research-article

Presenting a data imputation concept to support the continuous assessment of human vital data and activities

Authors:

Marian Haescher,

Denys J. C. Matthies,

Gerald BieberAuthors Info & Claims

PETRA '19: Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments

Pages 587 - 592

https://doi.org/10.1145/3316782.3322785

Published: 05 June 2019 Publication History

Abstract

Data acquisition of mobile tracking devices often suffers from invalid and non-continuous input data streams. This issue especially occurs with current wearables tracking the user's activity and vital data. Typical reasons include the short battery life and the fact that the body-worn tracking device may be doffed. Other reasons, such as technical issues, can corrupt the data and render it unusable. In this paper, we introduce a data imputation concept which complements and thus fixes incomplete datasets by using a new merging approach that is particularly suitable for assessing activities and vital data. Our technique enables the dataset to become coherent and comprehensive so that it is ready for further analysis. In contrast to previous approaches, our technique enables the controlled creation of continuous data sets that also contain information on the level of uncertainty for possible reconversions, approximations, or later analysis.

References

[1]

Rubin, D., (1976). Inference and missing data

[2]

Hernández, M. A., & Stolfo, S. J. (1998). Real-world data is dirty: Data cleansing and the merge/purge problem. Data mining and knowledge discovery, 2(1), 9--37.

Digital Library

[3]

Krishnan, N. C., & Cook, D. J. (2014). Activity recognition on streaming sensor data. Pervasive and mobile computing, 10, 138--154.

Digital Library

[4]

Vassiliadis, P. (2009). A survey of Extract-transform-Load technology. International Journal of Data Warehousing and Mining (IJDWM), 5(3), 1--27.

[5]

Stonebraker, M., Bruckner, D., Ilyas, I. F., et.al. (2013). Data Curation at Scale: The Data Tamer System. In CIDR - Conference on Innovative Data Systems Research, U.S.A., 2013.

[6]

Coyle, K. (2012). Linked data tools: connecting on the Web. American Library Association.

[7]

Yuan, Y. C. (2010). Multiple imputation for missing data: Concepts and new development (Version 9.0). SAS Institute Inc, Rockville, MD, 49, 1--11.

[8]

Marwala, T. (Ed.). (2009). Computational Intelligence for Missing Data Imputation, Estimation, and Management: Knowledge Optimization Techniques: Knowledge Optimization Techniques. IGI Global.

Digital Library

[9]

Tudor-Locke, C., Burkett, L., Reis, J. P., Ainsworth, B. E., Macera, C. A., & Wilson, D. K. (2005). How many days of pedometer monitoring predict weekly physical activity in adults?. Preventive medicine, 40(3), 293--298.

[10]

Fung, D. S. (2006). Methods for the estimation of missing values in time series, Dissertation, Edith Cowan University, Australia

[11]

Halpin, B. (2016). Multiple imputation for categorical time-series, The Stata Journal 16(3), pp. 590--612

[12]

Boegl et al. (2015). Visually and statistically guided imputation of missing values in univariate seasonal time series, in Visual Analytics Science and Technology (VAST), IEEE.

[13]

Marwala, T. (2009). Computational Intelligence for Missing Data Imputation, Estimation, and Management: Knowledge Optimization Techniques: Knowledge Optimization Techniques. IGI Global, Chicago.

Digital Library

[14]

Tudor-Locke, C., Williams, J. E., Reis, J. P., & Pluto, D. (2002). Utility of pedometers for assessing physical activity. Sports Medicine, 32(12), 795--808.

[15]

Schiel R., Kaps A., Bieber G. (2012). Electronic health technology for the assessment of physical activity and eating habits in children and adolescents with overweight and obesity IDA, Journal Appetite, Volume 58, Issue 2, Pages 432--437, April 2012

[16]

Mathie, M. J., Coster, A. C., Lovell, N. H., Celler, B. G., Lord, S. R., & Tiedemann, A. (2004). A pilot study of long-term monitoring of human movements in the home using accelerometry. Journal of telemedicine and telecare, 10(3), 144--151.

[17]

Acebo, C., Sadeh, A., Seifer, R., Tzischinsky, O., Hafer, A., & Carskadon, M. A. (2005). Sleep/wake patterns derived from activity monitoring and maternal report for healthy 1-to 5-year-old children. SLEEP-New York then Westchester, 28(12), 1568.

[18]

Schroeder M., (2016). Reconstruction of Everyday Life Behaviour based on Noisy Sensor Data, Proceedings of the 8th International Conference on Agents and Artificial Intelligence (ICAART 2016), Rome, Italy

Digital Library

[19]

Actigraph, 49 East Chase Street. Pensacola, FL 32502, U.S.A., www.actigraphcorp.com

Cited By

Braem CYavuz UHermens HVeltink P(2024)Missing Data Statistics Provide Causal Insights into Data Loss in Diabetes Health Monitoring by Wearable SensorsSensors10.3390/s2405152624:5(1526)Online publication date: 27-Feb-2024
https://doi.org/10.3390/s24051526
Yarza SHassan LShtein ALesser DNovack LKatra IKloog INovack V(2020)Novel Approaches to Air Pollution Exposure and Clinical Outcomes Assessment in Environmental Health StudiesAtmosphere10.3390/atmos1102012211:2(122)Online publication date: 21-Jan-2020
https://doi.org/10.3390/atmos11020122

Index Terms

Presenting a data imputation concept to support the continuous assessment of human vital data and activities
1. Human-centered computing
  1. Ubiquitous and mobile computing

Recommendations

HTAD: A Home-Tasks Activities Dataset with Wrist-Accelerometer and Audio Features
MultiMedia Modeling
Abstract
In this paper, we present HTAD: A Home Tasks Activities Dataset. The dataset contains wrist-accelerometer and audio data from people performing at-home tasks such as sweeping, brushing teeth, washing hands, or watching TV. These activities ...
Internal Data Imputation in Data Warehouse Dimensions
Database and Expert Systems Applications
Abstract
Missing data occur commonly in data warehouses and may generate data usefulness problems. Thus, it is essential to address missing data to carry out a better analysis. There exists data imputation methods for missing data in fact tables, but not ...
Privacy-preserving imputation of missing data

Handling missing data is a critical step to ensuring good results in data mining. Like most data mining algorithms, existing privacy-preserving data mining algorithms assume data is complete. In order to maintain privacy in the data mining process while ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

PETRA '19: Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments

June 2019

655 pages

ISBN:9781450362320

DOI:10.1145/3316782

Conference Chair:
Fillia Makedon
University of Texas at Arlington

Copyright © 2019 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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 June 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

PETRA '19

PETRA '19: The 12th PErvasive Technologies Related to Assistive Environments Conference

June 5 - 7, 2019

Rhodes, Greece

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
75
Total Downloads

Downloads (Last 12 months)9
Downloads (Last 6 weeks)0

Reflects downloads up to 08 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Braem CYavuz UHermens HVeltink P(2024)Missing Data Statistics Provide Causal Insights into Data Loss in Diabetes Health Monitoring by Wearable SensorsSensors10.3390/s2405152624:5(1526)Online publication date: 27-Feb-2024
https://doi.org/10.3390/s24051526
Yarza SHassan LShtein ALesser DNovack LKatra IKloog INovack V(2020)Novel Approaches to Air Pollution Exposure and Clinical Outcomes Assessment in Environmental Health StudiesAtmosphere10.3390/atmos1102012211:2(122)Online publication date: 21-Jan-2020
https://doi.org/10.3390/atmos11020122

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.

Figures

Tables

Media

View Table of Conten