---
title: "Cross-People Mobile-Phone Based Activity Recognition"
authors:
  - "Zhongtang Zhao"
  - "Yiqiang Chen"
  - "Junfa Liu"
  - "Zhiqi Shen"
  - "Mingjie Liu"
doi: "10.5591/978-1-57735-516-8/IJCAI11-423"
url: "https://ijcai.org/proceedings/2011/423"
pdf: "https://ijcai.org/proceedings/2011/0423.pdf"
venue: "Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence"
year: 2011
pages: "2545-2549"
track: ""
keywords:
---

# Cross-People Mobile-Phone Based Activity Recognition

Zhongtang Zhao, Yiqiang Chen, Junfa Liu, Zhiqi Shen, Mingjie Liu

*Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence*, pages 2545-2549. DOI: [10.5591/978-1-57735-516-8/IJCAI11-423](https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-423)

## Abstract

Activity recognition using mobile phones has great potential in many applications including mobile healthcare. In order to let a person easily know whether he is in strict compliance with the doctor's exercise prescription and adjust his exercise amount accordingly, we can use a smart-phone based activity reporting system to accurately recognize a range of daily activities and report the duration of each activity. A triaxial accelerometer embedded in the smart phone is used for the classification of several activities, such as staying still, walking, running, and going upstairs and downstairs. The model learnt from a specific person often cannot yield accurate results when used on a different person. To solve the cross-people activity recognition problem, we propose an algorithm known as TransEMDT (Transfer learning EMbedded Decision Tree) that integrates a decision tree and the k-means clustering algorithm for personalized activity-recognition model adaptation. Tested on a real-world data set, the results show that our algorithm outperforms several traditional baseline algorithms.