research-article

PDMove: Towards Passive Medication Adherence Monitoring of Parkinson's Disease Using Smartphone-based Gait Assessment

Authors:

Aditya Singh Rathore,

Wenyao XuAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 3, Issue 3

Article No.: 123, Pages 1 - 23

https://doi.org/10.1145/3351281

Published: 09 September 2019 Publication History

Abstract

The medicine adherence in Parkinson's disease (PD) treatment has attracted tremendous attention due to the critical consequences it can lead to otherwise. As a result, clinics need to ensure that the medicine intake is performed on time. Existing approaches, such as self-report, family reminder, and pill counts, heavily rely on the patients themselves to log the medicine intake (hereafter, patient involvement). Unfortunately, PD patients usually suffer from impaired cognition or memory loss, which leads to the so-called medication non-adherence, including missed doses, extra doses, and mistimed doses. These instances can nullify the treatment or even harm the patients. In this paper, we present PDMove, a smartphone-based passive sensing system to facilitate medication adherence monitoring without the need for patient involvement. Specifically, PDMove builds on the fact that PD patients will present gait abnormality if they do not follow medication treatment. To begin with, PDMove passively collects gait data while putting the smartphone in the pocket. Afterward, the gait preprocessor helps extract gait cycle containing the Parkinsonism-related biomarkers. Finally, the medicine intake detector consisting of a multi-view convolutional neural network predicts the medicine intake. In this way, PDMove enables the medication adherence monitoring. To evaluate PDMove, we enroll 247 participants with PD and collect more than 100,000 gait cycle samples. Our results show that smartphone-based gait assessment is a feasible approach to the AI-care strategy to monitor the medication adherence of PD patients.

References

[1]

J. E. Fleisher and M. B. Stern, "Medication nonadherence in parkinson's disease," Current neurology and neuroscience reports, vol. 13, no. 10, p. 382, 2013.

[2]

K. L. Davis, H. M. Edin, and J. K. Allen, "Prevalence and cost of medication nonadherence in parkinson's disease: evidence from administrative claims data," Movement Disorders, vol. 25, no. 4, pp. 474--480, 2010.

[3]

K. A. Grosset, J. L. Reid, and D. G. Grosset, "Medicine-taking behavior: Implications of suboptimal compliance in parkinson's disease," Movement disorders: official journal of the Movement Disorder Society, vol. 20, no. 11, pp. 1397--1404, 2005.

[4]

K. A. Grosset, I. Bone, and D. G. Grosset, "Suboptimal medication adherence in parkinson's disease," Movement disorders: official journal of the Movement Disorder Society, vol. 20, no. 11, pp. 1502--1507, 2005.

[5]

M. R. DiMatteo, H. S. Lepper, and T. W. Croghan, "Depression is a risk factor for noncompliance with medical treatment: meta-analysis of the effects of anxiety and depression on patient adherence," Archives of internal medicine, vol. 160, no. 14, pp. 2101--2107, 2000.

[6]

C. G. Goetz, M. Emre, and B. Dubois, "Parkinson's disease dementia: definitions, guidelines, and research perspectives in diagnosis," Annals of Neurology: Official Journal of the American Neurological Association and the Child Neurology Society, vol. 64, no. S2, pp. S81-S92, 2008.

[7]

K. Insel, D. Morrow, B. Brewer, and A. Figueredo, "Executive function, working memory, and medication adherence among older adults," The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, vol. 61, no. 2, pp. P102-P107, 2006.

[8]

G. P. Stoehr, S.-Y. Lu, L. Lavery, J. Vander Bilt, J. A. Saxton, C.-C. H. Chang, and M. Ganguli, "Factors associated with adherence to medication regimens in older primary care patients: the steel valley seniors survey," The American journal of geriatric pharmacotherapy, vol. 6, no. 5, pp. 255--263, 2008.

[9]

D. E. Clarkesmith, H. M. Pattison, P. H. Khaing, and D. A. Lane, "Educational and behavioural interventions for anticoagulant therapy in patients with atrial fibrillation," Cochrane Database of Systematic Reviews, no. 4, 2017.

