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Surface electromyography signal plays an important role in hand function recovery training. In this paper, an IoT-enabled stroke rehabilitation system was introduced which was based on a smart wearable armband (SWA), machine learning (ML) algorithms, and a 3-D printed dexterous robot hand. User comfort is one of the key issues which should be addressed for wearable devices. The SWA was developed by integrating a low-power and tiny-sized IoT sensing device with textile electrodes, which can measure, pre-process, and wirelessly transmit bio-potential signals. By evenly distributing surface electrodes over user’s forearm, drawbacks of classification accuracy poor performance can be mitigated. A new method was put forward to find the optimal feature set. ML algorithms were leveraged to analyze and discriminate features of different hand movements, and their performances were appraised by classification complexity estimating algorithms and principal components analysis. According to the verification results, all nine gestures can be successfully identified with an average accuracy up to 96.20%. In addition, a 3-D printed five-finger robot hand was implemented for hand rehabilitation training purpose. Correspondingly, user’s hand movement intentions were extracted and converted into a series of commands which were used to drive motors assembled inside the dexterous robot hand. As a result, the dexterous robot hand can mimic the user’s gesture in a real-time manner, which shows the proposed system can be used as a training tool to facilitate rehabilitation process for the patients after stroke.

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A pneumatically powered, reconfigurable omnidirectional soft robot based on caterpillar locomotion is described. The robot is composed of nine modules arranged as a three by three matrix and the length of this matrix is 154 mm. The robot propagates a traveling wave inspired by caterpillar locomotion, and it has all three degrees of freedom on a plane (X, Y, and rotation). The speed of the robot is about 18.5 m/h (two body lengths per minute) and it can rotate at a speed of 1.63°/s. The modules have neodymium-iron-boron (NdFeB) magnets embedded and can be easily replaced or combined into other configurations. Two different configurations are presented to demonstrate the possibilities of the modular structure: (1) by removing some modules, the omnidirectional robot can be reassembled into a form that can crawl in a pipe and (2) two omnidirectional robots can crawl close to each other and be assembled automatically into a bigger omnidirectional robot. Omnidirectional motion is important for soft robots to explore unstructured environments. The modular structure gives the soft robot the ability to cope with the challenges of different environments and tasks.

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