Recognizing household activities from human motion data using active learning and feature selection.
The ability to accurately recognize human household activities is an important stepping stone toward creating home living assistance systems in the future. Classifying these activities can be difficult due to noisy sensor data, lack of labeled training samples for rare actions and large individual d...
| Publicado en: | Technology & Disability Vol. 22; no. 1/2; pp. 17 - 27 |
|---|---|
| Autores principales: | , , |
| Formato: | equations & formulas pictorial tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2010
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105035046&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105035046 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10554181 3QS jtl: Technology & Disability issn: 10554181 maglogo: N pubinfo: dt: 2010 vid: 22 iid: 1/2 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 105035046 2010692658 10.3233/TAD-2010-0284 105035046 ppf: 17 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Recognizing household activities from human motion data using active learning and feature selection. aug: au: Zhao L Wang X Sukthankar G affil: School of Electrical Engineering and Computer Science, University of Central Florida, 4000 Central Florida Blvd., Orlando, FL 32816-2362, USA sug: subj: Activities of Daily Living Artificial Intelligence Assistive Technology Devices Home Environment Motion Analysis Systems Methods Movement Evaluation Computer Simulation Learning Systems Design Task Performance and Analysis ab: The ability to accurately recognize human household activities is an important stepping stone toward creating home living assistance systems in the future. Classifying these activities can be difficult due to noisy sensor data, lack of labeled training samples for rare actions and large individual differences in activity execution. In this article, we present two techniques for improving the supervised classification of human activities from motion data: 1) an active learning framework to improve sample efficiency and 2) intelligent feature selection to reduce training time. We demonstrate our techniques using the CMU Multimodal Activity database. pubtype: Academic Journal doctype: equations & formulas pictorial tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|