[10]

E. Costa, A. Giardini, M. Savin, E. Menditto, E. Lehane, O. Laosa, S. Pecorelli, A. Monaco, and A. Marengoni, "Interventional tools to improve medication adherence: review of literature," Patient preference and adherence, vol. 9, p. 1303, 2015.

[11]

D. Aarsland, K. Andersen, J. P. Larsen, and A. Lolk, "Prevalence and characteristics of dementia in parkinson disease: an 8-year prospective study," Archives of neurology, vol. 60, no. 3, pp. 387--392, 2003.

[12]

M. Schenkman, C. G. Moore, W. M. Kohrt, D. A. Hall, A. Delitto, C. L. Comella, D. A. Josbeno, C. L. Christiansen, B. D. Berman, B. M. Kluger et al., "Effect of high-intensity treadmill exercise on motor symptoms in patients with de novo parkinson disease: a phase 2 randomized clinical trial," JAMA neurology, vol. 75, no. 2, pp. 219--226, 2018.

[13]

H. Kim, H. J. Lee, W. Lee, S. Kwon, S. K. Kim, H. S. Jeon, H. Park, C. W. Shin, W. J. Yi, B. S. Jeon et al., "Unconstrained detection of freezing of gait in parkinson's disease patients using smartphone," in Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE. IEEE, 2015, pp. 3751--3754.

[14]

"A survey on gait recognition," ACM Comput. Surv., vol. 51, no. 5, pp. 89:1-89:35, Aug. 2018. {Online}. Available:

Digital Library

[15]

R. Iansek, F. Huxham, and J. McGinley, "The sequence effect and gait festination in parkinson disease: Contributors to freezing of gait?" Movement Disorders, vol. 21, no. 9, pp. 1419--1424. {Online}. Available:

[16]

C. G. Goetz, B. C. Tilley, S. R. Shaftman, G. T. Stebbins, S. Fahn, P. Martinez-Martin, W. Poewe, C. Sampaio, M. B. Stern, R. Dodel et al., "Movement disorder society-sponsored revision of the unified parkinson's disease rating scale (mds-updrs): Scale presentation and clinimetric testing results," Movement disorders, vol. 23, no. 15, pp. 2129--2170, 2008.

[17]

J. O. Smith, Introduction to Digital Filters with Audio Applications. http://www.w3k.org/books/: W3K Publishing, 2007.

[18]

R. W. Bohannon, "Comfortable and maximum walking speed of adults aged 20âĂŤ79 years: reference values and determinants," Age and ageing, vol. 26, no. 1, pp. 15--19, 1997.

[19]

A. Mirelman, P. Bonato, R. Camicioli, T. D. Ellis, N. Giladi, J. L. Hamilton, C. J. Hass, J. M. Hausdorff, E. Pelosin, and Q. J. Almeida, "Gait impairments in parkinson's disease," The Lancet Neurology, 2019.

[20]

V. Radu, C. Tong, S. Bhattacharya, N. D. Lane, C. Mascolo, M. K. Marina, and F. Kawsar, "Multimodal deep learning for activity and context recognition," Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 1, no. 4, p. 157, 2018.

Digital Library

[21]

A. Bhandare, M. Bhide, P. Gokhale, and R. Chandavarkar, "Applications of convolutional neural networks," International Journal of Computer Science and Information Technologies, vol. 7, no. 5, pp. 2206--2215, 2016.

[22]

K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770--778.

[23]

I. Goodfellow, Y. Bengio, and A. Courville, Deep learning. MIT press, 2016.

Digital Library

[24]

E. S. Olivas, Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques: Algorithms, Methods, and Techniques. IGI Global, 2009.

Digital Library

[25]

G. A. Kang, J. M. Bronstein, D. L. Masterman, M. Redelings, J. A. Crum, and B. Ritz, "Clinical characteristics in early parkinson's disease in a central california population-based study," Movement disorders: official journal of the Movement Disorder Society, vol. 20, no. 9, pp. 1133--1142, 2005.

[26]

Y. Ouchi, H. Rakugi, H. Arai, M. Akishita, H. Ito, K. Toba, I. Kai et al., "Redefining the elderly as aged 75 years and older: proposal from the joint committee of japan gerontological society and the japan geriatrics society," Geriatr Gerontol Int, vol. 17, no. 7, pp. 1045--7, 2017.

