It appears you don't have support to open PDFs in this web browser. To view this file, Open with your PDF reader
Abstract
Parkinson’s Disease (PD) is the second most common neurodegenerative disorder, affecting more than 1% of the population above 60 years old with both motor and non-motor symptoms of escalating severity as it progresses. Since it cannot be cured, treatment options focus on the improvement of PD symptoms. In fact, evidence suggests that early PD intervention has the potential to slow down symptom progression and improve the general quality of life in the long term. However, the initial motor symptoms are usually very subtle and, as a result, patients seek medical assistance only when their condition has substantially deteriorated; thus, missing the opportunity for an improved clinical outcome. This situation highlights the need for accessible tools that can screen for early motor PD symptoms and alert individuals to act accordingly. Here we show that PD and its motor symptoms can unobtrusively be detected from the combination of accelerometer and touchscreen typing data that are passively captured during natural user-smartphone interaction. To this end, we introduce a deep learning framework that analyses such data to simultaneously predict tremor, fine-motor impairment and PD. In a validation dataset from 22 clinically-assessed subjects (8 Healthy Controls (HC)/14 PD patients with a total data contribution of 18.305 accelerometer and 2.922 typing sessions), the proposed approach achieved 0.86/0.93 sensitivity/specificity for the binary classification task of HC versus PD. Additional validation on data from 157 subjects (131 HC/26 PD with a total contribution of 76.528 accelerometer and 18.069 typing sessions) with self-reported health status (HC or PD), resulted in area under curve of 0.87, with sensitivity/specificity of 0.92/0.69 and 0.60/0.92 at the operating points of highest sensitivity or specificity, respectively. Our findings suggest that the proposed method can be used as a stepping stone towards the development of an accessible PD screening tool that will passively monitor the subject-smartphone interaction for signs of PD and which could be used to reduce the critical gap between disease onset and start of treatment.
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer
Details
1 Aristotle University of Thessaloniki, Multimedia Understanding Group, Information Processing Laboratory, Department of Electrical and Computer Engineering, Thessaloníki, Greece (GRID:grid.4793.9) (ISNI:0000000109457005)
2 Aristotle University of Thessaloniki, Signal Processing and Biomedical Technology Unit, Telecommunications Laboratory, Department of Electrical and Computer Engineering, Thessaloníki, Greece (GRID:grid.4793.9) (ISNI:0000000109457005)
3 Technical University of Dresden, Department of Neurology, Dresden, Germany (GRID:grid.4488.0) (ISNI:0000 0001 2111 7257)
4 Papanikolaou Hospital, Third Neurological Clinic, Thessaloníki, Greece (GRID:grid.4488.0)
5 King’s College Hospital NHS Foundation Trust, International Parkinson Excellence Research Centre, London, United Kingdom (GRID:grid.429705.d) (ISNI:0000 0004 0489 4320)
6 Aristotle University of Thessaloniki, Signal Processing and Biomedical Technology Unit, Telecommunications Laboratory, Department of Electrical and Computer Engineering, Thessaloníki, Greece (GRID:grid.4793.9) (ISNI:0000000109457005); Khalifa University of Science and Technology, Department of Electrical Engineering and Computer Science/Department of Biomedical Engineering, Abu Dhabi, UAE (GRID:grid.440568.b) (ISNI:0000 0004 1762 9729)