The number of patients with neurological diseases, such as stroke, Parkinson’s disease, dementia, etc, is growing globally because of the aging population as well as an increasing trend of the disease incidence in younger people. Long-term neurorehabilitation for the patients has been challenging in the traditional rehabilitation services which heavily rely on manual operations by professional manpower in diagnoses, treatments, and follow-ups. The challenge may become even more problematic when the patient population expanding. Furthermore, the traditional out-patient rehabilitation services in clinics and hospitals could be easily disrupted by epidemic controls, e.g., quarantines in the recent COVID-19 pandemic.
Long-term and continuous neurorehabilitation is essential for alleviating symptoms of these diseases and restoring the lost neurological functions for the patients, so as to help them regain or maintain necessary independence in their daily life. Automations now become a new trend in neurorehabilitation services, complementary to the traditional long-term service, by facilitating self-help management with minimized close contact and involvement of human professionals, meanwhile without sacrificing the rehabilitation quality. For example, a patient can receive the routine treatment and preliminary diagnoses by self-help operation at home; and/or a human professional can serve in and manage more rehabilitation processes of different clients remotely at the same time, when part of the manpower is released by the automation.
Successful neurorehabilitation depends on timely diagnoses and adaptive treatments with adequate follow-ups. This Research Topic aims to discuss innovative and multi-disciplinary technologies and methodologies, such as bio-sensors, robotics, neuroinformatics, artificial intelligence and telecommunications, and their potential applications for self-help rehabilitation management, the clinical optimization and validations (e.g., clinical trials) in long-term neurorehabilitation. Investigations in diagnosis, rehabilitation treatments, quantitative evaluations, and distributed management are welcome. Compared with conventional rehabilitation, automatic operation lacks frequent human interaction, thus this topic also covers studies improving the communicative interactions, for example, the motivation of patients by doll robotic therapies.
Sub-themes of this Research Topic include, but are not limited to the following:
a) Automated neurorehabilitation
b) Brain machine/computer interfaces
c) Brain activity recording and monitoring devices
d) Data mining and information processing for neural data
e) Effectiveness and performance of neurologist-patient delivery of telemedicine and e-care
f) Home-based tele-rehabilitation
g) Learning paradigms and algorithms in rehabilitation systems
h) Neuro-robotic system for rehabilitation
i) Neuro-stimulation
j) Neural signal processing in rehabilitation neural sensors
k) Quantitative evaluations on rehabilitation effectiveness
The number of patients with neurological diseases, such as stroke, Parkinson’s disease, dementia, etc, is growing globally because of the aging population as well as an increasing trend of the disease incidence in younger people. Long-term neurorehabilitation for the patients has been challenging in the traditional rehabilitation services which heavily rely on manual operations by professional manpower in diagnoses, treatments, and follow-ups. The challenge may become even more problematic when the patient population expanding. Furthermore, the traditional out-patient rehabilitation services in clinics and hospitals could be easily disrupted by epidemic controls, e.g., quarantines in the recent COVID-19 pandemic.
Long-term and continuous neurorehabilitation is essential for alleviating symptoms of these diseases and restoring the lost neurological functions for the patients, so as to help them regain or maintain necessary independence in their daily life. Automations now become a new trend in neurorehabilitation services, complementary to the traditional long-term service, by facilitating self-help management with minimized close contact and involvement of human professionals, meanwhile without sacrificing the rehabilitation quality. For example, a patient can receive the routine treatment and preliminary diagnoses by self-help operation at home; and/or a human professional can serve in and manage more rehabilitation processes of different clients remotely at the same time, when part of the manpower is released by the automation.
Successful neurorehabilitation depends on timely diagnoses and adaptive treatments with adequate follow-ups. This Research Topic aims to discuss innovative and multi-disciplinary technologies and methodologies, such as bio-sensors, robotics, neuroinformatics, artificial intelligence and telecommunications, and their potential applications for self-help rehabilitation management, the clinical optimization and validations (e.g., clinical trials) in long-term neurorehabilitation. Investigations in diagnosis, rehabilitation treatments, quantitative evaluations, and distributed management are welcome. Compared with conventional rehabilitation, automatic operation lacks frequent human interaction, thus this topic also covers studies improving the communicative interactions, for example, the motivation of patients by doll robotic therapies.
Sub-themes of this Research Topic include, but are not limited to the following:
a) Automated neurorehabilitation
b) Brain machine/computer interfaces
c) Brain activity recording and monitoring devices
d) Data mining and information processing for neural data
e) Effectiveness and performance of neurologist-patient delivery of telemedicine and e-care
f) Home-based tele-rehabilitation
g) Learning paradigms and algorithms in rehabilitation systems
h) Neuro-robotic system for rehabilitation
i) Neuro-stimulation
j) Neural signal processing in rehabilitation neural sensors
k) Quantitative evaluations on rehabilitation effectiveness
