Smartphone-based continuous mobility monitoring of Parkinsons disease patients reveals impacts of ambulatory bout length on gait features
Abstract
Smartphone-based remote monitoring is a potential solution for providing long-term, objective assessment of gait and mobility in patients with Parkinsons disease (PD). In the Multiple Ascending Dose study of PRX002/RG7935, forty-four mild to moderate PD patients from cohorts 4 to 6 were included in a smartphone-based assessment for up to 24 weeks, while in a separate control study, thirty-five age-and gender-matched healthy individuals performed the same assessment up to 6 weeks. In total, over 30,000 hours of sensor data from subjects' daily activities were collected. A convolutional recurrent neural network was used for human activity recognition and extracted gait-related activities, followed by a mobility analysis on extracted mobility features during ambulatory bouts and turns. The analysis revealed that PD patients showed significantly lower mobility in terms of average ambulatory bout length - length of time of one continuous ambulatory segment, average per-step power, turn speed, and number of turns per ambulatory minute. In addition, bout-length stratified analysis shows the between-group difference of multiple features is associated with bout lengths. These study results support the potential use of smartphones for long-term mobility monitoring in future clinical practice, and also shed lights on previously inaccessible relationships between bout length and gait features under free-living condition.
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The HAR model was trained on two independent public data sets of everyday activity from normal individuals, with 90% used for training and 10% for validation.
“The HAR model was trained on two independent public data sets of everyday activity from normal individuals53, 54; of these data, 90% were used to train the HAR model and 10% for model validation (see Cheng and colleagues51 for details).”
From Evaluation of smartphone‐based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial (2018) · cites this paper as [51]
Cites
Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition
Ordóñez, F., & Roggen, D. (2016) · Sensors · doi:10.3390/s16010115 · Cited by 2,761 · cited 1× · see citing works
“The HAR model is composed of a 9-layer convolutional recurrent neural network (CRNN) adapted from previously published structure [10] and trained”
MethodRai, A., Chintalapudi, K. K., Padmanabhan, V. N., & Sen, R. (2012) · ACM Annual International Conference on Mobile Computing and Networking (MobiCom) · doi:10.1145/2348543.2348580 · Cited by 1,040 · see citing works
Systematic evaluation of rating scales for impairment and disability in Parkinson's disease
Ramaker, C. C., Marinus, J., Stiggelbout, A. M., & van Hilten, B. J. (2002) · Movement Disorders · doi:10.1002/mds.10248 · Cited by 664 · see citing works
Din, S. D., Godfrey, A., Galna, B., Lord, S., & Rochester, L. (2016) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-016-0154-5 · Cited by 352 · see citing works
Moving forward on gait measurement: Toward a more refined approach
Lord, S., Galna, B., & Rochester, L. (2013) · Movement Disorders · doi:10.1002/mds.25545 · Cited by 314 · see citing works
Continuous Monitoring of Turning in Patients with Movement Disability
El-Gohary, M., Pearson, S., McNames, J., Mancini, M., Horak, F., Mellone, S., & Chiari, L. (2013) · Sensors · doi:10.3390/s140100356 · Cited by 258 · see citing works
Real-time gait event detection for normal subjects from lower trunk accelerations
González, R. C., López, A. M., Rodríguez-Uría, J., Álvarez, D., & Alvarez, J. C. (2010) · Gait & Posture · doi:10.1016/j.gaitpost.2009.11.014 · Cited by 205 · see citing works
The association between retirement and age on physical activity in older adults
Godfrey, A., Lord, S. E., Galna, B., Mathers, J. C., Burn, D. J., & Rochester, L. (2013) · Age and Ageing · doi:10.1093/ageing/aft168 · Cited by 98 · see citing works
Step Detection Robust against the Dynamics of Smartphones
Lee, H.-H., Choi, S., & Lee, M. (2015) · Sensors · doi:10.3390/s151027230 · Cited by 63 · see citing works
