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Proof of Concept in Artificial-Intelligence-Based Wearable Gait Monitoring for Parkinson's Disease Management Optimization
| Content Provider | MDPI |
|---|---|
| Author | Robert, Radu Ileșan Claudia-Georgiana, Cordoș Laura-Ioana, Mihăilă Radu, Fleșar Lăcrămioara Perju-Dumbravă, Paul, Faragó Popescu, Ana-Sorina |
| Copyright Year | 2022 |
| Description | Parkinson’s disease (PD) is the second most common progressive neurodegenerative disorder, affecting 6.2 million patients and causing disability and decreased quality of life. The research is oriented nowadays toward artificial intelligence (AI)-based wearables for early diagnosis and long-term PD monitoring. Our primary objective is the monitoring and assessment of gait in PD patients. We propose a wearable physiograph for qualitative and quantitative gait assessment, which performs bilateral tracking of the foot biomechanics and unilateral tracking of arm balance. Gait patterns are assessed by means of correlation. The surface plot of a correlation coefficient matrix, generated from the recorded signals, is classified using convolutional neural networks into physiological or PD-specific gait. The novelty is given by the proposed AI-based decisional support procedure for gait assessment. A proof of concept of the proposed physiograph is validated in a clinical environment on five patients and five healthy controls, proving to be a feasible solution for ubiquitous gait monitoring and assessment in PD. PD management demonstrates the complexity of the human body. A platform empowering multidisciplinary, AI-evidence-based decision support assessments for optimal dosing between drug and non-drug therapy could lay the foundation for affordable precision medicine. |
| Starting Page | 189 |
| e-ISSN | 20796374 |
| DOI | 10.3390/bios12040189 |
| Journal | Biosensors |
| Issue Number | 4 |
| Volume Number | 12 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-03-23 |
| Access Restriction | Open |
| Subject Keyword | Biosensors Medical Informatics Artificial Intelligence Sensors Convolutional Neural Networks Parkinson's Disease Biomedical Monitoring Accelerometer Pressure Sensor Disease Management Electromyography Correlation |
| Content Type | Text |
| Resource Type | Article |