[27]

S.-u. Ko, J. M. Hausdorff, and L. Ferrucci, "Age-associated differences in the gait pattern changes of older adults during fast-speed and fatigue conditions: results from the baltimore longitudinal study of ageing," Age and ageing, vol. 39, no. 6, pp. 688--694, 2010.

[28]

S.-u. Ko, M. I. Tolea, J. M. Hausdorff, and L. Ferrucci, "Sex-specific differences in gait patterns of healthy older adults: results from the baltimore longitudinal study of aging," Journal of biomechanics, vol. 44, no. 10, pp. 1974--1979, 2011.

[29]

A. L. Benabid, "Deep brain stimulation for parkinsonâĂ&Zacute;s disease," Current opinion in neurobiology, vol. 13, no. 6, pp. 696--706, 2003.

[30]

E. Ertin, N. Stohs, S. Kumar, A. Raij, M. al'Absi, and S. Shah, "Autosense: unobtrusively wearable sensor suite for inferring the onset, causality, and consequences of stress in the field," in Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems. ACM, 2011, pp. 274--287.

Digital Library

[31]

K. S. Chun, S. Bhattacharya, and E. Thomaz, "Detecting eating episodes by tracking jawbone movements with a non-contact wearable sensor," Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 2, no. 1, p. 4, 2018.

Digital Library

[32]

T. Hamatani, M. Elhamshary, A. Uchiyama, and T. Higashino, "Fluidmeter: Gauging the human daily fluid intake using smartwatches," Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 2, no. 3, p. 113, 2018.

Digital Library

[33]

S. Curci, A. Mura, and D. Riboni, "Toward naturalistic self-monitoring of medicine intake," in Proceedings of the 12th Biannual Conference on Italian SIGCHI Chapter. ACM, 2017, p. 3.

Digital Library

[34]

R. De Oliveira, M. Cherubini, and N. Oliver, "Movipill: improving medication compliance for elders using a mobile persuasive social game," in Proceedings of the 12th ACM international conference on Ubiquitous computing. ACM, 2010, pp. 251--260.

Digital Library

[35]

M. L. Lee and A. K. Dey, "Real-time feedback for improving medication taking," in Proceedings of the 32nd annual ACM conference on Human factors in computing systems. ACM, 2014, pp. 2259--2268.

Digital Library

[36]

V. Sharma, K. Mankodiya, F. De La Torre, A. Zhang, N. Ryan, T. G. Ton, R. Gandhi, and S. Jain, "Spark: personalized parkinson disease interventions through synergy between a smartphone and a smartwatch," in International Conference of Design, User Experience, and Usability. Springer, 2014, pp. 103--114.

[37]

S. Arora, V. Venkataraman, A. Zhan, S. Donohue, K. Biglan, E. Dorsey, and M. Little, "Detecting and monitoring the symptoms of parkinson's disease using smartphones: A pilot study," Parkinsonism & related disorders, vol. 21, no. 6, pp. 650--653, 2015.

[38]

H. Zhang, A. Wang, D. Li, and W. Xu, "Deepvoice: A voiceprint-based mobile health framework for parkinson's disease identification," in Biomedical & Health Informatics (BHI), 2018 IEEE EMBS International Conference on. IEEE, 2018, pp. 214--217.

[39]

H. Zhang, C. Song, A. Wang, C. Xu, D. Li, and W. Xu, "Pdvocal: Towards privacy-preserving parkinson's disease detection using non-speech body sounds."

Digital Library

[40]

S. T. Moore, H. G. MacDougall, and W. G. Ondo, "Ambulatory monitoring of freezing of gait in parkinson's disease," Journal of neuroscience methods, vol. 167, no. 2, pp. 340--348, 2008.

[41]

S. Mazilu, M. Hardegger, Z. Zhu, D. Roggen, G. Troster, M. Plotnik, and J. M. Hausdorff, "Online detection of freezing of gait with smartphones and machine learning techniques," in Pervasive Computing Technologies for Healthcare (PervasiveHealth), 2012 6th International Conference on. IEEE, 2012, pp. 123--130.

