Innovative Detection and Segmentation of Mobility Activities in Patients Living with Parkinson’s Disease Using a Single Ankle-Positioned Smartwatch
Abstract
:1. Introduction
2. Materials and Methods
2.1. Participants
2.2. Instrumentation
2.3. Experimental Procedures
2.4. Detection Algorithms
2.5. Manual Segmentation
2.6. Statistical Analyses
3. Results
3.1. Activity Detection
3.2. Activity Segmentation
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Sveinbjornsdottir, S. The Clinical Symptoms of Parkinson’s Disease. J. Neurochem. 2016, 139 (Suppl. S1), 318–324. [Google Scholar] [CrossRef] [PubMed]
- Casamassima, F.; Ferrari, A.; Milosevic, B.; Ginis, P.; Farella, E.; Rocchi, L. A Wearable System for Gait Training in Subjects with Parkinson’s Disease. Sensors 2014, 14, 6229–6246. [Google Scholar] [CrossRef] [PubMed]
- Choquette, S.; Hamel, M.; Boissy, P. Accelerometer-Based Wireless Body Area Network to Estimate Intensity of Therapy in Post-Acute Rehabilitation. J. Neuroeng. Rehabil. 2008, 5, 20. [Google Scholar] [CrossRef]
- Moore, S.T.; MacDougall, H.G.; Gracies, J.-M.; Cohen, H.S.; Ondo, W.G. Long-Term Monitoring of Gait in Parkinson’s Disease. Gait Posture 2007, 26, 200–207. [Google Scholar] [CrossRef]
- Del Din, S.; Godfrey, A.; Mazzà, C.; Lord, S.; Rochester, L. Free-Living Monitoring of Parkinson’s Disease: Lessons from the Field. Mov. Disord. 2016, 31, 1293–1313. [Google Scholar] [CrossRef] [PubMed]
- Kirk, C.; Zia Ur Rehman, R.; Galna, B.; Alcock, L.; Ranciati, S.; Palmerini, L.; Garcia-Aymerich, J.; Hansen, C.; Schaeffer, E.; Berg, D.; et al. Can Digital Mobility Assessment Enhance the Clinical Assessment of Disease Severity in Parkinson’s Disease? J. Park. Dis. 2023, 13, 999–1009. [Google Scholar] [CrossRef]
- Mirelman, A.; Volkov, J.; Salomon, A.; Gazit, E.; Nieuwboer, A.; Rochester, L.; Del Din, S.; Avanzino, L.; Pelosin, E.; Bloem, B.R.; et al. Digital Mobility Measures: A Window into Real-World Severity and Progression of Parkinson’s Disease. Mov. Disord. 2024, 39, 328–338. [Google Scholar] [CrossRef]
- Moradi, H.; Hannink, J.; Stallforth, S.; Gladow, T.; Ringbauer, S.; Mayr, M.; Winkler, J.; Klucken, J.; Eskofier, B.M. Monitoring Medication Optimization in Patients with Parkinson’s Disease. In Proceedings of the 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Sydney, Australia, 24–27 July 2023; pp. 1–4. [Google Scholar]
- Okuma, Y.; Yanagisawa, N. The Clinical Spectrum of Freezing of Gait in Parkinson’s Disease. Mov. Disord. 2008, 23, S426–S430. [Google Scholar] [CrossRef]
- Mughal, H.; Javed, A.R.; Rizwan, M.; Almadhor, A.S.; Kryvinska, N. Parkinson’s Disease Management via Wearable Sensors: A Systematic Review. IEEE Access 2022, 10, 35219–35237. [Google Scholar] [CrossRef]
- Jung, S.; Michaud, M.; Oudre, L.; Dorveaux, E.; Gorintin, L.; Vayatis, N.; Ricard, D. The Use of Inertial Measurement Units for the Study of Free Living Environment Activity Assessment: A Literature Review. Sensors 2020, 20, 5625. [Google Scholar] [CrossRef]
