Lessons Learned About Designing and Conducting Studies From HRI Experts

The field of human-robot interaction (HRI) research is multidisciplinary and requires researchers to understand diverse fields including computer science, engineering, informatics, philosophy, psychology, and more disciplines. However, it is hard to be an expert in everything. To help HRI researchers develop methodological skills, especially in areas that are relatively new to them, we conducted a virtual workshop, Workshop Your Study Design (WYSD), at the 2021 International Conference on HRI. In this workshop, we grouped participants with mentors, who are experts in areas like real-world studies, empirical lab studies, questionnaire design, interview, participatory design, and statistics. During and after the workshop, participants discussed their proposed study methods, obtained feedback, and improved their work accordingly. In this paper, we present 1) Workshop attendees’ feedback about the workshop and 2) Lessons that the participants learned during their discussions with mentors. Participants’ responses about the workshop were positive, and future scholars who wish to run such a workshop can consider implementing their suggestions. The main contribution of this paper is the lessons learned section, where the workshop participants contributed to forming this section based on what participants discovered during the workshop. We organize lessons learned into themes of 1) Improving study design for HRI, 2) How to work with participants - especially children -, 3) Making the most of the study and robot’s limitations, and 4) How to collaborate well across fields as they were the areas of the papers submitted to the workshop. These themes include practical tips and guidelines to assist researchers to learn about fields of HRI research with which they have limited experience. We include specific examples, and researchers can adapt the tips and guidelines to their own areas to avoid some common mistakes and pitfalls in their research

Lessons Learned About Designing and Conducting Studies From HRI Experts.

The field of human-robot interaction (HRI) research is multidisciplinary and requires researchers to understand diverse fields including computer science, engineering, informatics, philosophy, psychology, and more disciplines. However, it is hard to be an expert in everything. To help HRI researchers develop methodological skills, especially in areas that are relatively new to them, we conducted a virtual workshop, Workshop Your Study Design (WYSD), at the 2021 International Conference on HRI. In this workshop, we grouped participants with mentors, who are experts in areas like real-world studies, empirical lab studies, questionnaire design, interview, participatory design, and statistics. During and after the workshop, participants discussed their proposed study methods, obtained feedback, and improved their work accordingly. In this paper, we present 1) Workshop attendees' feedback about the workshop and 2) Lessons that the participants learned during their discussions with mentors. Participants' responses about the workshop were positive, and future scholars who wish to run such a workshop can consider implementing their suggestions. The main contribution of this paper is the lessons learned section, where the workshop participants contributed to forming this section based on what participants discovered during the workshop. We organize lessons learned into themes of 1) Improving study design for HRI, 2) How to work with participants - especially children -, 3) Making the most of the study and robot's limitations, and 4) How to collaborate well across fields as they were the areas of the papers submitted to the workshop. These themes include practical tips and guidelines to assist researchers to learn about fields of HRI research with which they have limited experience. We include specific examples, and researchers can adapt the tips and guidelines to their own areas to avoid some common mistakes and pitfalls in their research

Interaction Design of Sociable Driving Agents as an Effective In-vehicle Interface

豊橋技術科学大

The Effect of Kinesiotape Application on Functional Performance in Surgeons Who Have Musculo-Skeletal Pain After Performing Surgery

AIM: Surgeons make up a unique group that is at risk for developing work-related musculoskeletal symptoms. The purpose of this study was to evaluate the effect of Kinesiotape technique on pain and functional performance in surgeons who have musculoskeletal system pain after performing surgery. MATERIAL and METHODS: 32 surgeons between the ages of 27 and 44 yrs working in a university hospital were included. The Visual Analog Scale (VAS) was used to evaluate the surgeons' neck and low back pain and the Oswestry Low Back and Neck Disability Indexes were used to determine the impact of pain on daily living activities. First, surgeons were evaluated without Kinesiotape application, then evaluated again on the first day and fourth day of Kinesiotape application. RESULTS: The results showed that surgeons had a significant reduction in neck and low back pain (p<0.05). There were improvements in both Oswestry Low Back Disability Index and Neck Disability Index scores when compared with their initial status (p<0.05). After Kinesiotape application, neck and low back range of motions' scores showed an increase (p<0.05). CONCLUSION: Findings demonstrated that Kinesio taping would be an effective method for reducing neck and low back pain and improving functional performance.WoSScopu

