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Real-time human intent recognition is important for controlling low-limb wearable robots. In this paper, to achieve continuous and precise recognition results on different terrains, we propose a real-time training and recognition method for six locomotion modes including standing, level ground walking, ramp ascending, ramp descending, stair ascending and stair descending. A locomotion recognition system is designed for the real-time recognition purpose with an embedded BPNN-based algorithm. A wearable powered orthosis integrated with this system and two inertial measurement units is used as the experimental setup to evaluate the performance of the designed method while providing hip assistance. Experiments including on-board training and real-time recognition parts are carried out on three able-bodied subjects. The overall recognition accuracies of six locomotion modes based on subject-dependent models are 98.43% and 98.03% respectively, with the wearable orthosis in two different assistance strategies. The cost time of recognition decision delivered to the orthosis is about 0.9[Formula: see text]ms. Experimental results show an effective and promising performance of the proposed method to realize real-time training and recognition for future control of low-limb wearable robots assisting users on different terrains.

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With the advent of the post-epidemic era, O2O e-commerce on community breaks through the original business model and forms a new online-to-offline integrated business model. This study is based on the value co-creation perspective and the TAM-TPB theory systematically builds a theoretical model of consumers’ continuous use intention of O2O e-commerce on the community. A structural equation model (SEM) was used to verify the research hypotheses. The research results were as follows: (1) the subjective norms and structural assurance in social factors have a positive impact on consumers’ continuous use intention; (2) the degree of convenience and safety in platform factors not only positively affect customer satisfaction but also positively affect customers’ continuous use intension, and customer satisfaction mediates between platform factors and customers’ continuous use intension; (3) customer–enterprise co-creation has a positive impact on customer perceived value, and customer perceived value plays a mediating role between customer–enterprise co-creation behavior and consumers’ willingness to continue using; (4) customer perceived value and customer satisfaction in the user factor have a significant impact on consumers’ continuous use intention.

In this paper, we present a powered bionic knee exoskeleton aiming at physical assistance and rehabilitation. The mechanical structure of the knee exoskeleton is based on anthropomorphic design, the tibia rolling or sliding on the femur during joint movements. In addition, a proxy-based sliding mode controller is used to improve the safety of the wearable device. Preliminary experimental results on trajectory tracking and dynamic response verify the feasibility of the proposed knee exoskeleton.978-1-4799-7800-7/15/$31.00 ©2015 IEEE

In this paper, we aim to investigate the effect of proprioceptive neuromuscular facilitation (PNF)-based rehabilitation for ankle plantar flexors spasticity by using a Robotic Ankle–foot Rehabilitation System (RARS). A modified robot-assisted system was proposed, and seven poststroke patients with hemiplegic spastic ankles participated in a 3-month robotic PNF training. Their impaired sides were used as the experimental group, while their unimpaired sides as the control group. A robotic intervention for the experimental group started from a 2-min passive stretching to warming-up or relaxing the soleus and gastrocnemius muscles and also ended with the same one. Then a PNF training session including 30 trials was activated between them. The rehabilitation trainings were carried out three times a week as an addition to their regular rehabilitation exercise. Passive range of motion, resistance torque, and stiffness were measured in both ankles before and after the interventions. The changes in Achilles tendon length, walking speed, and lower limb function were also evaluated by the same physician or physiotherapist for each participant. Biomechanical measurements before interventions showed significant difference between the experimental group and the control group due to ankle spasticity. For the control group, there was no significant difference in the 3 months with no robotic intervention. But for the experimental group, passive dorsiflexion range of motion increased (p < 0.01), resistance torque under different dorsiflexion angle levels (0°, 10°, and 20°) decreased (p < 0.05, p < 0.001, and p < 0.001, respectively), and quasi-static stiffness under different dorsiflexion angle levels (0°, 10°, and 20°) also decreased (p < 0.01, p < 0.001, and p < 0.001, respectively). Achilles’s tendon length shortened (p < 0.01), while its thickness showed no significant change (p > 0.05). The robotic rehabilitation also improved the muscle strength (p < 0.01) and muscle control performance (p < 0.001). In addition, improvements were observed in clinical and functional measurements, such as Timed Up-and-Go (p < 0.05), normal walking speed (p > 0.05), and fast walking speed (p < 0.05). These results indicated that the PNF-based robotic intervention could significantly alleviate lower limb spasticity and improve the motor function in chronic stroke participant. The robotic system could potentially be used as an effective tool in poststroke rehabilitation training.

It has been proposed that learning from movement errors involves a credit assignment problem: did I misestimate properties of the object or those of my body? For example, an overestimate of arm strength and an underestimate of the weight of a coffee cup can both lead to coffee spills. Though previous studies have found signs of simultaneous learning of the object and of the body during object manipulation, there is little behavioral evidence about their quantitative relation. Here we employed a novel weight-transportation task, in which participants lift the first cup filled with liquid while assessing their learning from errors. Specifically, we examined their transfer of learning when switching to a contralateral hand, the second identical cup, or switching both hands and cups. By comparing these transfer behaviors, we found that 25% of the learning was attributed to the object (simply because of the use of the same cup) and 58% of the learning was attributed to the body (simply because of the use of the same hand). The nervous system thus seems to partition the learning of object manipulation between the object and the body.