Nerve injury can cause lower limb paralysis and gait disorder. Currently lower limb rehabilitation exoskeleton robots used in the hospitals need more power to correct abnormal motor patterns of stroke patients’ legs. These gait rehabilitation robots are powered by cumbersome and bulky electric motors, which provides a poor user experience. A newly developed gait rehabilitation exoskeleton robot actuated by low-cost and lightweight pneumatic artificial muscles (PAMs) is presented in this research. A model-free proxy-based sliding mode control (PSMC) strategy and a model-based chattering mitigation robust variable control (CRVC) strategy were developed and first applied in rehabilitation trainings, respectively. As the dynamic response of PAM due to the compressed air is low, an innovative intention identification control strategy was taken in active trainings by the use of the subject’s intention indirectly through the estimation of the interaction force between the subject’s leg and the exoskeleton. The proposed intention identification strategy was verified by treadmill-based gait training experiments.

To regain mobility, stroke patients need to receive repetitive and intensive therapy. Robot-assisted rehabilitation is an active area of research. Cheap robotic leg rehabilitation devices should be developed to meet the demands and assist most patients. A low cost hip-knee exoskeleton prototype powered by pneumatic muscles was developed. On this basis, Functional Electrical Stimulation (FES) induced paralyzed muscles to realize ankle joint rehabilitation training. These three ankle muscles: the tibialis anterior, the soleus, and the gastrocnemius under electrical stimulation cooperated together to realize optimally coordinated control of dorsiflexion and plantar-flexion movement. As both of pneumatic muscle and FES induced muscle possess highly nonlinear characteristics, a sliding control algorithm called Chattering mitigation Robust Variable Control (CRVC) was applied to leg hybrid rehabilitation. The combination of exoskeleton and FES is a promising way to reduce the cost and the complexity of designing hip-knee-ankle exoskeleton. The proposed hybrid method was verified by treadmill-based gait training experiments.

A wearable, portable, low-cost, and easy-to-use upper extremity exoskeleton robot, RUPERT, is presented here for clinical and in-home therapies of patients who have survived a stroke. The robot system has five degrees-of-freedom and is driven by compliant and safe pneumatic muscles. Its primary function is to assist the movement of an affected arm in 3-D space and perform a daily training program in a virtual environment. Subjects are recruited for experiments of both clinical and in-home therapies. While using RUPERT, most subjects exhibited significant improvements in various functional measures. During their in-home therapy trials, subjects expressed enthusiasm regarding the modules and the use of our robotic system. The experimental results look promising and the proposed robotic system exhibits a good prospect for a future commercialized in-home therapy product.

One of the most significant challenges for many online communities is increasing members' contributions over time. Prior studies on peer feedback in online communities have suggested its impact on contribution, but have been limited by their correlational nature. In this paper, we conducted a field experiment on Wikipedia to test the effects of different feedback types (positive feedback, negative feedback, directive feedback, and social feedback) on members' contribution. Our results characterize the effects of different feedback types, and suggest trade-offs in the effects of feedback between the focal task and general motivation, as well as differences in how newcomers and experienced editors respond to peer feedback. This research provides insights into the mechanisms underlying peer feedback in online communities and practical guidance to design more effective peer feedback systems.

Recent investigation of cortical coding and computation indicates that temporal coding is probably a more biologically plausible scheme used by neurons than the rate coding used commonly in most published work. We propose and demonstrate in this letter that spiking neural networks (SNN), consisting of spiking neurons that propagate information by the timing of spikes, are a better alternative to the coding scheme based on spike frequency (histogram) alone. The SNN model analyzes cortical neural spike trains directly without losing temporal information for generating more reliable motor command for cortically controlled prosthetics. In this letter, we compared the temporal pattern classification result from the SNN approach with results generated from firing-rate-based approaches: conventional artificial neural networks, support vector machines, and linear regression. The results show that the SNN algorithm can achieve higher classification accuracy and identify the spiking activity related to movement control earlier than the other methods. Both are desirable characteristics for fast neural information processing and reliable control command pattern recognition for neuroprosthetic applications.