Are two heads better than one? exploring two-party conversations for car navigation voice guidance

© 2020 Owner/Author. Voice guidance for car navigation typically considers drivers as docile actors. Recent works highlight limitations to this assumption which make drivers rely less on given directions. To explore how drivers can make better navigation decisions, we conducted a pilot Wizard-of-Oz study that gives turn suggestions in conversations between two voice agents. We asked 30 participants to drive in a simulation environment using voice guidance that gives three types of suggestions: familiar, optimal, and new routes. We examined their route choices, perceived workload and utterances while driving. We found that while most drivers followed directions appropriate for the given scenarios, they were more likely to make inappropriate choices after hearing alternatives in conversations. On the other hand, two-party conversations allowed drivers to better reflect on their choices after trips. We conclude by discussing preliminary design implications for car navigation voice guidance specifically and recommender systems in general

Comparison of Mini-squats and Straight Leg Raises in Patients with Knee Osteoarthritis: A Randomized Controlled Clinical Trial

WOS: 000317807500004Objectives: In this study, we aimed to compare the effects of the straight leg raise exercise (SLRE) and mini squat exercise (MSE) on pain intensity, performance, muscle strength, physical function and balance in patients with knee osteoarthritis (OA). Patients and methods: Forty female patients (mean age 59.93 +/- 10.61 years; range 30-65 years) who were admitted to Baskent University Hospital between February 2009 and February 2010 with bilateral knee OA were included in this randomized controlled double-blind clinical trial. The patients were assigned into two groups, including SLRE and MSE. Both exercise groups were scheduled for a three-week program of five sessions per week. Both groups also received an electrotherapy program in each session. Knee pain using the visual analog scale (VAS), physical performance using Timed Up and Go (TUG) Test, isokinetic quadriceps and hamstring muscle strengths using Cybex II, static balance using SportKAT 3000, physical function using Knee Injury and Osteoarhtritis Outcome Score-Physical Function Short Form (KOOS-PS) were assessed at baseline, at treatment end, and one month after treatment end. Results: No significant difference was found in static balance testing parameters after exercise program (EP) (p=0.659) and during follow-up period (p=0.327) between the groups. There was no significant difference in KOOS-PS scores after EP (p=0.398) and during follow-up (p=0.201) between SLRE and MSE groups. There was no significant difference in VAS scores after EP (p=0.149), however significant difference was found during follow-up (p=0.030) between SLRE and MSE groups. Mini squat exercise group had significantly higher TUG scores and higher right knee extensor torque at 60 degrees/sec (p=0.024), 90 degrees/sec (p=0.003), 120 degrees/sec (p=0.005) and 180 degrees/sec (p=0.017) compared to the SLRE group at the end of EP. Conclusion: Further studies are required including long-term follow-up and the evaluation criteria of the effectiveness of SLRE and MSE after this period

Exploring factors that influence connected drivers to (not) use or follow recommended optimal routes

Navigation applications are becoming ubiquitous in our daily navigation experiences. With the intention to circumnavigate congested roads, their route guidance always follows the basic assumption that drivers always want the fastest route. However, it is unclear how their recommendations are followed and what factors affect their adoption. We present the results of a semi-structured qualitative study with 17 drivers, mostly from the Philippines and Japan. We recorded their daily commutes and occasional trips, and inquired into their navigation practices, route choices and on-the-fly decision-making. We found that while drivers choose a recommended route in urgent situations, many still preferred to follow familiar routes. Drivers deviated because of a recommendation’s use of unfamiliar roads, lack of local context, perceived driving unsuitability, and inconsistencies with realized navigation experiences. Our findings and implications emphasize their personalization needs, and how the right amount of algorithmic sophistication can encourage behavioral adaptation. © 2019 Copyright held by the owner/author(s)