More Web Proxy on the site http://driver.im/

research-article

SARRIMA: smart ADL recognizer and resident identifier in multi-resident accommodations

Authors:

Ifat Afrin Emi,

John A. StankovicAuthors Info & Claims

WH '15: Proceedings of the conference on Wireless Health

Article No.: 4, Pages 1 - 8

https://doi.org/10.1145/2811780.2811916

Published: 14 October 2015 Publication History

Abstract

Systems for measuring Activities of Daily Livings (ADL) play a significant role in home health-care. The ability of performing ADLs successfully is used as an important factor in deciding treatments and services for patients and elderly citizens. However, most of these systems are designed for single-resident homes. The presence of multiple people creates higher numbers of parallel and overlapping activities, and introduces additional complexities in defining and recognizing activity instances. We present SARRIMA, a system that recognizes activity instances and assigns those activities to a person in 2-resident homes using only passive sensors. We evaluate the efficiency of SARRIMA in two different public datasets (data from real homes) with multiple residents. On the average SARRIMA detects more than 97% of the activity instances. We also show how the person assignment accuracy varies as a function of the similarity of behavior of the 2 people living together and of the types of passive sensors installed.

References

[1]

A profile of older american : 2013. Technical report.

[2]

R. Agrawal, R. Srikant, et al. Fast algorithms for mining association rules. In Proc. 20th int. conf. very large data bases, VLDB, volume 1215, pages 487--499, 1994.

Digital Library

[3]

H. Alerndar, H. Ertan, O. Incel, and C. Ersoy. Aras human activity datasets in multiple homes with multiple residents. In Pervasive Computing Technologies for Healthcare (PervasiveHealth), 2013 7th International Conference on, pages 232--235, May 2013.

Digital Library

[4]

T. S. Barger, D. E. Brown, and M. Alwan. Health-status monitoring through analysis of behavioral patterns. Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, 35(1):22--27, 2005.

Digital Library

[5]

M. Buettner, R. Prasad, M. Philipose, and D. Wetherall. Recognizing daily activities with rfid-based sensors. In Proceedings of the 11th international conference on UbiCom, pages 51--60. ACM, 2009.

Digital Library

[6]

S. Consolvo, D. W. McDonald, T. Toscos, M. Y. Chen, J. Froehlich, B. Harrison, P. Klasnja, A. LaMarca, L. LeGrand, R. Libby, et al. Activity sensing in the wild: a field trial of ubifit garden. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pages 1797--1806. ACM, 2008.

Digital Library

[7]

D. J. Cook, M. Schmitter, et al. Assessing the quality of activities in a smart environment. Methods Inf Med, 48(5):480--485, 2009.

[8]

R. Dickerson, E. Hoque, P. Asare, S. Nirjon, and J. Stankovic. Resonate: Reverberation environment simulation for improved classification of speech models. In Information Processing in Sensor Networks, IPSN-14 Proceedings of the 13th International Symposium on, pages 107--117, April 2014.

Digital Library

[9]

R. F. Dickerson, E. I. Gorlin, and J. A. Stankovic. Empath: a continuous remote emotional health monitoring system for depressive illness. In Proceedings of the 2nd Conf. on Wireless Health, page 5. ACM, 2011.

Digital Library

[10]

M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. In Kdd, volume 96, pages 226--231, 1996.

Digital Library

[11]

J. E. Froehlich, E. Larson, T. Campbell, C. Haggerty, J. Fogarty, and S. N. Patel. Hydrosense: infrastructure-mediated single-point sensing of whole-home water activity. In 11th international conference on Ubiquitous computing, pages 235--244. ACM, 2009.

Digital Library

[12]

E. Hoque and J. Stankovic. Aalo: Activity recognition in smart homes using active learning in the presence of overlapped activities. In Pervasive Computing Technologies for Healthcare (PervasiveHealth), pages 139--146. IEEE, 2012.

[13]

H. Kalantarian, N. Alshurafa, and M. Sarrafzadeh. A wearable nutrition monitoring system. In 11th International Conference of Wearable and Implantable Body Sensor Networks (BSN), pages 75--80. IEEE, 2014.

Digital Library

[14]

A. L. Lansky. A representation of parallel activity based on events, structure, and causality. In Reasoning About Actions & Plans---Proceedings, pages 123--160, 1987.

