Disability limits an individual's ability to participate in everyday activities. Rehabilitation is specialized healthcare to improve, maintain or restore physical strength, cognition, and mobility. Exoskeletons are the external devices used to aid the disabled in performing daily life activities and restoring their strength and capability. Brain-computer interface (BCI) is a technique to use brain signals in controlling external devices directly. In this paper, BCI-based control of the lower limb exoskeleton is proposed using functional near-infrared spectroscopy (fNIRS). Brain signals for waking nine healthy subjects on a treadmill are recorded and pre-processed, followed by channel selection and feature extraction. Linear discriminant analysis is used to classify walking and rest signals and achieved significantly (p < 0.05) higher accuracy of 75.5 ± 13.0%. Furthermore, the proposed system showed better performance as compared to using all channels for classification. The better performance of the proposed methodology is a step forward to achieve intuitive control of BCI-based exoskeletons.

It is estimated that spinal cord injuries affect approximately 2.5 million people worldwide leaving them unable to perform activities of daily living. However, until the advent of lower limb exoskeletons, the wearer was unable to perform everyday activities such as walking and standing from a static position. A lower- limb exoskeleton is a wearable device encompassing the body to support the neuromuscular system's structural and functional features to assist paraplegic patients with activities of daily living. In this work, a novel, lightweight, and human friendly exoskeleton is designed and analyzed that adapts to the wearer according to his body configuration and allows him to perform 2 DOF movements during walking. Lower Limb exoskeleton is designed in CAD software's according to anthropomorphic requirements that ensures safety and ergonomics. The designed exoskeleton is modeled analytically using the Lagrangian approach and simulated using FEA software's to ensure the structural stability and visualize the motion of the exoskeleton in a virtual environment. From the results, it can be concluded that the proposed exoskeleton can be used for rehabilitation and walking purposes. The simulated results and diagrams of the gait cycle will be important for the development of a physical exoskeleton prototype. The study provides the basis for the development of a prototype lower limb exoskeleton to assist the disabled and elderly in standing and walking.

In this paper, we present a systematic method to reduce the time lag in detecting initial dips using a vector-based phase diagram and an autoregressive moving average with exogenous signals (ARMAX) model-based q-step-ahead prediction algorithm. With functional near-infrared spectroscopy (fNIRS), signals related to mental arithmetic and right-hand clenching are acquired from the prefrontal and left primary motor cortices, respectively. The interrelationship between oxygenated hemoglobin, deoxygenated hemoglobin, total hemoglobin and cerebral oxygen exchange are related to initial dips. Specifically, a threshold value from the resting state hemodynamics is incorporated, as a decision criterion, into the vector-based phase diagram to determine the occurrence of initial dips. To further reduce the time lag, a [Formula: see text]-step-ahead prediction method is applied to predict the occurrence of the dips. A combination of the threshold criterion and the prediction method resulted in the delay time of about 0.9[Formula: see text]s. The results demonstrate that rapid detection of initial dip is possible and therefore can be used for real-time brain-computer interfacing.