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.

We previously proposed cable-driven wearable devices for exercise and gait assistance. It was a lightweight and suit-type device with programmable resistance or assistance adjustment capabilities. In this paper, we introduce 1) a wearable gym device designed to focus on lower limb muscles and 2) a stationary gym device for comprehensive strength (weight) training. The actuation module can be used interchangeably for both devices. This actuation module includes a control board and a cable-driven actuator with a smaller size, improved strength, and greater speed compared to previous version. Its compact size makes easy implementation into our proposed devices. To evaluate the effectiveness of these devices, we conducted surface electromyography (sEMG) experiments during exercises comparing the effects of the developed devices with traditional dumbbells to confirm their efficacy.

Hip assistance with cable-driven devices has been proven to help decrease the metabolic cost of gait. However, most existing devices use heavy actuating modules or provide assistance in only one direction, limiting the effectiveness. Cable-driven devices are also difficult to accurately estimate the hip position using only motor encoders, therefore utilizing various auxiliary sensors. This paper introduces a 1.5 kg cable-driven soft wearable hip assist device that can provide assistance in both flexion and extension, using a velocity-dependent delayed output feedback controller (v-DOFC). The device is designed with the consideration of ergonomics and pressure distribution of wearable parts, to increase the anchoring performance and comfort. The controller uses time-delayed feedback proportional to the velocity output state, allowing control without requiring accurate position estimation. Additionally, directional weighting is used to provide different assistance forces for extension and flexion to match different optimal assistance values. Experimental results show that the device can reduce metabolic cost by 13.8 % compared to walking without the device. The soft wearable hip assist device can be applied to help the elderly with weaker muscles to walk longer distances.

To provide an alternative to conventional large-scale fitness equipment, we previously developed a soft passive wearable device for upper limb resistance exercises that utilized elastic exercise bands. However, the user was required to manually adjust the level of strength. In this paper, we introduce a novel wearable fitness device for upper limb exercise that constitutes cable-driven actuation to control the resistance profiles. Our proposed device allows for the generation of isotonic force trajectories, similar to those produced during dumbbell lifting, utilizing custom cable-driven actuators. The cable path of the device was determined based on the results of user testing aimed at optimizing muscle stimulation levels. In addition, linear resonant actuators were installed at customized haptic handles and upper arm modules to enhance proprioceptive sensitivity and exercise efficacy. The immersive “exer-tainment” user display interface was also redesigned to increase user motivation. The efficacy of our device was assessed by comparing the surface electromyography (sEMG) activities of upper limb muscles during chest press and triceps extension exercises performed with our device versus those using traditional weight training machines and rubber bands. It was found that our device could effectively strengthen during arm exercise, such as triceps extension.

This paper proposes an end-to-end speech translation system that utilizes contextual information. Contextual information helps clarify the meaning of the utterances. However, conventional end-to-end speech translation (E2E-ST) is primarily designed to handle single-utterance. Thus, we introduce a context encoder that extracts contextual information from previous translation results. Here, the context encoder obtains high-quality contextual information by adopting the BERT model. Then, we combine it with speech information extracted from speech signals to generate translation results. On the widely used TED-based speech translation corpus, we show that the results of the contextual E2E-ST model are significantly better than those of the single utterance-based E2E-ST model. Furthermore, we demonstrate that contextual information contributes to the processing of unclearly spoken utterances as well as ambiguity caused by pronouns and homophones.

The work presented is about the postprocessor, which improves the performance of an automatic speech segmentation system by correcting the phoneme boundary errors. The proposed postprocessor reduces the range of errors in the auto labeled results that are ready to be used directly as synthesis unit. Starting from a baseline automatic segmentation system, our proposed postprocessor trains the features of hand labeled results using a multi-layer perceptron (MLP) algorithm. Then, the auto labeled result combined with the MLP postprocessor determines a new phoneme boundary. For phonetically rich sentences, we have achieved 19.9% improvement for the frame accuracy, comparing with the performance of a conventional automatic labeling system. Also, we have reduced the absolute error about 28.6%.