Recognize which Daily Living Activity is Performed by People Wearing Actigraphy Devices

Maurizio Leotta, Andrea Fasciglione

MAKEathon 2020 (17-18 October 2020)

FHNW University of Applied Sciences and Arts Northwestern Switzerland

For this challenge you have to use the following dataset. It includes data of 9 volunteers: males aged between 23-37, with a weight between 52-94 kg and height between 172-186 cm. Regarding the dominant hand, 2 subjects out of 9 were left-handed, while the other ones (7 out of 9) were right-handed. In the next month we expect to record the data of 25-30 additional participants.

Each person performed these different activities:

Group A activities have been performed for a fixed time, while Group B not. In particular, Group A activities have been performed for more than 120 seconds (in general for about 150 seconds), and we included in the dataset the central 120 seconds of each execution in order to obtain cleaner data.

On the other hand, Group B includes: WALKING performed for 160 meters (in at least 110 seconds); WALKING_FAST performed for 205 meters (in at least 110 seconds); and DOWNSTAIRS, UPSTAIRS, UPSTAIRS_FAST performed using a single flight of stairs with no intermediate floors between the steps for an average time of 40 seconds.

The two wearable devices were worn by the participants as follows and with the following settings:
  • 1 Actigraph GT9X Link at the right hip at the height of the iliac crest (using the device belt clip). IMU (i.e., accelerometer, magnetometer, and gyroscope) recording at a sampling rate of 100 Hz.
  • 1 Actigraph GT9X Link at the height of the right ankle placed, with the help of the belt clip, on the subject's right side of the shoe, over the malleolus. IMU recording at a sampling rate of 100 Hz.

The dataset raw data and more detailed information and instructions can be found HERE (note, for this challenge use only data from the hip and the ankle).
For any question on the challenge please write to:

Please cite this Dataset as:

Maurizio Leotta, Andrea Fasciglione, Alessandro Verri.
Daily Living Activity Recognition Using Wearable Devices: A Features-rich Dataset and a Novel Approach.
Proceedings of 25th International Conference on Pattern Recognition Workshops (ICPR 2021 Workshops), Milan, Italy, 10-15 January 2021, pp.171-187, Volume 12662, LNCS, Editors: A. Del Bimbo et al. Springer, 2021.
DOI: 10.1007/978-3-030-68790-8_15, ISBN: 978-3-030-68790-8.