Outdoor Sport Activities (MTS-5)

jamasoftwares·January 12, 2025

Description

The time series dataset of sport activities (232) in 5 categories (Walking, Running, Skiing, Roller-Skiing, Biking).

Summary


Description

You may check the related TSC project here

Dataset consists of data in categories Biking, Running, Skiing, Roller-Skiing, and Walking. This dataset consists of the same activities as previously created Outdoor Sport Activities (MTS) , but it expands Other category to Skiing, Roller-Skiing, and Walking.

Sport activities have been performed by a single active (non-professional) athlete.

Data is standardized and splitted in four parts (each dimension in its own file):

NOTE: Signal order between the separate files must not be confused when processing the data. Signal order is critical; first index in each of the file comes from the same activity which label corresponds to first index in the target data file, and so on. So, data should be constructed and files combined into the same table while reading the files, ideally using nested data structure. Something like in the picture below:

Nested data structure for multivariate time series classifiers

In the following picture one can see five signal samples for each dimension (Heart Rate, Speed, Altitude) in standard feature value format. So, each figure contains signal from five different random activities (can be same or different category). However, for example, signal indexes number 1 in each three figure are from the very same activity. Figures just visualizes what kind of signals we are processing. They do not have any more significant meaning.

Signals from sport activities (Heart Rate, Speed, and Altitude)

Dataset size and construction procedure

The original amount of sport activities is 228. From each of them, starting from the index 100 (seconds), have been picked 5 x 69 second consecutive segments, that is expressed as a formula below:

Data segmentation and augmentation formula

where 𝐷 = 𝑜𝑟𝑖𝑔𝑖𝑛𝑎𝑙 𝑓𝑖𝑙𝑡𝑒𝑟𝑒𝑑 𝑑𝑎𝑡𝑎 ,𝑁 = 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑎𝑐𝑡𝑖𝑣𝑖𝑡𝑖𝑒𝑠 , 𝑠 = 𝑠𝑒𝑔𝑚𝑒𝑛𝑡 𝑠𝑡𝑎𝑟𝑡 𝑖𝑛𝑑𝑒𝑥 , 𝑙 = 𝑠𝑒𝑔𝑚𝑒𝑛𝑡 𝑙𝑒𝑛𝑔𝑡ℎ, and 𝑛 = 𝑡ℎ𝑒 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑠𝑒𝑔𝑚𝑒𝑛𝑡𝑠 from a single original sequence 𝐷𝑖 , resulting the new set of equal length segments 𝐷𝑠𝑒𝑔. And in this certain case the equation takes the form of:

Data segmentation and augmentation formula with values

Thus, dataset has dimensions of 1140 x 69 x 3.

Basic info
Author
jamasoftwares
Shared withEveryone
CreatedJune 16, 2023
Size4 MB
LicensePublic Domain
Dictionary4 tables
Original URLGo to check
Publishedimage
multivariate data
multivariate
time series
sport
biking
running
walking
skiing
rollerskiing
public datasets
Advanced features
Insights
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Outdoor Sport Activities (MTS-5)

jamasoftwares·January 12, 2025

Description

The time series dataset of sport activities (232) in 5 categories (Walking, Running, Skiing, Roller-Skiing, Biking).

Summary


Description

You may check the related TSC project here

Dataset consists of data in categories Biking, Running, Skiing, Roller-Skiing, and Walking. This dataset consists of the same activities as previously created Outdoor Sport Activities (MTS) , but it expands Other category to Skiing, Roller-Skiing, and Walking.

Sport activities have been performed by a single active (non-professional) athlete.

Data is standardized and splitted in four parts (each dimension in its own file):

NOTE: Signal order between the separate files must not be confused when processing the data. Signal order is critical; first index in each of the file comes from the same activity which label corresponds to first index in the target data file, and so on. So, data should be constructed and files combined into the same table while reading the files, ideally using nested data structure. Something like in the picture below:

Nested data structure for multivariate time series classifiers

In the following picture one can see five signal samples for each dimension (Heart Rate, Speed, Altitude) in standard feature value format. So, each figure contains signal from five different random activities (can be same or different category). However, for example, signal indexes number 1 in each three figure are from the very same activity. Figures just visualizes what kind of signals we are processing. They do not have any more significant meaning.

Signals from sport activities (Heart Rate, Speed, and Altitude)

Dataset size and construction procedure

The original amount of sport activities is 228. From each of them, starting from the index 100 (seconds), have been picked 5 x 69 second consecutive segments, that is expressed as a formula below:

Data segmentation and augmentation formula

where 𝐷 = 𝑜𝑟𝑖𝑔𝑖𝑛𝑎𝑙 𝑓𝑖𝑙𝑡𝑒𝑟𝑒𝑑 𝑑𝑎𝑡𝑎 ,𝑁 = 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑎𝑐𝑡𝑖𝑣𝑖𝑡𝑖𝑒𝑠 , 𝑠 = 𝑠𝑒𝑔𝑚𝑒𝑛𝑡 𝑠𝑡𝑎𝑟𝑡 𝑖𝑛𝑑𝑒𝑥 , 𝑙 = 𝑠𝑒𝑔𝑚𝑒𝑛𝑡 𝑙𝑒𝑛𝑔𝑡ℎ, and 𝑛 = 𝑡ℎ𝑒 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑠𝑒𝑔𝑚𝑒𝑛𝑡𝑠 from a single original sequence 𝐷𝑖 , resulting the new set of equal length segments 𝐷𝑠𝑒𝑔. And in this certain case the equation takes the form of:

Data segmentation and augmentation formula with values

Thus, dataset has dimensions of 1140 x 69 x 3.

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