A Sensor-Based Perspective in Early-Stage Parkinson’s Disease: Current State and the Need for Machine Learning Processes
Abstract
:1. Introduction
2. Early Detection and the Need for Sensor-Based Approaches
Type of Sensor | Targeting/Monitoring | Ref. |
---|---|---|
Single-walled carbon nanotubes fabricated by sodium dodecyl sulfate | Simultaneous electrocatalytic determination of ascorbic acid (AA), dopamine (DA) and uric acid (UA) | [21] |
Nanosized copper oxide/multiwall carbon nanotubes | Electrocatalytic oxidation of dopamine monitoring | [23] |
Microneedle sensing platform | Electrochemical monitoring of levodopa (enzymatic–amperometric and nonenzymatic voltammetric detection) | [25] |
Electroanalytical assay using alpha-synuclein modified electrodes | a-synuclein detection through autoantibodies sampling | [27] |
Semiconductor quantum dots (CdSe/ZnS) | Mitochondrial complex I activity fluorescence monitoring | [28] |
DNA electrochemical biosensor through an imprinted polymer layer fabricated on a gold electrode | Nucleic acid degradation products determination (8-hydroxyguanine) | [29] |
Antibody-based biosensor on multiblock nanorods (Au and Ag)/biotinylated aptamers immobilization | Dopamine detection | [30,31] |
A segmented double-integration algorithm | Calculation of step length and step time from wearable inertial measurement units, spatiotemporal gait parameters measurement | [36] |
Embedded triaxial accelerometers from consumer smartwatches and multitask classification models | Assessment of the amplitude and constancy of resting tremor | [37] |
MCPD-Net, a multimodal deep learning model using visions accelerometer sensors | Effective representations of human movements prediction | [38] |
mKinetikos, a mobile-based system (mHealth system) | Continuous and remote monitoring of PD patients’ functional mobility and global clinical status | [39] |
Flexible wearable sensors attached to the hands, arms and thighs | Detection of bradykinesia and tremor in the upper extremities | [40] |
3. The Sensor Perspective
4. Recent Machine Learning Advancements in Sensor-Based Data
5. Ensemble Methods in Sensor-Based Data—Towards the Future Big Challenge
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Krokidis, M.G.; Dimitrakopoulos, G.N.; Vrahatis, A.G.; Tzouvelekis, C.; Drakoulis, D.; Papavassileiou, F.; Exarchos, T.P.; Vlamos, P. A Sensor-Based Perspective in Early-Stage Parkinson’s Disease: Current State and the Need for Machine Learning Processes. Sensors 2022, 22, 409. https://doi.org/10.3390/s22020409
Krokidis MG, Dimitrakopoulos GN, Vrahatis AG, Tzouvelekis C, Drakoulis D, Papavassileiou F, Exarchos TP, Vlamos P. A Sensor-Based Perspective in Early-Stage Parkinson’s Disease: Current State and the Need for Machine Learning Processes. Sensors. 2022; 22(2):409. https://doi.org/10.3390/s22020409
Chicago/Turabian StyleKrokidis, Marios G., Georgios N. Dimitrakopoulos, Aristidis G. Vrahatis, Christos Tzouvelekis, Dimitrios Drakoulis, Foteini Papavassileiou, Themis P. Exarchos, and Panayiotis Vlamos. 2022. "A Sensor-Based Perspective in Early-Stage Parkinson’s Disease: Current State and the Need for Machine Learning Processes" Sensors 22, no. 2: 409. https://doi.org/10.3390/s22020409
APA StyleKrokidis, M. G., Dimitrakopoulos, G. N., Vrahatis, A. G., Tzouvelekis, C., Drakoulis, D., Papavassileiou, F., Exarchos, T. P., & Vlamos, P. (2022). A Sensor-Based Perspective in Early-Stage Parkinson’s Disease: Current State and the Need for Machine Learning Processes. Sensors, 22(2), 409. https://doi.org/10.3390/s22020409