Sit-to-stand and stand-to-sit motions are important in daily activities. However, elderly individuals often find these motions difficult to perform with declining lower limb strength, which causes a considerable reduction to their quality of life. In this study, a sensing method for controlling robotic assistive devices was proposed. This method utilizes electromyographic measurements and a deep neural network to predict motion initiation, and it estimates the timing of triggering assistive devices. Experimental results indicate that four muscle synergy patterns are required to represent the sit-to-stand and stand-to-sit motions together, with two of them being shared between both movements. Subsequently, a long short-term memory network was designed to forecast these two motions, and the result indicates that the prediction accuracy reached 92.95% ± 0.83% with forecasting time of 300 ms.

Recently, a worker’s subjective satisfaction, in other words Quality-of-Working Life (QWL), has attracted more attention than productivity or efficiency. To provide QWL-oriented working support in a factory manufacturing environment, this study proposes a framework for recognizing manual assembly behaviors that may reflect a worker’s inner state or physical condition. First, a new set of interactions is defined to describe the behavioral fluctuations and diversity that appear even in the same assembly task. We expand the conventional interaction definitions for manufacturing analysis in three ways: 1) we add primitive interactions that qualify the fundamental interactions, 2) we install a spatial attribute into the interaction definition, and 3) we allow the simultaneous occurrence of multiple interactions. Additionally, an image-based automatic recognition technique is designed to detect the newly defined interactions. Through experimental evaluations for a compressor attachment task, we found various differences in manual assembly behaviors and confirmed that they can be distinguished using the recognized interactions.

A smile is a representative expression of happiness or high quality-of-life; however, automatic recognition of a smile according to happiness remains a challenging task. Because expressions of happiness are strongly dependent upon physical condition and occurrence of other emotions, and similar facial expression often occur under different emotions, we consider that there is no absolute visual pattern of a smile corresponding to happiness. Therefore, in this study, we assumed that a “smile with happiness” is observed as the temporal ascent in the degree of smiling and attempted to recognize this by capturing changes in facial expression within temporally sequential images. As an implementation of this scheme, we proposed a Siamese-structure deep neural network to compare facial expressions in two input images and estimate the existence of smile ascension or descension. For primal analysis of the proposed network, we developed a unique smiling dataset containing image pairs with various changes in smiling degree, including slight changes. The results demonstrated that the proposed method achieved nearly perfect recognition with > 0.95 accuracy when recognizing changes in the degree of smiling that humans certainly recognize. Attention regions that contributed to the predicted labels were concentrated on the mouth, cheeks, and tail of the eyes, which indicates a reasonable function for recognizing changes in smiling degree was constructed by the proposed method.

This paper introduces a novel form of communication via a combination of muscle sensing by electromyography and stimulation via a skin-stretcher device as a motion monitoring system. After sensing muscle activity through electromyography, the skin-stretcher device provides a skin sensation that confidentially informs or induces movements of the user who wears the device. This paper also introduces methods for translating muscle activities to the skin-stretch sensations, and additional filtering to improve the performance. In this study, we conducted preliminary experiments that demonstrate the potential of our system design.

This paper introduces a novel concept of “Memory Ubiquitous” which provides smart memory functions for appliances, facilities, and equipments used in everyday life. At the first step to realize it, we first propose three fundamental functions. These are capturing and recording the surrounding scene including human behaviors, annotating and editing the records to construct smart memories, and presenting the integrated memories. These functions can support human activities by aiding recall, giving instructions, and providing asynchronous communications. We chose a kitchen as an actual test bed and built a prototype cooking support system with the above smart memory functions. We implemented a context sensitive record presentation based on state recognition and simultaneous display of multiple records that is adaptive to users' intentions. By evaluating the system's performance, we obtained several results that show how smart memory functions can assist human activities and how they can be extended for more useful services.

We propose a novel wearable imaging system that can capture omnidirectional movies from the viewpoint of the camera wearer. The imaging system solves the problems of resolution uniformity and gaze matching that conventional approaches do not address. We combine cameras with curved mirrors that control the projection of the imaging system to produce uniform resolution. Use of the mirrors also enables the viewpoint to be moved closer to the eyes of the camera wearer, thus reducing gaze mismatching. The optics, including the curved mirror, have been designed to form an objective projection. The capability of the designed optics is evaluated with respect to resolution, aberration, and gaze matching. We have developed a prototype based on the designed optics for practical use. Capability of the prototype and effectiveness of first-person perspective omnidirectional movies were demonstrated through quantitative evaluations and presentation experiments to ordinary people, respectively.

The HBP (horizontal fixed viewpoint biconical paraboloidal) mirror is an anisotropic convex mirror that has a property of inhomogeneous angular resolution about azimuth angle. In this paper, we investigate the effectiveness of the HBP mirror system for remote surveillance. We developed a real remote surveillance system that is constructed by the HBP mirror system mounted on an electric cart. Through the surveillance experiments, surveyors usually looked almost front views, and they paid attention to interesting objects only when the cart approaches them. Since the HBP mirror system has high resolution in frontal view, it seems to work well in the surveillance. We also constructed a simulational remote surveillance environment in order to quantitatively compare the HBP mirror system with a conventional omnidirectional mirror system under fair experimental conditions. As a practical task, we assumed object searching in a virtually constructed devastated area. We confirmed that objects can be detected earlier and with certainty by the HBP mirror system

A real-time omnidirectional imaging system that can acquire an omnidirectional field of view at video rate using a convex mirror was applied to a variety of conditions. The imaging system consists of an isotropic convex mirror and a camera pointing vertically toward the mirror with its optical axis aligned with the mirror's optical axis. Because of these optics, angular resolution is independent of the azimuth angle. However, it is important for a mobile robot to find and avoid obstacles in its path. We consider that angular resolution in the direction of the robot's moving needs higher resolution than that of its lateral view. In this paper, we propose a non-isotropic omnidirectional imaging system for navigating a mobile robot.