ArticleFrontiers in neurology2017
A Perspective on Wearable Sensor Measurements and Data Science for Parkinson's Disease.
Article in Frontiers in neurology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Using an unbiased symbolic movement representation to characterize Parkinson's disease states.Scientific reports · 2020Trial
- Technological monitoring of motor parameters to assess multidimensional frailty of older people in the PRO-HOME project.Scientific reports · 2024Article
- Interpretable video-based tracking and quantification of parkinsonism clinical motor states.NPJ Parkinson's disease · 2024Article
- Continuous and Unconstrained Tremor Monitoring in Parkinson's Disease Using Supervised Machine Learning and Wearable Sensors.Parkinson's disease · 2024Article
- Clinical Management of Movement Disorders.Journal of clinical medicine · 2023Article
- Classifying Tremor Dominant and Postural Instability and Gait Difficulty Subtypes of Parkinson's Disease from Full-Body Kinematics.Sensors (Basel, Switzerland) · 2023Article
- Rapid Dynamic Naturalistic Monitoring of Bradykinesia in Parkinson's Disease Using a Wrist-Worn Accelerometer.Sensors (Basel, Switzerland) · 2021Article
- A Comparative Study of Time Frequency Representation Techniques for Freeze of Gait Detection and Prediction.Sensors (Basel, Switzerland) · 2021Article
- Automatic Resting Tremor Assessment in Parkinson's Disease Using Smartwatches and Multitask Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2021Article
- Deep Learning Approaches for Detecting Freezing of Gait in Parkinson's Disease Patients through On-Body Acceleration Sensors.Sensors (Basel, Switzerland) · 2020Article
- Leap motion controlled video game-based therapy for upper limb rehabilitation in patients with Parkinson's disease: a feasibility study.Journal of neuroengineering and rehabilitation · 2019Article
- Quantifying Motor Impairment in Movement Disorders.Frontiers in neuroscience · 2018Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Miniaturized and wearable sensor-based measurements enable the assessment of Parkinson's disease (PD) motor-related features like never before and hold great promise as non-invasive biomarkers for early and accurate diagnosis, and monitoring the progression of PD. High-fidelity human movement reconstruction and simulation can already be conducted in a clinical setting with increasingly precise and affordable motion technology enabling access to high-quality labeled data on patients' subcomponents of movement (kinematics and kinetics). At the same time, body-worn sensors now allow us to extend some quantitative movement-related measurements to patients' daily living activities. This era of patient movement "cognification" is bringing us previously inaccessible variables that encode patients' movement, and that, together with measures from clinical examinations, poses new challenges in data analysis. We present herein examples of the application of an unsupervised methodology to classify movement behavior in healthy individuals and patients with PD where no specific knowledge on the type of behaviors recorded is needed. We are most certainly leaving the early stage of the exponential curve that describes the current technological evolution and soon will be entering its steep ascent. But there is already a benefit to be derived from current motion technology and sophisticated data science methods to objectively measure parkinsonian impairments.
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