- Nguyen, H.; Lebel, K.; Boissy, P.; Bogard, S.; Goubault, E.; Duval, C. Auto Detection and Segmentation of Daily Living Activities during a Timed Up and Go Task in People with Parkinson’s Disease Using Multiple Inertial Sensors. J. Neuroeng. Rehabil. 2017, 14, 26. [Google Scholar] [CrossRef] [PubMed]
- Nguyen, H.; Lebel, K.; Bogard, S.; Goubault, E.; Boissy, P.; Duval, C. Using Inertial Sensors to Automatically Detect and Segment Activities of Daily Living in People with Parkinson’s Disease. IEEE Trans. Neural Syst. Rehabil. Eng. 2018, 26, 197–204. [Google Scholar] [CrossRef] [PubMed]
- Jalloul, N.; Porée, F.; Viardot, G.; L’Hostis, P.; Carrault, G. Detection of Levodopa Induced Dyskinesia in Parkinson’s Disease Patients Based on Activity Classification. In Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy, 25–29 August 2015; Volume 2015, pp. 5134–5137. [Google Scholar] [CrossRef]
- Stavropoulos, T.G.; Papastergiou, A.; Mpaltadoros, L.; Nikolopoulos, S.; Kompatsiaris, I. IoT Wearable Sensors and Devices in Elderly Care: A Literature Review. Sensors 2020, 20, 2826. [Google Scholar] [CrossRef]
- Botros, A.; Schütz, N.; Camenzind, M.; Urwyler, P.; Bolliger, D.; Vanbellingen, T.; Kistler, R.; Bohlhalter, S.; Müri, R.M.; Mosimann, U.P.; et al. Long-Term Home-Monitoring Sensor Technology in Patients with Parkinson’s Disease-Acceptance and Adherence. Sensors 2019, 19, 5169. [Google Scholar] [CrossRef]
- Morgan, C.; Tonkin, E.L.; Craddock, I.; Whone, A.L. Acceptability of an In-Home Multimodal Sensor Platform for Parkinson Disease: Nonrandomized Qualitative Study. JMIR Hum. Factors 2022, 9, e36370. [Google Scholar] [CrossRef]
- Sun, X.; Qiu, L.; Wu, Y.; Cao, G. ActDetector: Detecting Daily Activities Using Smartwatches. In Proceedings of the 2017 IEEE 14th International Conference on Mobile Ad Hoc and Sensor Systems (MASS), Orlando, FL, USA, 22–25 October 2017; pp. 1–9. [Google Scholar]
- Navarro-López, V.; Fernández-Vázquez, D.; Molina-Rueda, F.; Cuesta-Gómez, A.; García-Prados, P.; del-Valle-Gratacós, M.; Carratalá-Tejada, M. Arm-Swing Kinematics in Parkinson’s Disease: A Systematic Review and Meta-Analysis. Gait Posture 2022, 98, 85–95. [Google Scholar] [CrossRef]
- Goubault, E.; Nguyen, H.P.; Bogard, S.; Blanchet, P.J.; Bézard, E.; Vincent, C.; Langlois, M.; Duval, C. Cardinal Motor Features of Parkinson’s Disease Coexist with Peak-Dose Choreic-Type Drug-Induced Dyskinesia. J. Park. Dis. 2018, 8, 323–331. [Google Scholar] [CrossRef]
- Goubault, E.; Nguyen, H.P.; Bogard, S.; Blanchet, P.J.; Bézard, E.; Vincent, C.; Sarna, J.; Monchi, O.; Duval, C. Remnants of Cardinal Symptoms of Parkinson’s Disease, Not Dyskinesia, Are Problematic for Dyskinetic Patients Performing Activities of Daily Living. Front. Neurol. 2019, 10, 256. [Google Scholar] [CrossRef] [PubMed]
- Ossig, C.; Antonini, A.; Buhmann, C.; Classen, J.; Csoti, I.; Falkenburger, B.; Schwarz, M.; Winkler, J.; Storch, A. Wearable Sensor-Based Objective Assessment of Motor Symptoms in Parkinson’s Disease. J. Neural Transm. 2016, 123, 57–64. [Google Scholar] [CrossRef]
