# Localization Data for Person Activity

## Person Activity Data Set

This arithmetica practice is from the paper An Agent-based Approach to Care in Independent Living posted at UCI.

The paper introduces a fall detector based on a neural network and a multi-agent architecture for requesting emergency services. It presented a multi-agent system for the care of elderly people living at home on their own, with the aim to prolong their independence. The system is composed of seven groups of agents providing a reliable, robust and flexible monitoring by sensing the user in the environment, reconstructing the position and posture to create the physical awareness of the user in the environment, reacting to critical situations, calling for help in the case of an emergency, and issuing warnings if unusual behavior is detected. The system has been tested during several on-line demonstration. People used for recording of the data were wearing four tags (ankle left, ankle right, belt and chest). Each instance is a localization data for one of the tags. The tag can be identified by one of the attributes. The goal for this practice is to correctly predict the activity the user is performing.

Source:

- Creators: Mitja Lustrek (mitja.lustrek '@' ijs.si), Bostjan Kaluza (bostjan.kaluza '@' ijs.si), Rok Piltaver (rok.piltaver '@' ijs.si), Jana Krivec (jana.krivec '@' ijs.si), Vedrana Vidulin (vedrana.vidulin '@' ijs.si
- Jozef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, Slovenija
- Donor: Bozidara Cvetkovic (boza.cvetkovic '@' ijs.si
- Jozef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, Slovenija
- Date received: October, 2010

 Relevant Papers:B. Kaluza, V. Mirchevska, E. Dovgan, M. Lustrek, M. Gams, An Agent-based Approach to Care in Independent Living, International Joint Conference on Ambient Intelligence (AmI-10), Malaga, Spain, In pressRecent Updates: Check out the medium article for this practice

## Evaluation

The evaluation of this dataset is done using Area Under the ROC curve (AUC).

# Use of external data is not permitted. This includes use of pre-trained models.Hand-labeling is allowed on the training dataset only. Hand-labeling is not permitted on test data and will be grounds for disqualification.

## Leaderboard

Rank Team Score Count Submitted Date

## Data License

Citation Policy:

Please refer to : https://archive.ics.uci.edu/ml/citation_policy.html

Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.