Recent progress in neural interface technologies and demonstration of direct cortical control of robotic arm or computer cursor for simple movement has generated high expectation of rapid development of cortically controlled neuroprosthetics to improve motor function in subjects with severe neurological deficits. However, several challenging engineering and biological issues remain to be resolved before a practical system can be developed for patients to receive real benefit. The ability of neural systems to adapt to changes and learn new functions should be taken into consideration in the design and development of neuroprosthetics so that the two systems can cooperatively work together to accommodate continued changes (neural degeneration or functional improvement) in a human user. An intelligent neural interface should be able to activate residual function and facilitate adaptation and learning ability of the neural system. Current efforts in developing wireless and networked neural interfaces and utilizing smart materials and sensors will revolutionize the future design of neuroprosthetics and advance the investigation of brain function. This report reviews two recent projects in activating residual neural functions and investigating brain control of lower limb functions.

This paper outlines our work in developing a series of game-like VR scenarios to assess and rehabilitate System with 3-D Exercise Machine for Upper Limb, coordination, range of motion and other relevant perceptual motor activities. The novel experiments with a new perturbation paradigm were designed to study the control of hand orientation and arm movement during 3D reach-to-grasp in both human subjects and rhesus monkeys. The experiments captured the signal from the monkey's brain (electrocorticogram, EcoG), and integrated the signals into a DATABASE. Our efforts have focused on building engaging game-based stereoscopic video and audio scenarios that allow clients to participate in perceptual-motor rehabilitation. From the audio and video, the system of DEMO could activate the patient's consciousness to adjust the arm's movement; this depends on the patient self-adjust and self- rehabilitation. Exploratory work using EcoG signal DATABASE to match the motion and the biofeedback model, detect and track 3D body motion, identify body postures and quantify motor performance is also described, this method of biofeedback could extract the subjective desire and promote the effect of the rehabilitative training.

This paper presents a novel real-time, multi-modal biofeedback system for stroke patient therapy. The problem is important as traditional mechanisms of rehabilitation are monotonous, and do not incorporate detailed quantitative assessment of recovery in addition to traditional clinical schemes. We have been working on developing an experiential media system that integrates task dependent physical therapy and cognitive stimuli within an interactive, multimodal environment. The environment provides a purposeful, engaging, visual and auditory scene in which patients can practice functional therapeutic reaching tasks, while receiving different types of simultaneous feedback indicating measures of both performance and results. There are three contributions of this paper - (a) identification of features and goals for the functional task (b) The development of sophisticated feedback (auditory and visual) mechanisms that match the semantics of action of the task. We additionally develop novel action-feedback coupling mechanisms. (c) New metrics to validate the ability of the system to promote learnability, stylization and engagement. We have validated the system for nine subjects with excellent results.

This paper presents a novel real-time, multi-modal biofeedback system for stoke patient therapy. The problem is important as traditional mechanisms of rehabilitation are monotonous, and do not incorporate detailed quantitative assessment of recovery in addition to traditional clinical schemes. We have been working on developing an experiential media system that integrates task dependent physical therapy and cognitive stimuli within an interactive, multimodal environment. The environment provides a purposeful, engaging, visual and auditory scene in which patients can practice functional therapeutic reaching tasks, while receiving different types of simultaneous feedback indicating measures of both performance and results. There are two contributions of this paper - (a) identification of features and goals for the functional task, (b) the development of sophisticated feedback (auditory and visual) mechanisms that match the semantics of action of the task.

A procedure is developed to probe the changes in the functional interactions among neurons in primary motor cortex of the monkey brain during adaptation. A monkey is trained to learn a new skill, moving its arm to reach a target under the influence of external perturbations. The spike trains of multiple neurons in the primary motor cortex are recorded simultaneously. We utilize the methodology of directed transfer function, derived from a class of linear stochastic models, to quantify the causal interactions between the neurons. We find that the coupling between the motor neurons tends to increase during the adaptation but return to the original level after the adaptation. Furthermore, there is evidence that adaptation tends to affect the topology of the neural network, despite the approximate conservation of the average coupling strength in the network before and after the adaptation.