- Barrachina-Fernández, M.; Maitín, A.M.; Sánchez-Ávila, C.; Romero, J.P. Wearable Technology to Detect Motor Fluctuations in Parkinson’s Disease Patients: Current State and Challenges. Sensors 2021, 21, 4188. [Google Scholar] [CrossRef]
- Adams, J.L.; Kangarloo, T.; Tracey, B.; O’Donnell, P.; Volfson, D.; Latzman, R.D.; Zach, N.; Alexander, R.; Bergethon, P.; Cosman, J.; et al. Using a Smartwatch and Smartphone to Assess Early Parkinson’s Disease in the WATCH-PD Study. npj Park. Dis. 2023, 9, 64. [Google Scholar] [CrossRef] [PubMed]
- Czech, M.; Patel, S. GaitPy: An Open-Source Python Package for Gait Analysis Using an Accelerometer on the Lower Back. J. Open Source Softw. 2019, 4, 1778. [Google Scholar] [CrossRef]
- El-Gohary, M.; Pearson, S.; McNames, J.; Mancini, M.; Horak, F.; Mellone, S.; Chiari, L. Continuous Monitoring of Turning in Patients with Movement Disability. Sensors 2014, 14, 356–369. [Google Scholar] [CrossRef] [PubMed]
- Jung, S.; de l’Escalopier, N.; Oudre, L.; Truong, C.; Dorveaux, E.; Gorintin, L.; Ricard, D. A Machine Learning Pipeline for Gait Analysis in a Semi Free-Living Environment. Sensors 2023, 23, 4000. [Google Scholar] [CrossRef]
- Packer, E.; Debelle, H.; Bailey, H.G.B.; Ciravegna, F.; Ireson, N.; Evers, J.; Niessen, M.; Shi, J.Q.; Yarnall, A.J.; Rochester, L.; et al. Translating Digital Healthcare to Enhance Clinical Management: A Protocol for an Observational Study Using a Digital Health Technology System to Monitor Medication Adherence and Its Effect on Mobility in People with Parkinson’s. BMJ Open 2023, 13, e073388. [Google Scholar] [CrossRef]
- Mannini, A.; Intille, S.S.; Rosenberger, M.; Sabatini, A.M.; Haskell, W. Activity Recognition Using a Single Accelerometer Placed at the Wrist or Ankle. Med. Sci. Sports Exerc. 2013, 45, 2193–2203. [Google Scholar] [CrossRef]
- Prigent, G.; Aminian, K.; Cereatti, A.; Salis, F.; Bonci, T.; Scott, K.; Mazzà, C.; Alcock, L.; Del Din, S.; Gazit, E.; et al. A Robust Walking Detection Algorithm Using a Single Foot-Worn Inertial Sensor: Validation in Real-Life Settings. Med. Biol. Eng. Comput. 2023, 61, 2341–2352. [Google Scholar] [CrossRef]
- Hughes, A.J.; Daniel, S.E.; Kilford, L.; Lees, A.J. Accuracy of Clinical Diagnosis of Idiopathic Parkinson’s Disease: A Clinico-Pathological Study of 100 Cases. J. Neurol. Neurosurg. Psychiatry 1992, 55, 181–184. [Google Scholar] [CrossRef]
- Ayachi, F.S.; Nguyen, H.P.; Lavigne-Pelletier, C.; Goubault, E.; Boissy, P.; Duval, C. Wavelet-Based Algorithm for Auto-Detection of Daily Living Activities of Older Adults Captured by Multiple Inertial Measurement Units (IMUs). Physiol. Meas. 2016, 37, 442–461. [Google Scholar] [CrossRef]
Characteristics | Mean ± SD | Range |
---|---|---|
Patients (n = 22, 11 Females) | ||
Age (year) | 66.3 ± 9.0 | 47–78 |
Weight (kg) | 72.9 ± 13.6 | 50–97 |
Height (cm) | 166.6 ± 8.9 | 152–180 |
Years since diagnosis | 7.1 ± 5.2 | 1–21 |
Comorbidity index (/18) | 4.8 ± 2.4 | 1–9 |
Moca (/30) | 27.1 ± 2.8 | 19–30 |
Notthingham ADL scale (/22) | 19.6 ± 1.6 | 16–22 |
