This paper presents a cable-driven actuation module design that can be easily integrated into soft wearable robots to enhance their functional versatility. The actuation module is easily mountable to various wearable systems through a simple locking mechanism and customized electronics. Furthermore, the symmetrical design of the actuation module allows for multiple orientation options, significantly expanding its range of applications. Through the use of finite element analysis (FEA), we reduce the weight of the actuation module by selecting lightweight materials, without compromising its reliability and durability. The modular and lightweight characteristics of this actuator make it suited for integration into various wearable usages. We applied the modular actuator to the soft wearable lower-limb device and showed the effectiveness through metabolic cost and electromyography(EMG) signal measurements.

An increase in health awareness has fueled the development of fitness equipment or devices nowadays. Most conventional fitness devices have had some issues in space limitation and the high cost of equipment. With the advance in wearable robotics, we proposed a soft passive fitness wearable device for upper limb resistance exercises such as chest press, frontal raise, and chest fly. Users can customize the exercise intensity by adjusting the length of the elastic bands embedded in the wearable device. Moreover, the exer-tainment (Exercise-entertainment) user display interface was designed to motivate the user. Movements of users were estimated using inertial measurement units (IMUs), and haptic feedback was provided through the vibro-stimulation. Furthermore, the effectiveness of the proposed device was evaluated with the Borg scales representing the rating of perceived exertion (RPE) and measuring the surface electromyography (sEMG) of the three muscles located one on the shoulder and two on the chest. Both the Borg 6–20 and CR 10 scales were increased, and the normalized sEMG activities of the upper limb muscles with the activated device had more than double in magnitude compared to that with a bare condition; therefore, the proposed device has a potential effectiveness as for resistance exercise. Overall, this research devotes preliminary evidence on the benefits of the device in promoting the user to work out and contributing to the exercise effects.

This paper aims to investigate how adequate cognitive functions for recognizing, predicting, and generating a variety of actions can be developed through iterative learning of action-caused dynamic perceptual patterns. Particularly, we examined the capabilities of mental simulation of one's own actions as well as the inference of others' intention because they play a crucial role, especially in social cognition. We propose a dynamic neural network model based on predictive coding which can generate and recognize dynamic visuo-proprioceptive patterns. The proposed model was examined by conducting a set of robotic simulation experiments in which a robot was trained to imitate visually perceived gesture patterns of human subjects in a simulation environment. The experimental results showed that the proposed model was able to develop a predictive model of imitative interaction through iterative learning of large-scale spatio-temporal patterns in visuo-proprioceptive input streams. Also, the experiment verified that the model was able to generate mental imagery of dynamic visuo-proprioceptive patterns without feeding the external inputs. Furthermore, the model was able to recognize the intention of others by minimizing prediction error in the observations of the others' action patterns in an online manner. These findings suggest that the error minimization principle in predictive coding could provide a primal account for the mirror neuron functions for generating actions as well as recognizing those generated by others in a social cognitive context.

This paper introduces our approach to building a robot with communication capability based on the two key features: stochastic neural dynamics and prediction error minimization. A preliminary experiment showed that the humanoid robot was able to imitate other's action by means of those key features. In addition, we found that some sorts of communicative patterns emerged between two robots in which the robots inferred the intention of another agent behind the sensory observation.

One of Autism Spectrum Disorder (ASD)' major characteristics is the lack of social communication and interaction with other people. Recently, some research studies have examined robot-based interventions for children with ASD. In line with those studies, the present paper introduces our ongoing research on robot-mediated imitation training for children with ASD. In this paper, we focus on designing human-robot interaction for autism intervention whereas previous studies largely focused on developing robots for intervention. Several human-robot interaction scenarios for robot-mediated imitation training were developed with emphasis on facilitating communication between a child and a therapist. Two main concepts (triadic interaction and reciprocal imitation training) were adopted and modified to increase the efficacy of the intervention. The developed interaction scenarios described how a robot can be integrated into the clinical sessions and the requirements for a robot. The field trials for examining the efficacy of robot-mediated imitation training will be carried out.