A study of an iterative user-specific human activity classification approach

Fürderer, Niklas (2018). A study of an iterative user-specific human activity classification approach. Thesis (Master thesis), E.T.S. de Ingenieros Informáticos (UPM).

Description

Title: A study of an iterative user-specific human activity classification approach
Author/s:
  • Fürderer, Niklas
Contributor/s:
  • Boström, Henrik
  • Thorsteinsdottir, Arnrun
  • Carlsson, Tor
  • Kajko-Mattsson, Mira
Item Type: Thesis (Master thesis)
Masters title: Data Science
Date: 2018
Subjects:
Freetext Keywords: Human activity recognition; Classification; Random forest; Xgboost; Decision tree; Iterative learning approach; User-specific
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Otro
Creative Commons Licenses: Recognition - No derivative works - Non commercial

Full text

[img]
Preview
PDF - Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (2MB) | Preview

Abstract

Applications for sensor-based human activity recognition use the latest algorithms for the detection and classification of human everyday activities, both for online and offline use cases. The insights generated by those algorithms can in a next step be used within a wide broad of applications such as safety, fitness tracking, localization, personalized health advice and improved child and elderly care. In order for an algorithm to be performant, a significant amount of annotated data from a specific target audience is required. However, a satisfying data collection process is cost and labor intensive. This also may be unfeasible for specific target groups as aging effects motion patterns and behaviors. One main challenge in this application area lies in the ability to identify relevant changes over time while being able to reuse previously annotated user data. The accurate detection of those user-specific patterns and movement behaviors therefore requires individual and adaptive classification models for human activities. The goal of this degree work is to compare several supervised classifier performances when trained and tested on a newly iterative user-specific human activity classification approach as described in this report. A qualitative and quantitative data collection process was applied. The tree-based classification algorithms Decision Tree, Random Forest as well as XGBoost were tested on custom based datasets divided into three groups. The datasets contained labeled motion data of 21 volunteers from wrist worn sensors. Computed across all datasets, the average performance measured in recall increased by 5.2% (using a simulated leave-one-subject-out cross evaluation) for algorithms trained via the described approach compared to a random non-iterative approach.

More information

Item ID: 57478
DC Identifier: http://oa.upm.es/57478/
OAI Identifier: oai:oa.upm.es:57478
Deposited by: Biblioteca Facultad de Informatica
Deposited on: 11 Dec 2019 10:43
Last Modified: 11 Dec 2019 10:43
  • Logo InvestigaM (UPM)
  • Logo GEOUP4
  • Logo Open Access
  • Open Access
  • Logo Sherpa/Romeo
    Check whether the anglo-saxon journal in which you have published an article allows you to also publish it under open access.
  • Logo Dulcinea
    Check whether the spanish journal in which you have published an article allows you to also publish it under open access.
  • Logo de Recolecta
  • Logo del Observatorio I+D+i UPM
  • Logo de OpenCourseWare UPM