centroid: ride centroid
- bbox: ride bounding box,
- datetime: ride datetime,
- duration: ride duration (HH:MM:SS),
- distance: ride distance in kilometers,
- elevation_google: ride elevation obtained from google API in meters,
- speed_avg: Average ride speed in KM/H,
- heartrate_avg: Average ride heartrate in bpm,
- temperature_avg: Average ride temperature in celsius,
- trackpoints: total ride trackpoints
The original data was obtained from:
- RAUTER, S.; FISTER, I. A collection of sport activity files for data analysis and data mining. University of Ljubljana, 2015
Additional information on data preprocessing
- RAUTER, S.; FISTER, I.; JR, I. F. How to deal with sports activity datasets for data mining and analysis:. International Journal of Advanced Pervasive and Ubiquitous Computing, v. 7, p. 27–37, 04 2015
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api_key |
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id |
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