Objective: The purpose of our study is to develop a spoken dialogue system (SDS) for pain questionnaire in patients with spinal disease. We evaluate user satisfaction and validated the performance accuracy of the SDS in medical staff and patients.Methods: The SDS was developed to investigate pain and related psychological issues in patients with spinal diseases based on the pain questionnaire protocol. We recognized patients’ various answers, summarized important information, and documented them. User satisfaction and performance accuracy were evaluated in 30 potential users of SDS, including doctors, nurses, and patients and statistically analyzed.Results: The overall satisfaction score of 30 patients was 5.5 ± 1.4 out of 7 points. Satisfaction scores were 5.3 ± 0.8 for doctors, 6.0 ± 0.6 for nurses, and 5.3 ± 0.5 for patients. In terms of performance accuracy, the number of repetitions of the same question was 13, 16, and 33 (13.5%, 16.8%, and 34.7%) for doctors, nurses, and patients, respectively. The number of errors in the summarized comment by the SDS was 5, 0, and 11 (5.2%, 0.0%, and 11.6 %), respectively. The number of summarization omissions was 7, 5, and 7 (7.3%, 5.3%, and 7.4%), respectively.Conclusion: This is the first study in which voice-based conversational artificial intelligence (AI) was developed for a spinal pain questionnaire and validated by medical staff and patients. The conversational AI showed favorable results in terms of user satisfaction and performance accuracy. Conversational AI can be useful for the diagnosis and remote monitoring of various patients as well as for pain questionnaires in the future.

Robot backchanneling is implemented using verbal expressions, such as reiterating after remembering the response of the user or chiming in with the user. We performed experiments to investigate how the backchannel behavior of the robot affects the user's perception of the robot's empathy and attentiveness toward their reported pain and sleep concerns. The results indicate that users prefer the robot that provides backchannel and perceive it as more intelligent. Where the robot backchanneled, the number of conversational turns between the user and the robot decreased compared to situations where the robot did not backchannel. The refusal rate of the robot repeating the same question to the user decreased. When a robot is conducting a medical questionnaire and offers backchannels to the user, the overall efficiency of the conversation improves. Users also perceive the robot as understanding and empathetic towards their pain and discomfort, leading to a positive evaluation of the interaction.

This study investigated the electro-optic properties of organic light-emitting diode (OLED) devices with the addition of an interlayer. A device with the perfluorododecyl-1H,1H,2H,2H-triethoxysilane–perfluorotetradecyl 1H,1H,2H,2H-triethoxysilane mixture (hybrid solution) membrane applied to the hole injection layer (HIL) using a self-assembled monolayer (SAM), along with N,N’-Bis (naphtha-len-1-yl)-N,N’-bis(phenyl)-2,2’-dimethylbenzidine (α-NPD) applied to the hole transport layer (HTL), was fabricated and subsequently assessed. Compared to the device without an HIL, the device with HIL using SAM showed a luminance of 1630.16 (Cd/m²) at the maximum cut-off voltage of 11 V, and the efficiency was increased to ≥200% from the initial turn-on voltage of 3 V to the maximum cut-off voltage. These results suggest that adding an interlayer and the consequent carrier ladder effect led to more efficient injection and transport, possibly lowering the hole injection barrier and allowing excellent power efficiency.

In this paper, we propose a new approach for service robots to perform delivery tasks in indoor environments, including map-building and the automatic renewal of destinations for navigation. The first step involves converting the available floor plan (i.e., CAD drawing) of a new space into a grid map that the robot can navigate. The system then segments the space in the map and generates movable initial nodes through a generalized Voronoi graph (GVG) thinning process. As the second step, we perform room segmentation from the grid map of the indoor environment and classify each space. Next, when the delivery object is recognized while searching the set space using the laser and RGB-D sensor, the system automatically updates itself to a position that makes it easier to grab objects, taking into consideration geometric relationships with surrounding obstacles. Also, the system supports the robot to autonomously explore the space where the user’s errand can be performed by hierarchically linking recognized objects and spatial information. Experiments related to map generation, estimating space from the recognized objects, and destination node updates were conducted from CAD drawings of buildings with actual multiple floors and rooms, and the performance of each stage of the process was evaluated. From the quantitative evaluation of each stage, the proposed system confirmed the potential of partial automation in performing location-based robot services.

Recently, some research has been attended to estimate the moving destination automatically from a recognized object in order to run a user