PMData Dataset

About Dataset

Paper: https://dl.acm.org/doi/10.1145/3339825.3394926

In this dataset, we present the PMData dataset that aims to combine traditional lifelogging with sports activity logging. Such a dataset enables the development of several interesting analysis applications, e.g., where additional sports data can be used to predict and analyze everyday developments like a person's weight and sleep patterns, and where traditional lifelog data can be used in a sports context to predict an athletes performance. In this respect, we have used the Fitbit Versa 2 smartwatch wristband, the PMSys sports logging app a and Google forms for the data collection, and PMData contains logging data for 5 months from 16 persons. Our initial experiments show that such analyzes are possible, but there are still large rooms for improvements.

Dataset Details

The structure of the main folder:

[Main folder]
├── p01
├── p02
├── ...
├── p16
└── participant-overview.xlsx

Each participant's folder (pXX) contains:

fitbit [folder]
- calories.json: Shows how many calories the person have burned the last minute.
- distance.json: Gives the distance moved per minute. Distance seems to be in centimeters.
- exercise.json: Describes each activity in more detail. It contains the date with start and stop time, time in different activity levels, type of activity and various performance metrics depending a bit on type of exercise, e.g., for running, it contains distance, time, steps, calories, speed and pace.
- heart_rate.json: Shows the number of heart beats per minute (bpm) at a given time.
- lightly_active_minutes.json: Sums up the number of lightly active minutes per day.
- moderately_active_minutes.json: Sums up the number of moderately active minutes per day.
- resting_heart_rate.json: Gives the resting heart rate per day.
- sedentary_minutes.json: Sums up the number of sedentary minutes per day.
- sleep_score.csv: Helps understand the sleep each night so you can see trends in the sleep patterns. It contains an overall 0-100 score made up from composition, revitalization and duration scores, the number of deep sleep minutes, the resting heart rate and a restlessness score.
- sleep.json: A per sleep breakdown of the sleep into periods of light, deep, rem sleeps and time awake.
- steps.json: Displays the number of steps per minute.
- time_in_heart_rate_zones.json: Gives the number of minutes in different heart rate zones. Using the common formula of 220 minus your age, Fitbit will calculate your maximum heart rate and then create three target heart rate zones fat burn (50 to 69 percent of your max heart rate), cardio (70 to 84 percent of your max heart rate), and peak (85 to 100 percent of your max heart rate) - based off that number.
- very_active_minutes.json: Sums up the number of very active minutes per day.
googledocs [folder]
- reporting.csv: Contains one line per report including the date reported for, a timestamp of the report submission time, the eaten meals (breakfast, lunch, dinner and evening meal), the participants weight this day, the number of glasses drunk, and whether one has consumed alcohol.
pmsys [folder]
- injury.csv: Shows injuries with a time and date and corresponding injury locations and a minor and major severity.
- srpe.csv: Contains a training session’s end-time, type of activity, the perceived exertion (RPE), and the duration in the number of minutes. This is, for example, used to calculate the sessions training load or sRPE (RPE×duration).
- wellness.csv: Includes parameters like time and date, fatigue, mood, readiness, sleep duration (number of hours), sleep quality, soreness (and soreness area), and stress. Fatigue, sleep qual-ity, soreness, stress, and mood all have a 1-5 scale. The score 3 is normal, and 1-2 are scores below normal and 4-5 are scores above normal. Sleep length is just a measure of how long the sleep was in hours, and readiness (scale 0-10) is an overall subjective measure of how ready are you to exercise, i.e., 0 means not ready at all and 10 indicates that you cannot feel any better and are ready for anything!
food-images.zip: Participants 1, 3 and 5 have taken pictures of everything they have eaten except water during 2 months (February and March). There are food images included in this .zip file, and information about day and time is given in the image header. The participants used their own mobile cameras to collect the images (Iphone 6s, Iphone X and Iphone XS). The standard export function of the MacOS Photos software with full quality was used to export the images.

Term of use

The license for the PMData dataset is Attribution-NonCommercial 4.0 International. More information can be found here: https://creativecommons.org/licenses/by-nc/4.0/legalcode

Citation

@inproceedings{10.1145/3339825.3394926,
  address   = {New York, NY, USA},
  author    = {Thambawita, Vajira and Hicks, Steven Alexander and Borgli, Hanna and Stensland, H\r{a}kon Kvale and Jha, Debesh and Svensen, Martin Kristoffer and Pettersen, Svein-Arne and Johansen, Dag and Johansen, H\r{a}vard Dagenborg and Pettersen, Susann Dahl and Nordvang, Simon and Pedersen, Sigurd and Gjerdrum, Anders and Gr\o{}nli, Tor-Morten and Fredriksen, Per Morten and Eg, Ragnhild and Hansen, Kjeld and Fagernes, Siri and Claudi, Christine and Bi\o{}rn-Hansen, Andreas and Nguyen, Duc Tien Dang and Kupka, Tomas and Hammer, Hugo Lewi and Jain, Ramesh and Riegler, Michael Alexander and Halvorsen, P\r{a}l},
  booktitle = {Proceedings of the 11th ACM Multimedia Systems Conference},
  doi       = {10.1145/3339825.3394926},
  isbn      = {9781450368452},
  keywords  = {sports logging, questionnaires, food pictures, neural networks, multimedia dataset, sensor data, machine learning},
  location  = {Istanbul, Turkey},
  numpages  = {6},
  pages     = {231-236},
  publisher = {Association for Computing Machinery},
  series    = {MMSys '20},
  title     = {PMData: A Sports Logging Dataset},
  url       = {https://doi.org/10.1145/3339825.3394926},
  year      = {2020},
}

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