[15]

U. Maurer, A. Smailagic, D. P. Siewiorek, and M. Deisher. Activity recognition and monitoring using multiple sensors on different body positions. In Wearable and Implantable Body Sensor Networks, 2006. BSN 2006. International Workshop on, pages 4--pp. IEEE, 2006.

Digital Library

[16]

L. Mercier, T. Audet, R. Hébert, A. Rochette, and M.-F. Dubois. Impact of motor, cognitive, and perceptual disorders on ability to perform activities of daily living after stroke. Stroke, 32(11):2602--2608, 2001.

[17]

B. Ni, G. Wang, and P. Moulin. Rgbd-hudaact: A color-depth video database for human daily activity recognition. In Consumer Depth Cameras for Computer Vision, pages 193--208. Springer, 2013.

[18]

M. Prossegger and A. Bouchachia. Multi-resident activity recognition using incremental decision trees. In Adaptive and Intelligent Systems, pages 182--191. Springer, 2014.

[19]

K. Rockwood. The measuring, meaning and importance of activities of daily living (adls) as an outcome. International psychogeriatrics, 19(03):467--482, 2007.

[20]

N. E. Sherwood and R. W. Jeffery. The behavioral determinants of exercise: implications for physical activity interventions. Annual review of nutrition, 20(1):21--44, 2000.

[21]

B. G. Steele, B. Belza, K. Cain, C. Warms, J. Coppersmith, and J. Howard. Bodies in motion: monitoring daily activity and exercise with motion sensors in people with chronic pulmonary disease. Journal of rehabilitation research and development, 40(5; SUPP/2):45--58, 2003.

[22]

E. M. Tapia, S. S. Intille, and K. Larson. Activity recognition in the home using simple and ubiquitous sensors. Springer, 2004.

[23]

J. M. Wiener, R. J. Hanley, R. Clark, and J. F. Van Nostrand. Measuring the activities of daily living: Comparisons across national surveys. Journal of Gerontology, 45(6):S229--S237, 1990.

[24]

D. H. Wilson and C. Atkeson. Simultaneous tracking and activity recognition (star) using many anonymous, binary sensors. In Pervasive computing, pages 62--79. Springer, 2005.

Digital Library

Cited By

Chen DYongchareon SLai ESheng QLiesaputra V(2022)Locally Weighted Ensemble-Detection-Based Adaptive Random Forest Classifier for Sensor-Based Online Activity Recognition for Multiple ResidentsIEEE Internet of Things Journal10.1109/JIOT.2021.31393309:15(13077-13085)Online publication date: 1-Aug-2022
https://doi.org/10.1109/JIOT.2021.3139330
Mohamed RZainudin MPerumal TMuhammad S(2022)Adaptive Profiling Model for Multiple Residents Activity Recognition Analysis Using Spatio-temporal Information in Smart HomeProceedings of the 8th International Conference on Computational Science and Technology10.1007/978-981-16-8515-6_60(789-802)Online publication date: 26-Mar-2022
https://doi.org/10.1007/978-981-16-8515-6_60
Zhu HSamtani SBrown RChen H(2021)A Deep Learning Approach for Recognizing Activity of Daily Living (ADL) for Senior Care: Exploiting Interaction Dependency and Temporal PatternsMIS Quarterly10.25300/MISQ/2021/1557445:2(859-896)Online publication date: 1-Jun-2021
https://doi.org/10.25300/MISQ/2021/15574
Show More Cited By

Index Terms

SARRIMA: smart ADL recognizer and resident identifier in multi-resident accommodations

Recommendations

A sequence-to-sequence model-based deep learning approach for recognizing activity of daily living for senior care
Graphical abstract

Display Omitted
Highlights
- We proposed an activity state representation for arbitrary sensor combinations.
Abstract
Ensuring the health and safety of independent-living senior citizens is a growing societal concern. Researchers have developed sensor based systems to monitor senior citizens' Activity of Daily Living (ADL), a set of daily activities ...
Implementing evidential activity recognition in sensorised homes

Automated recognition of activities of daily living such as preparing meals and grooming may be considered as one of the most desirable computational functions within a Smart Home for the elderly. In our current work we present a process framework with ...
Ubiquitous reminders to manage timetable in a smart home
iiWAS '16: Proceedings of the 18th International Conference on Information Integration and Web-based Applications and Services

