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Skip search results from other journals and go to results- 2 JMIR Research Protocols
- 2 Journal of Medical Internet Research
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Detecting Parkinson Disease Using a Web-Based Speech Task: Observational Study
Little et al [12] introduced pitch period entropy (PPE) as a measure of dysphonia to distinguish healthy people from people with PD with 91% accuracy. Later, Tsanas [13] expanded on this by calculating 132 dysphonia measures to classify PD versus control with almost 99% accuracy. In addition, Peker et al [14] used a novel feature selection technique with a complex-valued artificial neural network.
J Med Internet Res 2021;23(10):e26305
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However, these benefits come at a cost: it is challenging to develop algorithms that can cope with the large variation in signals encountered in daily life, of which only a small proportion may be explained by PD-related impairments. Since obtaining accurate labels in daily life environments is difficult and costly, most of the currently available reference data sets have been collected in the lab and involved patients performing a standardized set of tasks and activities.
J Med Internet Res 2020;22(10):e19068
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Osteoarthritis is an example of such a condition. It is a prevalent, degenerative condition [1,2] where fluctuating pain and loss of mobility are the major symptoms. In knee osteoarthritis, increased physical activity may exacerbate knee pain. Conversely, certain forms of exercise are known to have a beneficial effect on pain symptoms [3,4]. Characterizing the relationship between pain and activity could help in the development of targeted interventions.
JMIR Res Protoc 2019;8(1):e10238
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Large-Scale Wearable Sensor Deployment in Parkinson’s Patients: The Parkinson@Home Study Protocol
First, evaluation of day-to-day variations in a complex disease such as PD is difficult when relying solely upon periodic consultations with experts working in a clinical setting [7].
JMIR Res Protoc 2016;5(3):e172
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