Hand rehabilitation exoskeletons are in need of improving key features such as simplicity, compactness, bi-directional actuation, low cost, portability, safe human-robotic interaction, and intuitive control. This article presents a brain-controlled hand exoskeleton based on a multi-segment mechanism driven by a steel spring. Active rehabilitation training is realized using a threshold of the attention value measured by an electroencephalography (EEG) sensor as a brain-controlled switch for the hand exoskeleton. We present a prototype implementation of this rigid-soft combined multi-segment mechanism with active training and provide a preliminary evaluation. The experimental results showed that the proposed mechanism could generate enough range of motion with a single input by distributing an actuated linear motion into the rotational motions of finger joints during finger flexion/extension. The average attention value in the experiment of concentration with visual guidance was significantly higher than that in the experiment without visual guidance. The feasibility of the attention-based control with visual guidance was proven with an overall exoskeleton actuation success rate of 95.54% (14 human subjects). In the exoskeleton actuation experiment using the general threshold, it performed just as good as using the customized thresholds; therefore, a general threshold of the attention value can be set for a certain group of users in hand exoskeleton activation.

Improving the effectiveness of artificial tactile sensors for soft-tissue examination and tumor localization is a pressing need in robot-assisted minimally invasive surgery. Despite the availability of tactile probes, guidelines for optimal palpation behavior that best exploit soft-tissue properties are not available as yet. Simulations on soft-tissue palpation show that particular stress-velocity patterns during tissue probing lead to constructive dynamic interactions between the probe and the tissue, enhancing the detection and localization of hard nodules. To the best of our knowledge, this is the first attempt to methodically evaluate the hypothesis that specific human palpation behaviors (defined by the fingers' velocity, trajectory, and exerted force) directly influence the diagnosis of soft-tissue organs. Here, we use simulation studies involving human participants to establish open hypotheses on the interaction and influence of relevant behavioral palpation variables, such as finger trajectory, its velocity, and force exerted by fingers on the accuracy of detecting embedded nodules. We validate this hypothesis through finite element analysis and the investigation of palpation strategies used by humans during straight unidirectional examination to detect hard nodules inside silicone phantoms and ex-vivo porcine organs. Thus, we conclude that the palpation strategy plays an important role during soft-tissue examination. Our findings allow us, for the first time, to derive palpation behavior guidelines suitable for the design of controllers of palpation robots.

Strokes are a leading cause of acquired disability worldwide, and there is a significant need for novel interventions and further research to facilitate functional motor recovery in stroke patients. This article reviews motor rehabilitation methods for stroke survivors with a focus on rehabilitation controlled by human motor intent. The review begins with the neurodevelopmental principles of motor rehabilitation that provide the neuroscientific basis for intuitively controlled rehabilitation, followed by a review of methods allowing human motor intent detection, biofeedback approaches, and quantitative motor rehabilitation assessment. Challenges for future advances in motor rehabilitation after stroke using intuitively controlled approaches are addressed.

A key issue in brain-computer interface (BCI) is the detection of intentional control (IC) states and non-intentional control (NC) states in an asynchronous manner. Further, for steady-state visual evoked potential (SSVEP) BCI systems, multiple states (sub-states) exist within the IC state. Existing recognition methods rely on a threshold technique, which is difficult to realize high accuracy, i.e. simultaneously high true positive rate and low false positive rate. To address this issue, we proposed a novel convolutional neural network (CNN) to detect IC and NC states in a SSVEP-BCI system for the first time. Specifically, the steady-state motion visual evoked potentials (SSMVEP) paradigm, which has been shown to induce less visual discomfort, was chosen as the experimental paradigm. Two processing pipelines were proposed for the detection of IC and NC states. The first one was using CNN as a multi-class classifier to discriminate between all the states in IC and NC state (FFT-CNN). The second one was using CNN to discriminate between IC and NC states, and using canonical correlation analysis (CCA) to perform classification tasks within the IC (FFT-CNN-CCA). We demonstrated that both pipelines achieved a significant increase in accuracy for low-performance healthy participants when traditional algorithms such as CCA threshold were used. Further, the FFT-CNN-CCA pipeline achieved better performance than the FFT-CNN pipeline based on the stroke patients’ data. In summary, we showed that CNN can be used for robust detection in an asynchronous SSMVEP-BCI with great potential for out-of-lab BCI applications.