Cheng, W., Scotland, A., Lipsmeier, F., Kilchenmann, T., Jin, L., Schjodt‐Eriksen, J., Wolf, D., Zhang, Y., Garcia, I. F., Siebourg‐Polster, J., et al. (2017) · IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies · doi:10.1109/chase.2017.87 · Cited by 56 · see citing works
Self-citationZijlstra, A., & Zijlstra, W. (2013) · Gait & Posture · doi:10.1016/j.gaitpost.2013.04.021 · Cited by 54 · see citing works
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Lipsmeier, F., Taylor, K. I., Kilchenmann, T., Wolf, D., Scotland, A., Schjodt‐Eriksen, J., Cheng, W., Fernandez‐Garcia, I., Siebourg‐Polster, J., Jin, L., et al. (2018) · Movement Disorders · doi:10.1002/mds.27376 · Cited by 266 · see citing works
Self-citationLipsmeier, F., Taylor, K. I., Postuma, R. B., Volkova-Volkmar, E., Kilchenmann, T., Mollenhauer, B., Bamdadian, A., Popp, W., Cheng, W., Zhang, Y., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-15874-4 · Cited by 113 · see citing works
Self-citationPolhemus, A. M., Delgado‐Ortiz, L., Brittain, G., Chynkiamis, N., Salis, F., Gaßner, H., Groß, M., Kirk, C., Rossanigo, R., Taraldsen, K., et al. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00513-5 · Cited by 101 · see citing works
Taylor, K. I., Staunton, H., Lipsmeier, F., Nobbs, D., & Lindemann, M. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0305-8 · Cited by 85 · see citing works
Self-citationMorgan, C., Rolinski, M., McNaney, R., Jones, B., Rochester, L., Maetzler, W., Craddock, I. J., & Whone, A. L. (2020) · Journal of Parkinson s Disease · doi:10.3233/jpd-191781 · Cited by 89 · see citing works
Zhang, H., Song, C., Rathore, A. S., Huang, M.-C., Zhang, Y., & Xu, W. (2020) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2020.2991813 · Cited by 48 · see citing works
Shah, V. V., McNames, J., Harker, G. R., Mancini, M., Carlson‐Kuhta, P., Nutt, J. G., El‐Gohary, M., Curtze, C., & Horak, F. B. (2020) · Sensors · doi:10.3390/s20205769 · Cited by 46 · see citing works
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Snyder, C. W., Dorsey, E. R., & Atreja, A. (2018) · Digital Biomarkers · doi:10.1159/000489224 · Cited by 19 · see citing works
Lu, J., Wang, Y., Shu, Z., Zhang, X., Wang, J., Cheng, Y., Zhu, Z., Yu, Y., Wu, J., Han, J., et al. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac861e · Cited by 12 · see citing works
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Proffitt, R. M., Ma, M., & Skubic, M. (2023) · Sensors · doi:10.3390/s23187872 · Cited by 7 · see citing works
Bogaarts, G., Zanon, M., Dondelinger, F., Derungs, A., Lipsmeier, F., Gossens, C., & Lindemann, M. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9630594 · Cited by 5 · see citing works
Self-citationPost, E., van Laarhoven, T., Raykov, Y. P., Little, M. A., Nonnekes, J. H., Heskes, T., Bloem, B. R., & Evers, L. J. W. (2025) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-025-01578-z · Cited by 4 · see citing works
- Authors
- Cheng, W.-Y., Lipsmeier, F., Scotland, A., Creagh, A., Kilchenmann, T., Jin, L., Schjodt-Eriksen, J., Wolf, D., Zhang-Schaerer, Y.-P., Garcia, I. F., Siebourg-Polster, J., Soto, J., Verselis, L., Facklam, M. M., Boess, F., Koller, M., Grundman, M., Monsch, A. U., Postuma, R., Ghosh, A., Kremer, T., Taylor, K. I., Czech, C., Gossens, C., & Lindemann, M.
- Affiliations
- F. Hoffmann-La Roche AG (CH)
- Prothena (United States) (US)
- University of California San Diego (US)
- University of Basel (CH)
- McGill University (CA)
- Baden-Wuerttemberg Cooperative State University (DE)
- ORCID
- Venue
- 2017 IEEE Life Sciences Conference (LSC)
- Publisher
- IEEE
- Published
- 2017-12
- Pages
- 166-169
- OpenAlex
- W2786078236
- Cited by
- 13
- References
- 11
- Citations by year
- 2025: 1 · 2023: 1 · 2022: 3 · 2021: 2 · 2020: 4 · 2018: 2
- FWCI
- 12.56
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Life Sciences Conference (LSC) (publishedVersion)
- Topics
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Keywords
- Parkinson's disease, gait analysis, mobility monitoring, human activity recognition, convolutional recurrent neural network
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