Extended Deadline: 10th April 2015
First Reviews Due: 10th May 2015
Revised Manuscript Due: 12th June 2015
Final Decision: 6th July 2015
Karl E. Friedl
University of California
San Francisco, USA
University of Erlangen
The Tel Aviv Sourasky Medical Center
Tel Aviv, Israel
Early diagnosis of Parkinson’s Disease (PD) is clinically challenging due to the lack of a gold-standard test for the condition. Impaired motor function and behavior are the key clinical manifestations of the disease. The recent years have witnessed intense growth in the development of engineering solutions for both the assessment and management of patients with PD. real-time monitoring of locomotion and biosignals, signal processing and machine learning provide an excellent non-invasive basis for detecting normal and abnormal motor function. The purpose of this special issue is to address key topics in wearable sensor development, sensor data processing and novel analytical tools relevant to the diagnosis, prognosis and long-term quality of life assessment of PD patients. Our focus centers on the employment of synergistic approaches including engineering, mathematics, physics, medicine and biology.
The following topics are covered in this Special Issue of IEEE Journal of Biomedical and Health Informatics:
- Novel approaches in real-time machine learning with wearable and home monitoring technologies for the assessment of Parkinson’s disease progression.
- Advances in minimally invasive and less intrusive technologies for continuous monitoring.
- New assistive devices and systems for self-management.
- Novel predictive strategies for early detection of prodromal PD based on the analysis of fine motor changes.
- Advances in sensor-based gait assessment of Parkinson’s Disease.
- Telemedicine systems.
- Novel approaches in Machine-Machine interaction applied to diagnosis and treatment of Parkinson’s Disease.
- Biobanking of large samples of clinically annotated movements from everyday activities.
For more information, please refer to the Call-for-Papers (PDF).
This special issue is now closed. See all Past Special Issues.