Signal Processing for Neurorehabilitation and Assistive Technologies

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Signal Processing for Neurorehabilitation and Assistive Technologies

By: 
Dario Farina; Arash Mohammadi; Tulay Adali; Nitish V. Thakor; Konstantinos N. Plataniotis

During the last few decades, the number of seniors over the age of 60 has increased significantly. A recent study from the United Nations has shown that the number of people aged 65 years or over will increase from 727 million in 2020 to 1.5 billion by 2050 [1]. Consequently, the proportion of the global population aged 65 years or over will increase from 9.3% in 2020 to 16% in 2050. In parallel with the aging of the world population, there has been an increase in age-related health issues, such as stroke, sensorimotor disorders, Parkinson’s disease, and essential tremor, which significantly impact health-care systems. With a system that is underresourced, patients are transferred from hospitals to home while still suffering from major functional deficits. In this aging crisis, a potential solution is to develop technologies and techniques that can provide 1) efficient, effective, widely accessible, and affordable means of neurorehabilitation; and 2) intuitive and agile assistance to maximize the patients’ independence during activities of daily living. Biosignal processing (BSP) plays an imperative role in the development of these advanced, intelligent, and dynamic rehabilitation and assistive solutions.

Human-Machine Interfacing

Neurorehabilitation and assistive technologies are based on processing, decomposing, and decoding of bioelectrical, biomechanical, and biochemical signals. The nonstationary and nonlinear nature of biological signals requires innovative techniques beyond conventional approaches. The ultimate goal is to implement practical and effective augmentation techniques for the sensorimotor capabilities of patients to achieve either 1) the instantaneous replacement of lost functions (that is, assistive solution) or 2) the gradual enhancement of the residual functions (that is, rehabilitative solution). Achieving these goals require the development of human–machine interface (HMI) systems, which aim at the fusion of human neuromechanics and robotics.

 

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