The DOI matches its Crossref record; 162 of the 162 references it cites were found in a registry or an index.
How Wearable Sensors Can Support Parkinson's Disease Diagnosis and Treatment: A Systematic Review
Open accessAbstract
Parkinson's disease (PD) is a common and disabling pathology that is characterized by both motor and non-motor symptoms and affects millions of people worldwide. The disease significantly affects quality of life of those affected. Many works in literature discuss the effects of the disease. The most promising trends involve sensor devices, which are low cost, low power, unobtrusive, and accurate in the measurements, for monitoring and managing the pathology. OBJECTIVES: This review focuses on wearable devices for PD applications and identifies five main fields: early diagnosis, tremor, body motion analysis, motor fluctuations (ON-OFF phases), and home and long-term monitoring. The concept is to obtain an overview of the pathology at each stage of development, from the beginning of the disease to consider early symptoms, during disease progression with analysis of the most common disorders, and including management of the most complicated situations (i.e., motor fluctuations and long-term remote monitoring). DATA SOURCES: The research was conducted within three databases: IEEE Xplore®, Science Direct®, and PubMed Central®, between January 2006 and December 2016. STUDY ELIGIBILITY CRITERIA: Since 1,429 articles were found, accurate definition of the exclusion criteria and selection strategy allowed identification of the most relevant papers. RESULTS: Finally, 136 papers were fully evaluated and included in this review, allowing a wide overview of wearable devices for the management of Parkinson's disease.
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Cites
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Members of the UPDRS Development Committee. Unified Parkinson's Disease Rating Scale
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The diagnosis of Parkinson’s disease
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Wearable Assistant for Parkinson’s Disease Patients With the Freezing of Gait Symptom
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“Moreover, feedback should be context-aware, because continuous cueing is not appreciated by patients (Bächlin et al., 2010) and the efficacy of cueing could decrease over time.”
BackgroundMethodInfluentialMonitoring Motor Fluctuations in Patients With Parkinson's Disease Using Wearable Sensors
Patel, S., Lorincz, K., Hughes, R., Huggins, N., Growdon, J., Standaert, D., Akay, M., Dy, J., Welsh, M., & Bonato, P. (2009) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2009.2033471 · Cited by 623
Cited by
Sim, I. (2019) · New England Journal of Medicine · doi:10.1056/nejmra1806949 · Cited by 491
Artificial intelligence for assisting diagnostics and assessment of Parkinson’s disease—A review
Belić, M., Bobić, V., Badža, M., Šolaja, N., Đurić-Jovičić, M., & Kostić, V. S. (2019) · Clinical Neurology and Neurosurgery · doi:10.1016/j.clineuro.2019.105442 · Cited by 194
Stretchable and Highly Sensitive Optical Strain Sensors for Human-Activity Monitoring and Healthcare
Guo, J., Zhou, B., Zong, R., Pan, L., Li, X., Yu, X., Yang, C., Kong, L., & Dai, Q. (2019) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.9b09815 · Cited by 170
Mahadevan, N., Demanuele, C., Zhang, H., Volfson, D., Ho, B., Erb, M. K., & Patel, S. (2020) · npj Digital Medicine · doi:10.1038/s41746-019-0217-7 · Cited by 138
Fit‐for‐Purpose Biometric Monitoring Technologies: Leveraging the Laboratory Biomarker Experience
Godfrey, A., Vandendriessche, B., Bakker, J. P., Fitzer‐Attas, C., Gujar, N., Hobbs, M., Liu, Q., Northcott, C. A., Parks, V., Wood, W. A., et al. (2021) · Clinical and Translational Science · doi:10.1111/cts.12865 · Cited by 60
Metadata
- Authors
- Rovini, E., Maremmani, C., & Cavallo, F.
- Venue
- Frontiers in Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2017-10-06
- Volume
- 11
- Pages
- 555-555
- ISSN
- 1662-453X (ISSN-L)
- OpenAlex
- W2763160983
- Cited by
- 426
- References
- 162
- FWCI
- 4.91
- Type
- journal article
- Language
- en
- Open access
- gold
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