The aging of the population will bring changes on home care for elders and gerontechnologies provide alternatives for aging in place. This article presents a concept of a smart home that reminds the resident the activities he has planned on an ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

WH '15: Proceedings of the conference on Wireless Health

October 2015

157 pages

ISBN:9781450338516

DOI:10.1145/2811780

General Chairs:
Wendy Nilsen
National Institutes of Health
,
Jack Stankovic
University of Virginia

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 October 2015

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

National Science Foundation

Conference

WH '15

WH '15: Wireless Health 2015 Conference

October 14 - 16, 2015

Maryland, Bethesda

Acceptance Rates

WH '15 Paper Acceptance Rate 28 of 106 submissions, 26%;

Overall Acceptance Rate 35 of 139 submissions, 25%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

17
Total Citations
View Citations
151
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)0

Reflects downloads up to 03 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Chen DYongchareon SLai ESheng QLiesaputra V(2022)Locally Weighted Ensemble-Detection-Based Adaptive Random Forest Classifier for Sensor-Based Online Activity Recognition for Multiple ResidentsIEEE Internet of Things Journal10.1109/JIOT.2021.31393309:15(13077-13085)Online publication date: 1-Aug-2022
https://doi.org/10.1109/JIOT.2021.3139330
Mohamed RZainudin MPerumal TMuhammad S(2022)Adaptive Profiling Model for Multiple Residents Activity Recognition Analysis Using Spatio-temporal Information in Smart HomeProceedings of the 8th International Conference on Computational Science and Technology10.1007/978-981-16-8515-6_60(789-802)Online publication date: 26-Mar-2022
https://doi.org/10.1007/978-981-16-8515-6_60
Zhu HSamtani SBrown RChen H(2021)A Deep Learning Approach for Recognizing Activity of Daily Living (ADL) for Senior Care: Exploiting Interaction Dependency and Temporal PatternsMIS Quarterly10.25300/MISQ/2021/1557445:2(859-896)Online publication date: 1-Jun-2021
https://doi.org/10.25300/MISQ/2021/15574
Howedi ALotfi APourabdollah A(2020)An Entropy-Based Approach for Anomaly Detection in Activities of Daily Living in the Presence of a VisitorEntropy10.3390/e2208084522:8(845)Online publication date: 30-Jul-2020
https://doi.org/10.3390/e22080845
Howedi ALotfi APourabdollah A(2019)Exploring Entropy Measurements to Identify Multi-Occupancy in Activities of Daily LivingEntropy10.3390/e2104041621:4(416)Online publication date: 19-Apr-2019
https://doi.org/10.3390/e21040416
Kitbutrawat NYamaguchi HHigashino T(2019)EasyTrack: Zero-Calibration Smart-Home Tracking SystemJournal of Information Processing10.2197/ipsjjip.27.44527(445-455)Online publication date: 2019
https://doi.org/10.2197/ipsjjip.27.445
Howedi ALotfi APourabdollah AMakedon F(2019)Distinguishing activities of daily living in a multi-occupancy environmentProceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments10.1145/3316782.3322782(568-574)Online publication date: 5-Jun-2019
https://dl.acm.org/doi/10.1145/3316782.3322782
Kitbutrawat NPortet FYamaguchi HHigashino T(2019)Automatic Localization of Passive Infra-Red Binary Sensors in Home: from Dense to Scattered Network2019 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)10.1109/DASC/PiCom/CBDCom/CyberSciTech.2019.00154(848-853)Online publication date: Aug-2019
https://doi.org/10.1109/DASC/PiCom/CBDCom/CyberSciTech.2019.00154
Huh J(2018)An Efficient Solitary Senior Citizens Care Algorithm and Application: Considering Emotional Care for Big Data CollectionProcesses10.3390/pr61202446:12(244)Online publication date: 27-Nov-2018
https://doi.org/10.3390/pr6120244
Wang WMiao C(2018)Multi-Resident Activity Recognition with Unseen Classes in Smart Homes2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)10.1109/SmartWorld.2018.00147(780-787)Online publication date: Oct-2018
https://doi.org/10.1109/SmartWorld.2018.00147
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents