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

short-paper

A Correlation Network Model Utilizing Gait Parameters for Evaluating Health Levels

Authors:

Elham Rastegari,

Hesham AliAuthors Info & Claims

ACM-BCB '17: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics

Pages 568 - 574

https://doi.org/10.1145/3107411.3107487

Published: 20 August 2017 Publication History

Abstract

Healthcare is moving rapidly from the long-standing reactive treatment approach to the early detection and preventative era. However, to fully embrace this trend, new approaches need to be developed. A step in this direction is to explore how to leverage data collected from wearables sensors to help in assessing health levels. This would pave the way for continuously monitoring individuals, which, in turn, lead to helping physicians diagnose diseases in the early stages. However, a major missing piece in moving forward with this concept is the lack of a sophisticated data analytics model. In this study, we propose a new correlation network model in which several aspects associated with health levels can be identified using population analysis. The proposed model is based on identifying various mobility parameters associated with groups under study, then a correlation network is developed based on the specified parameters. In such network, each node corresponds to a person and two nodes are connected by an edge if the corresponding individuals share similar mobility profiles. We show that various network properties reflect health information of the groups under study. To test the proposed model, we use gait parameters collected from three various groups, healthy younger people, geriatrics and Parkinson's disease patients. Obtained results show that the proposed model is very promising and can be a starting point towards a robust population analysis technique for utilizing mobility data in assessing health levels and predicting potential health hazards.

References

[1]

Baltadjieva, R., Giladi, N., Gruendlinger, L., Peretz, C., & Hausdorff, J. M. (2006). Marked alterations in the gait timing and rhythmicity of patients with de novo Parkinson's disease. European Journal of Neuroscience, 24(6), 1815--1820.

[2]

Barbe, M. T., Amarell, M., Snijders, A. H., Florin, E., Quatuor, E.-L., Schönau, E., ... Timmermann, L. (2014). Gait and upper limb variability in Parkinson's disease patients with and without freezing of gait. Journal of Neurology, 261(2), 330--342.

[3]

Barth, J., Oberndorfer, C., Pasluosta, C., Schülein, S., Gassner, H., Reinfelder, S., ... Eskofier, B. M. (2015). Stride Segmentation during Free Walk Movements Using Multi-Dimensional Subsequence Dynamic Time Warping on Inertial Sensor Data. Sensors, 15(3), 6419--6440.

[4]

Bond, J. M., & Morris, M. (2000). Goal-directed secondary motor tasks: Their effects on gait in subjects with Parkinson disease. Archives of Physical Medicine and Rehabilitation, 81(1), 110--116.

[5]

Cooper, K. D., Pawaskar, S., & Ali, H. H. (2015). Identification of Biologically Significant Elements Using Correlation Networks in High Performance Computing Environments. In International Conference on Bioinformatics and Biomedical Engineering (pp. 607--619). Springer.

[6]

{Cooper, K. M., Bonasera, S., & Ali, H. (2015). Identifying aging genes in the aging mouse hypothalamus using gateway node analysis of correlation networks. In 6th International Conference on Bioinformatics Models, Methods and Algorithms, BIOINFORMATICS 2015. SciTePress.

Digital Library

[7]

Derawi, M. O., Bours, P., & Holien, K. (2010). Improved cycle detection for accelerometer based gait authentication. In Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP), 2010 Sixth International Conference on (pp. 312--317). IEEE.

Digital Library

[8]

Giladi, N., Hausdorff, J. M., & Balash, Y. (2013). Episodic and continuous gait disturbances in Parkinson's disease 22. Scientific Basis for the Treatment of Parkinson's Disease, 417.

[9]

Hausdorff, J. M., Schaafsma, J. D., Balash, Y., Bartels, A. L., Gurevich, T., & Giladi, N. (2003). Impaired regulation of stride variability in Parkinson's disease subjects with freezing of gait. Experimental Brain Research, 149(2), 187--194.

[10]

Hausdorff, Jeffrey M. (2009). Gait dynamics in Parkinson's disease: common and distinct behavior among stride length, gait variability, and fractal-like scaling. Chaos: An Interdisciplinary Journal of Nonlinear Science, 19(2), 026113.

[11]

Hausdorff, Jeffrey M., Cudkowicz, M. E., Firtion, R., Wei, J. Y., & Goldberger, A. L. (1998a). Gait variability and basal ganglia disorders: Stride-to-stride variations of gait cycle timing in parkinson's disease and Huntington's disease. Movement Disorders, 13(3), 428--437.

