About this Research Topic
Parkinsons’ Disease and related disorders are a growing concern worldwide due to their chronic nature, with enormous implications for national health systems. They exhibit increasing incidence and prevalence rates due in part to environmental factors and longer life expectancy.
Patients with these chronic disorders require treatment, continual palliative attention, rehabilitation, and caregiving. An ad hoc functional monitoring allowing for a detailed data collection, dedicated to attempt a representation of the symptoms in patients’ everyday life, is needed to better investigate the progression of the disease providing important data to research. Notably, wearable disposals have been used so far in PD patients, detecting tremor and gait abnormalities for example. Functional Monitoring can be performed via Multimodal Tracking using Machine Learning Methods.
Multimodal Tracking implies the use of simple non-invasive methods applied to patients who are homebound or are being treated in care associations. Tracking aims to gather data at different levels, from different sensory modalities. Machine Learning Methods process large amounts of implicitly related correlates from axial motor symptoms, as limb movement, for instance in handwriting with speech-related symptoms affecting respiration, phonation, articulation, and fluency.
Machine Learning (Deep Learning, Big Data Handling, Statistical Pattern Recognition) will help to handle Multimodal Tracking measures for speech and limb neuromotor activity, that are complex and diverse, allowing to predict and monitor the progression of the disease in large data sets.
Different solutions have been proposed in the last years to monitor Parkinson's and related disorders’ patients. There has been an increased focus on handwriting evaluation, speech competence analysis, gait, and body movement assessment, and object handling, as well as cognitive testing. Multimodal Tracking related to Language and Speech evaluation implies the use of Surface Face and Limb Electromyography (sFLEMG), Electroencephalography from speech-production related brain areas (EEG), 3D Face and Limb Accelerometry (3DFLAcc), Facial Image Recognition and Monitoring (FIRM), Speech Recordings (SR), Electropalatography (EPG), and others related.
Future developments include the use of multi-trait drilling, novel technologies testing and machine learning methods. These technologies are extremely helpful when having to deal with important and abundant data that needs to be processed in order to move towards an optimized prediction and correction approach, reporting to clinicians and caregivers.
The purpose of the Research Topic is to present a detailed view of the current achievements in this field, focusing on speech production, language processing, writing and motor abilities, that are notably and evidently affected in the progression of the disorders, in particular in Parkinsons’ Disease.
We would like to provide a perspective view of future developments in patient care, monitoring, and rehabilitation.
This Research Topic welcomes contributions focusing on the evaluation and monitoring of Parkinson’s and related disorders, patients. Attention should be directed towards speech and related neuromotor capacities evaluation and testing, addressing the following subtopics:
- Speech Neuromotor Evaluation
- Handwriting Neuromotor Testing
- Neurolinguistic Cognition Assessment
State-of-the-Art review papers on their field of competence, consolidated research availed by relevant results, challenging and speculative research, position papers where promising methodologies are presented, as well as results from young researchers, are welcome.
Keywords: Speech neuromotor evaluation, cognitive evaluation, multimodal tracking, neurodegenerative disorders, neurolinguistic cognition assessment
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