[42]

K. Wang, B. Li, D. Gu, K. Dai, and L. Zhou, "A smartphone based system for freezing of gait monitoring for parkinson's disease patients," in Communication Software and Networks (ICCSN), 2017 IEEE 9th International Conference on. IEEE, 2017, pp. 1529--1533.

[43]

M. Achey, J. L. Aldred, N. Aljehani, B. R. Bloem, K. M. Biglan, P. Chan, E. Cubo, E. R. Dorsey, C. G. Goetz, M. Guttman et al., "The past, present, and future of telemedicine for parkinson's disease," Movement disorders, vol. 29, no. 7, pp. 871--883, 2014.

[44]

InformedHealth.org. (2015) Parkinson's: Drug treatment. {Online}. Available: https://www.ncbi.nlm.nih.gov/books/NBK293715/

[45]

M. R. De Jong, M. Van der Elst, and K. A. Hartholt, "Drug-related falls in older patients: implicated drugs, consequences, and possible prevention strategies," Therapeutic advances in drug safety, vol. 4, no. 4, pp. 147--154, 2013.

[46]

M. Little, P. Wicks, T. Vaughan, and A. Pentland, "Quantifying short-term dynamics of parkinson's disease using self-reported symptom data from an internet social network," Journal of medical Internet research, vol. 15, no. 1, p. e20, 2013.

Cited By

Lang Cvan Dieen JBrodie MWelzel JMaetzler WSingh NRavi D(2024)Complexities and challenges of translating intervention success to real world gait in people with Parkinson’s diseaseFrontiers in Neurology10.3389/fneur.2024.145569215Online publication date: 9-Oct-2024
https://doi.org/10.3389/fneur.2024.1455692
Zhang WDai HXia DPan YLi ZWang WLi ZWang LChen G(2024)mP-Gait: Fine-grained Parkinson's Disease Gait Impairment Assessment with Robust Feature AnalysisProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36785778:3(1-31)Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1145/3678577
Ning JXie LYan ZBu YLuo JGanesan DShi W(2024)MoiréVision: A Generalized Moiré-based Mechanism for 6-DoF Motion SensingProceedings of the 30th Annual International Conference on Mobile Computing and Networking10.1145/3636534.3649374(467-481)Online publication date: 29-May-2024
https://dl.acm.org/doi/10.1145/3636534.3649374
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Index Terms

PDMove: Towards Passive Medication Adherence Monitoring of Parkinson's Disease Using Smartphone-based Gait Assessment
1. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile devices

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 3, Issue 3

September 2019

1415 pages

EISSN:2474-9567

DOI:10.1145/3361560

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Copyright © 2019 ACM.

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Published: 09 September 2019

Published in IMWUT Volume 3, Issue 3

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Cited By

Lang Cvan Dieen JBrodie MWelzel JMaetzler WSingh NRavi D(2024)Complexities and challenges of translating intervention success to real world gait in people with Parkinson’s diseaseFrontiers in Neurology10.3389/fneur.2024.145569215Online publication date: 9-Oct-2024
https://doi.org/10.3389/fneur.2024.1455692
Zhang WDai HXia DPan YLi ZWang WLi ZWang LChen G(2024)mP-Gait: Fine-grained Parkinson's Disease Gait Impairment Assessment with Robust Feature AnalysisProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36785778:3(1-31)Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1145/3678577
Ning JXie LYan ZBu YLuo JGanesan DShi W(2024)MoiréVision: A Generalized Moiré-based Mechanism for 6-DoF Motion SensingProceedings of the 30th Annual International Conference on Mobile Computing and Networking10.1145/3636534.3649374(467-481)Online publication date: 29-May-2024
https://dl.acm.org/doi/10.1145/3636534.3649374
Gaw NLi JYoon H(2024)A Novel Semi-Supervised Learning Model for Smartphone-Based Health TelemonitoringIEEE Transactions on Automation Science and Engineering10.1109/TASE.2022.321813221:1(428-441)Online publication date: Jan-2024
https://doi.org/10.1109/TASE.2022.3218132
Xu XZhang QNing RXin CWu H(2024)SPOT: Structure Patching and Overlap Tweaking for Effective Pipelining in Privacy-Preserving MLaaS with Tiny Clients2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS)10.1109/ICDCS60910.2024.00124(1318-1329)Online publication date: 23-Jul-2024
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