[12]

Hausdorff, Jeffrey M., Lertratanakul, A., Cudkowicz, M. E., Peterson, A. L., Kaliton, D., & Goldberger, A. L. (2000). Dynamic markers of altered gait rhythm in amyotrophic lateral sclerosis. Journal of Applied Physiology, 88(6), 2045--2053.

[13]

Jo, J., S, O., & 3rd, D. R. (1996). Effects of age on the biomechanics and physiology of gait. Clinics in Geriatric Medicine, 12(4), 659--678.

[14]

Kobsar, D., Olson, C., Paranjape, R., Hadjistavropoulos, T., & Barden, J. M. (2014). Evaluation of age-related differences in the stride-to-stride fluctuations, regularity and symmetry of gait using a waist-mounted tri-axial accelerometer. Gait & Posture, 39(1), 553--557.

[15]

Libby, R. (2008). A simple method for reliable footstep detection on embedded sensor platforms. Sensors (Peterborough, NH), 116.

[16]

Lord, S., Galna, B., Verghese, J., Coleman, S., Burn, D., & Rochester, L. (2013). Independent Domains of Gait in Older Adults and Associated Motor and Nonmotor Attributes: Validation of a Factor Analysis Approach. The Journals of Gerontology: Series A, 68(7), 820--827.

[17]

Luce, R. D., & Perry, A. D. (1949). A method of matrix analysis of group structure. Psychometrika, 14(2), 95--116.

[18]

Moe-Nilssen, R., & Helbostad, J. L. (2005). Interstride trunk acceleration variability but not step width variability can differentiate between fit and frail older adults. Gait & Posture, 21(2), 164--170.

[19]

Morris, M. E., Iansek, R., Matyas, T. A., & Summers, J. J. (1996). Stride length regulation in Parkinson's disease. Brain, 119(2), 551--568.

[20]

Muro-de-la-Herran, A., Garcia-Zapirain, B., & Mendez-Zorrilla, A. (2014). Gait Analysis Methods: An Overview of Wearable and Non-Wearable Systems, Highlighting Clinical Applications. Sensors, 14(2), 3362--3394.

[21]

Murray, M. P. (1959). Walking patterns in healthy old men. J. Gerontology, 24, 169--178.

[22]

Plotnik, M., & Hausdorff, J. M. (2008). The role of gait rhythmicity and bilateral coordination of stepping in the pathophysiology of freezing of gait in Parkinson's disease. Movement Disorders, 23(S2), S444--S450.

[23]

Provan, K. G., Fish, A., & Sydow, J. (2007). Interorganizational networks at the network level: A review of the empirical literature on whole networks. Journal of Management, 33(3), 479--516.

[24]

Sabatini, A. M., Martelloni, C., Scapellato, S., & Cavallo, F. (2005). Assessment of walking features from foot inertial sensing. IEEE Transactions on Biomedical Engineering, 52(3), 486--494.

[25]

Salarian, A., Russmann, H., Vingerhoets, F. J. G., Dehollain, C., Blanc, Y., Burkhard, P. R., & Aminian, K. (2004). Gait assessment in Parkinson's disease: toward an ambulatory system for long-term monitoring. IEEE Transactions on Biomedical Engineering, 51(8), 1434--1443.

[26]

Schaafsma, J. D., Balash, Y., Gurevich, T., Bartels, A. L., Hausdorff, J. M., & Giladi, N. (2003). Characterization of freezing of gait subtypes and the response of each to levodopa in Parkinson's disease. European Journal of Neurology, 10(4), 391--398.

[27]

Shamir, R., Sharan, R., & Tsur, D. (2004). Cluster graph modification problems. Discrete Applied Mathematics, 144(1--2), 173--182.

[28]

Patla, A. E., & Walt, S. E. (1990). Biomechanical W hng Pattern Changes in the Fit and Healthy Elderly. Phys Ther, 70, 340--347.

[29]

Ying, H., Silex, C., Schnitzer, A., Leonhardt, S., & Schiek, M. (2007). Automatic step detection in the accelerometer signal. In 4th International Workshop on Wearable and Implantable Body Sensor Networks (BSN 2007) (pp. 80--85). Springer.