MDS-UPDRS Part III On | ||
Speech (3.1) | 0.4 ± 0.6 | 0–2 |
Facial expression (3.2) | 0.6 ± 1.0 | 0–4 |
Neck rigidity (3.3) | 0.7 ± 0.8 | 0–2 |
Arm rigidity (3.3) | 0.9 ± 0.8 | 0–2 |
Leg rigidity (3.3) | 0.7 ± 0.7 | 0–2 |
Finger tapping (3.4) | 0.6 ± 0.7 | 0–2 |
Hand movements (3.5) | 0.8 ± 0.6 | 0–2 |
Pro-sup movements of hands (3.6) | 0.7 ± 0.7 | 0–2 |
Toe tapping (3.7) | 0.3 ± 0.4 | 0–1 |
Leg agility (3.8) | 0.3 ± 0.6 | 0–2 |
Arising from chair (3.9) | 0.1 ± 0.3 | 0–1 |
Gait (3.10) | 0.4 ± 0.8 | 0–2 |
Freezing of gait (3.11) | 0.1 ± 0.2 | 0–1 |
Postural stability (3.12) | 0.7 ± 0.8 | 0–2 |
Posture (3.13) | 0.4 ± 0.5 | 0–1 |
Body bradykinesia (3.14) | 0.3 ± 0.5 | 0–1 |
Postural tremor (3.15) | 0.4 ± 0.6 | 0–2 |
Kinetic tremor (3.16) | 0.7 ± 0.6 | 0–2 |
Rest tremor amplitude upper limbs (3.17) | 0.6 ± 0.8 | 0–3 |
Constancy of rest tremor (3.18) | 1.4 ± 1.3 | 0–4 |
Hoehn and Yahr score On | 1.4 ± 0.5 | 1–2 |
Task Detection | Algorithm Step | Sensor Used | Pre-Processing | Abbreviation |
---|---|---|---|---|
Walking | Threshold | Static az | 0.5 Hz HP filter | Thresh.Activity |
Initial walking | Ankle az | 0.5–0.8 Hz BP filter | ABPlow | |
Steps without walking | Ankle az | 0.5–3 Hz BP filter | ABPwalk | |
Turning | Initial turning | Ankle gz | 0.5 Hz LP filter | GLP |
Sitting-down/ Standing-up | RMS calculation from 1–25%, 25–75%, 75–100% of signals | Ankle gx, gy, gz | 4 Hz LP filter | GMVT |
Walking | Turning | Sitting-Down | Standing-Up | |
---|---|---|---|---|
Walking (n = 390) | 100 | 39.6 | 0.0 | 0.0 |
Turning (n = 641) | 0.0 | 100 | 0.0 | 0.0 |
Sitting-down (n = 51) | 2.3 | 0.0 | 93.2 | 0.0 |
Standing-up (n = 73) | 9.1 | 0.0 | 0.0 | 93.2 |
F-score | 74.2 | 100 | 92.1 | 74.5 |
Sen. (%) | 100 | 100 | 93.2 | 93.2 |
Spec. (%) | 71.6 | 100 | 97.8 | 91.7 |
Walking | Turning | Sitting-Down | Standing-Up | |
---|---|---|---|---|
Walking (n = 390) | 96.5 | 6.4 | 1.0 | 0.8 |
Turning (n = 641) | 4.9 | 90.2 | 0.3 | 1.1 |
Sitting-down (n = 51) | 39.2 | 25.5 | 57.5 | 0.0 |
Standing-up (n = 73) | 28.8 | 37.0 | 0.0 | 57.5 |
F-score | 96.0 | 91.7 | 52.3 | 54.1 |
Sen. (%) | 96.5 | 90.0 | 57.5 | 57.5 |
Spec. (%) | 94.7 | 93.6 | 70.5 | 72.9 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Goubault, E.; Duval, C.; Martin, C.; Lebel, K. Innovative Detection and Segmentation of Mobility Activities in Patients Living with Parkinson’s Disease Using a Single Ankle-Positioned Smartwatch. Sensors 2024, 24, 5486. https://doi.org/10.3390/s24175486
Goubault E, Duval C, Martin C, Lebel K. Innovative Detection and Segmentation of Mobility Activities in Patients Living with Parkinson’s Disease Using a Single Ankle-Positioned Smartwatch. Sensors. 2024; 24(17):5486. https://doi.org/10.3390/s24175486
Chicago/Turabian StyleGoubault, Etienne, Christian Duval, Camille Martin, and Karina Lebel. 2024. "Innovative Detection and Segmentation of Mobility Activities in Patients Living with Parkinson’s Disease Using a Single Ankle-Positioned Smartwatch" Sensors 24, no. 17: 5486. https://doi.org/10.3390/s24175486