YAMAZOE HIROTAKE

This paper proposes a method of integrating known relations about the target task into a neural network as predefined computational modules. During the training process, the network is automatically induced to estimate the hidden parameters required by the embedded computational modules. In experiments, the proposed method was applied to 2D eyeball center position estimation. Experimental results confirmed that the accuracy of 2D eyeball center position estimation is improved and that the system acquires the ability to estimate 3D eye positions, which are hidden parameters.

Abstract Gait analysis has been studied for a long time and applied to fields such as security, sport, and medicine. In particular, clinical gait analysis has played a significant role in improving the quality of healthcare. With the growth of machine learning technology in recent years, deep learning-based approaches to gait analysis have become popular. However, a large number of samples are required for training models when using deep learning, where the amount of available gait-related data may be limited for several reasons. This paper discusses certain techniques that can be applied to enable the use of deep learning for gait analysis in case of limited availability of data. Recent studies on the clinical applications of deep learning for gait analysis are also reviewed, and the compatibility between these applications and sensing modalities is determined. This article also provides a broad overview of publicly available gait databases for different sensing modalities.

Abstract As the geriatric population expands, caregivers require more accurate training to handle and care for the elderly. However, students lack methods for acquiring the necessary skill and experience, as well as sufficient opportunities to practice on real human beings. To investigate the necessity and feasibility of care training assistant robots in care education, we developed a simulated robot as a shoulder complex joint with multi-DOF. In this study, five experts with years of experience in elderly care participated in the data-acquisition process, to acquire information on aspects such as the glenohumeral angle, as well as the sterno-clavicular joint and its torque. The experts performed three types of range-of-motion exercises: (i) elevation–depression of sterno-clavicular joint; (ii) extension and flexion of glenohumeral joint; (iii) lateral and medial rotation of glenohumeral joint. The experimental results showed that the quantitative results for all the exercises were significantly different between the experts. Moreover, we observed that even experienced professionals need consistent care education based on quantitative data and feedback. Thus, we confirmed the necessity and feasibility of the care training assistant robot for improving the skills required for elderly care.

In this paper, we propose a wearable robot arm with consideration of weight and usability. Based on the features of existing wearable robot arms, we focused on the issues of weight and usability. The behavior of human hands during physical work can be divided into two phases. In the first, the shoulder and the elbow joints move before commencing the task by using the hands. In the second, the wrist joints move during the actual work. We found that these features can be applied to wearable robot arms. Consequently, we proposed hybrid actuation system (HAS) with a combination of two types of joints. In this study, HAS is implemented into the prototype wearable robot arm, assist oriented arm (AOA). To verify the validity of the proposed system, we implemented three types of robot arms (PasAct, Act, 6DOF) using simulation to compare the weight, working efficiency, and usability. Furthermore, we compared these simulation models with AOA for evaluation.

<p>We analyzed changes in human gait (way of walking) that corresponded to changes in human gaze direction. For the purpose, we constructed an immersive walking environment in which we measured participant gait in various controlled gazing situations via a motion capture system and an eye tracker. The environment consisted of a treadmill and 180-degree multi-screen for presenting the gazing target. As preliminary analysis of gaze-gait relations, in this paper, we focused on arm and leg swing amplitudes as a measure of gait and analyzed the relationship between gaze and arm/leg swings. Unlike previous studies, we sought to analyze behavior that occurred when humans intentionally gazed at a specific target. Our experimental results indicate that arm swing is affected by gaze direction. We observed a tendency for decreased swing amplitude in the arm further from the gazing direction, and for increased swing amplitude in the arm closer to the gaze direction. Contrary to our results for arm swing, we did not observe any evidence of modulated leg motion by gaze direction. Our results suggest that it may be possible to estimate gaze from human gait.</p>

Existing background subtraction methods often fail to extract a foreground region whose color is similar to that of the background. When we use a co-located camera and range sensor, by which we can obtain both a color image and depth map simultaneously, it is expected to get a better foreground region by integrating the two kind of images. However, it is not straightforward when a moving object is observed because the camera and range sensor do not capture the scene synchronously. In this paper, we propose a novel method that pseudo-synchronize the camera and range sensor and integrate the background subtraction of the color and depth images to realize a good foreground extraction. Experimental results of a walking human show its effectiveness.

This paper proposes a video communication assist system using a companion robot in coordination with the user's conversational attitude toward the communication. In order to maintain a conversation and to achieve comfortable communication, it is necessary to provide the user's attitude-aware assistance. First, the system estimates the user's conversational state by a machine learning method. Next, the robot appropriately expresses its active listening behaviors, such as nodding and gaze turns, to compensate for the listener's attitude when she/he is not really listening to another user's speech, the robot shows communication-evoking behaviors (topic provision) to compensate for the lack of a topic, and the system switches the camera images to create an illusion of eye-contact, corresponding to the current context of the user's attitude. From empirical studies and a demonstration experiment, i) both the robot's active listening behaviors and the switching of the camera image compensate for the other person's attitude, ii) elderly people prefer long intervals between the robot's behaviors, and iii) the topic provision function is effective for awkward silences.

We have designed and fabricated a tactile array sensors with three inclined micro-cantilevers embedded in elastomer, which can detect both normal and shear stresses. In this paper, we confirmed gripping status classification using sensor output. Using our sensor, four gripping status (free, grasping, holding and slipping) could be classified significantly.