A proposed adaptive soft orthotic device performs motion sensing and production of assistive forces with a modular, pneumatically-driven, hyper-elastic composite. Wrapping the material around a joint will allow simultaneous motion sensing and active force response through shape and rigidity control. This monolithic elastomer sheet contains a series of miniaturized pneumatically-powered McKibben-type actuators that exert tension and enable adaptive rigidity control. The elastomer is embedded with conductive liquid channels that detect strain and bending deformations induced by the pneumatic actua-tors. In addition, the proposed system is modular and can be configured for a diverse range of motor tasks, joints, and human subjects. This modular functionality is accomplished with a decentralized network of self-configuring nodes that manage the collection of sensory data and the delivery of actuator feedback commands. This paper mainly describes the design of the soft orthotic device as well as actuator and sensor components. The characterization of the individual sensors, actuators, and the integrated device is also presented.

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Many robotic devices have been developed for stroke patients to recover their upper limb motor function. Among them, master-slave type rehabilitation systems provide surveillance of the therapist to the patient who is performing home-rehabilitation. In this study, we proposed a wearable and light exoskeleton device for upper limb rehabilitation and designed a master-slave rehabili-tation system using the exoskeleton device as slave device and a haptic device (Phantom Premium) as master device. To convey therapist’s experience to patients using this system, the slave device is driven to track the motion of the master device manipulated by the therapist. In order to improve the tracking efficacy of traditional PI control, a self-tuning fuzzy PI control was proposed. Results of simulation indicated the proposed control method is more effective than the traditional PI control, particularly in tracking accuracy and response speed.

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This paper was aimed at the continuous recognition of the upper limb multi-motion during the upper limb movement for rehabilitation training. The amplitude of the surface electro-myographic ( sEMG ) signals change during movement of the upper limb and the features of sEMG signals are different with the changes. These variances in the features represent the different statuses of the upper limb. Recognizing the variances will lead to recognition of the upper limb motion. In this study, sEMG signals were recorded through five non-invasive electrodes attached on the anatomy points of the upper limb and an autoregressive model was used to extract the features of the detected sEMG signals. After that the Back-propagation Neural Networks was applied to recognize the patterns of the upper arm motion using the variant features as the training and input data. Three volunteers participated in the real-time experiment and the results stated that this method is effective for a real-time continuous recognition of the upper limb multi-motions.

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In this paper, we proposed a control method to implement the upper-limb active training which is performed with the proposed exoskeleton device. It provides a wide approach for Human Machine Interface (HMI) in which the device is of high inertia, high friction and non-backdrivabi-lity and it is difficult to obtain the contact force between human and the device directly. The main idea of this method is to measure the motion of human body rather than the motion of device. This method is more suitable to the HMI in which the contact between human and device can be assumed as a spring-damper model. According to two kinds of experiments designed, different contact resistance was exerted to the forearm of the user. The sEMG signals detected from biceps brachii and triceps brachii were processed and the two kinds of resistance exerted to humanforearm were confirmed.

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This paper presents a new design of exoskeleton for human locomotion system, based on a cam mechanism. Cam profile is determined by the main joints motion laws of the human locomotion system. Constructive solution adopted is validated by virtual simulation software package made by Adams software. The results obtained through virtual simulations consist in mass centres, displacements, velocities and accelerations of the exoskeleton kinematic elements.

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