Abstract
Mental workload is a neuroergonomic human factor, which is widely used in planning a system's safety and areas like brain–machine interface (BMI), neurofeedback, and assistive technologies. Robotic prosthetics methodologies are employed for assisting hemiplegic patients in performing routine activities. Assistive technologies' design and operation are required to have an easy interface with the brain with fewer protocols, in an attempt to optimize mobility and autonomy. The possible answer to these design questions may lie in neuroergonomics coupled with BMI systems. In this study, two human factors are addressed: designing a lightweight wearable robotic exoskeleton hand that is used to assist the potential stroke patients with an integrated portable brain interface using mental workload (MWL) signals acquired with portable functional near-infrared spectroscopy (fNIRS) system. The system may generate command signals for operating a wearable robotic exoskeleton hand using two-state MWL signals. The fNIRS system is used to record optical signals in the form of change in concentration of oxy and deoxygenated hemoglobin (HbO and HbR) from the pre-frontal cortex (PFC) region of the brain. Fifteen participants participated in this study and were given hand-grasping tasks. Two-state MWL signals acquired from the PFC region of the participant's brain are segregated using machine learning classifier—support vector machines (SVM) to utilize in operating a robotic exoskeleton hand. The maximum classification accuracy is 91.31%, using a combination of mean-slope features with an average information transfer rate (ITR) of 1.43. These results show the feasibility of a two-state MWL (fNIRS-based) robotic exoskeleton hand (BMI system) for hemiplegic patients assisting in the physical grasping tasks.
Highlights
Tetraplegia and stroke are among the major causes leading to lesser control over muscular movements (Blokland et al, 2014)
information transfer rate (ITR) is calculated from HbO, HbR, and HbT to examine the successful information transferred for the deigned soft exoskeleton system
The mental workload (MWL) control signals are applied to the exoskeletal hand online, but the results show that real-time testing can be applied but with a limited capability of ITR compared with EEG, which has high ITR and faster control translations (Spüler, 2017; Xing et al, 2018)
Summary
Tetraplegia and stroke are among the major causes leading to lesser control over muscular movements (Blokland et al, 2014) Patients suffering from such diseases show a declining trend in the uncontrolled motor movements during the later stages of the disease. The pattern of neural and hemodynamic signals in patients with the brain and spinal injuries differs from that of healthy patients (Käthner et al, 2017). For such patients, there is a need to devise a methodology to partially, if not fully, rehabilitate them, helping them in performing routine tasks (Hong and Santosa, 2016). Low cost, and non-invasiveness, techniques like electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) are commonly used in rehabilitation (Hong and Khan, 2017)
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