[30]

Yogev, G., Plotnik, M., Peretz, C., Giladi, N., & Hausdorff, J. M. (2007). Gait asymmetry in patients with Parkinson's disease and elderly fallers: when does the bilateral coordination of gait require attention? Experimental Brain Research, 177(3), 336--346.

[31]

Youn, I.-H., Khazanchi, D., Youn, J.-H., & Siu, K.-C. (2016). Multidimensional Mobility Metric for Continuous Gait Monitoring using a Single Accelerometer. Int'l Conf. Healht informatics and Medical Systems.

[32]

Zijlstra, W., & Aminian, K. (2007). Mobility assessment in older people: new possibilities and challenges. European Journal of Ageing, 4(1), 3--12.

[33]

Zijlstra, W., & Hof, A. L. (2003). Assessment of spatio-temporal gait parameters from trunk accelerations during human walking. Gait & Posture, 18(2), 1--10.

Cited By

Rastegari EAli HMarmelat V(2022)Detection of Parkinson’s Disease Using Wrist Accelerometer Data and Passive MonitoringSensors10.3390/s2223912222:23(9122)Online publication date: 24-Nov-2022
https://doi.org/10.3390/s22239122
Rastegari EAli H(2020)A bag-of-words feature engineering approach for assessing health conditions using accelerometer dataSmart Health10.1016/j.smhl.2020.100116(100116)Online publication date: Mar-2020
https://doi.org/10.1016/j.smhl.2020.100116
Rastegari EMarmelat VNajjar LBastola DAli H(2017)Using gait parameters to recognize various stages of Parkinson's disease2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM.2017.8217906(1647-1651)Online publication date: Nov-2017
https://doi.org/10.1109/BIBM.2017.8217906

Index Terms

A Correlation Network Model Utilizing Gait Parameters for Evaluating Health Levels
1. Applied computing
  1. Life and medical sciences
2. Networks
  1. Network components

Recommendations

Gait parameters change prior to freezing in Parkinson's disease: a data-driven study with wearable inertial units
BodyNets '15: Proceedings of the 10th EAI International Conference on Body Area Networks

Freezing of gait (FoG) is a motor impairment among patients with advanced Parkinson's disease which is associated with falls and has a negative impact on a patient's quality of life. Wearable systems have been developed to detect FoG and to help ...
An Evaluation Framework for a Rural Home-Based Telerehabilitation Network

This study was a needs assessment to inform the design and evaluation of a home-based telerehabilitation network for rural elderly patients. We conducted a literature review of telerehabilitation studies and a needs-assessment by interviewing 43 ...
Evaluating public health uses of health information exchange

Health information exchange (HIE) initiatives are in various stages of development across the United States. They aim to bring previously unavailable clinical data from patients' disparate health records, which may be spread over multiple provider and ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

ACM-BCB '17: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics

August 2017

800 pages

ISBN:9781450347228

DOI:10.1145/3107411

General Chairs:
Nurit Haspel
University of Massachusetts Boston, USA
,
Lenore J. Cowen
Tufts University, USA
,
Program Chairs:
Amarda Shehu
George Mason University, USA
,
Tamer Kahveci
University of Florida, USA
,
Giuseppe Pozzi
Politecnico di Milano, Italy

Copyright © 2017 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]

Sponsors

SIGBio: ACM Special Interest Group on Bioinformatics

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 August 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Conference

BCB '17

Sponsor:

SIGBio

BCB '17: 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

August 20 - 23, 2017

Massachusetts, Boston, USA

Acceptance Rates

ACM-BCB '17 Paper Acceptance Rate 42 of 132 submissions, 32%;

Overall Acceptance Rate 254 of 885 submissions, 29%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
145
Total Downloads

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

Reflects downloads up to 31 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Rastegari EAli HMarmelat V(2022)Detection of Parkinson’s Disease Using Wrist Accelerometer Data and Passive MonitoringSensors10.3390/s2223912222:23(9122)Online publication date: 24-Nov-2022
https://doi.org/10.3390/s22239122
Rastegari EAli H(2020)A bag-of-words feature engineering approach for assessing health conditions using accelerometer dataSmart Health10.1016/j.smhl.2020.100116(100116)Online publication date: Mar-2020
https://doi.org/10.1016/j.smhl.2020.100116
Rastegari EMarmelat VNajjar LBastola DAli H(2017)Using gait parameters to recognize various stages of Parkinson's disease2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM.2017.8217906(1647-1651)Online publication date: Nov-2017
https://doi.org/10.1109/BIBM.2017.8217906

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