Self-efficacy is crucial for the effective application of assistive technology and rehabilitation. This study proposes a novel approach to assess the impact of motor interventions on motor self-efficacy, relevant for human-robot interaction in rehabilitation, by focusing on the perceived reachable space. Twelve healthy adults underwent an arm movement restriction intervention using a robotic arm (KINARM), and changes in the perceived reachable space and muscle activity were measured before and after the intervention. The results indicated a reduction in the perceived reachable space and an adaptive decrease in muscle activity for unreachable targets following motor restriction. This suggests that the perceived reachable space can serve as an objective proxy for task-specific motor self-efficacy, which is valuable for evaluating user adaptation to robotic interfaces. Furthermore, these findings imply that in rehabilitation using interactive robots, a patient’s effort levels may be influenced by their perception of task achievability.

Deep vein thrombosis (DVT) is a disease in which blood clots form in the deep veins of the lower limbs. It is a common condition particularly for wheelchair and bedridden patients due to prolonged sitting and supine positions. Calf compression and ankle exercises can prevent DVT, and current studies aim to combine them. However, existing devices suffer from low user comfort because of their exoskeletal structure, which consists of a rigid frame. In addition, the devices have not been tested in various positions, such as sitting and supine positions. This study introduces a flexible DVT prevention device that combines intermittent pneumatic compression (IPC) with assisted ankle exercises, which can be used in high-risk positions. The developed prototype has an endoskeleton-type structure with soft actuators, enabling natural ankle movements while remaining lightweight. Basic characteristic experiments with the prototype demonstrated that a maximum joint range of motion of approximately 30° could be achieved with a force exceeding 50 N when combining IPC and ankle exercises in both the sitting and supine positions. Blood flow evaluation experiments further showed that the combined therapy of IPC and ankle exercises, regardless of the position, was more likely to prevent DVT than no motion or single-motion modes effectively.

Understanding fine-grained urban population distribution based on GPS location data is important for urban applications such as traffic management and new store openings for retailers. However, GPS-based population distribution relies heavily on the number of users who agree to provide GPS logs. With only a limited number of users, the fine-grained population distribution becomes sparse and must be aggregated as coarse-grained. In this paper, we present the challenge of developing a model to estimate fine-grained population distributions from coarse-grained population distributions and propose a model capable of incorporating extensive auxiliary information using a CNN-based image super-resolution approach. Our experiments with real data reveal two key findings: (i) traditional regression models tend to estimate similar populations for adjacent grids, which is often overlooked by existing metrics, and (ii) CNN-based image super-resolution models reproduce population distribution features of adjacent grids having different population volumes, although they sometimes provide simplistic estimates depending on auxiliary information. Based on these findings, we present our vision for developing a promising model and improving the evaluation metrics tailored to this challenge.

Previous studies have shown that the personality composition of a group significantly affects learners’ satisfaction during collaborative learning. However, while these studies investigated a group as a whole by focusing on group statistics, such as the mean and standard deviation of the members’ personalities, they paid little attention to the personality differences of individual pairs within the group, albeit the group contains many pairwise interactions. In this paper, we studied whether and how pairwise personality differences between a learner and groupmates affect the learner’s satisfaction. Examining data collected from an employee training program during which learners had reflective group discussions, we confirmed that pairwise personality differences significantly affect a learner’s level of satisfaction in the program. Specifically, satisfaction is affected by (1) the average of the personality differences between the learner and each individual groupmate, which reflects the degree to which the learner is different from the groupmates on average, and (2) the personality difference from the groupmate who has the most different/similar personality from/to the learner.

The interpersonal compatibility of a company agent and a customer significantly affects the outcome of their communication. In existing research, however, compatibility has been studied only in terms of the similarity in personality and values. That is, agents and customers were compared only on the same dimensions that make up their personality and values, e.g., on the same trait of the Big Five (compared agent's Extraversion and customer's Extraversion) or same values as per Schwartz's Basic Values (agent's Conformity and customer's Conformity). In this paper, we studied compatibility from a broader perspective, i.e., in addition to the similarity, we investigated interactions across different dimensions (e.g., an agent's Extraversion and a customer's Conformity, or the former's Extraversion and the latter's Neuroticism). Examining 7,594 real call logs collected from telemarketing call centers, we have confirmed that such different dimensional interactions significantly affect a customer making a purchase (i.e., a customer's conversion) or not. A simulation where we matched agents and customers demonstrated that our compatibility model that incorporated the interactions across different dimensions yielded significant conversion lift, i.e., +46% on the average, compared from one that used only similarity in personality and values.

We have developed a high-precision method that selects an appropriate model of a video image in order to track an unknown face in front of a large display. Currently, Active Appearance Models (AAMs) are used to track non-rigid objects, such as a faces, because the models efficiently learn the correlation between shape and texture. The problem with an AAM is that when it tracks an unknown face, excessive training data increases tracking errors because there is an intermediate model size beyond which the reduction in fitting performance outweighs the gains from any improved representational power of the model. To increases the accuracy with which an unknown face is tracked, we built clustered models from training datasets and select a cluster that includes a face which is similar to the unknown face. Our method of clustering and cluster selecting is based on the Mutual Subspace Method (MSM). We demonstrated the effectiveness of our method by using the leave-one-out cross-validation.