Redesign and initial validation of an instrument to assess the motivational qualities of music in exercise: The Brunel Music Rating Inventory-2

In the present study, a measure to assess the motivational qualities of music in exercise was redesigned, extending previous research efforts (Karageorghis et al., 1999). The original measure, the Brunel Music Rating Inventory (BMRI), had shown limitations in its factor structure and its applicability to non-experts in music selection. Redesign of the BMRI used in-depth interviews with eight participants (mean age 31.9 years, s¼8.9 years) to establish the initial item pool, which was examined using a series of confirmatory factor analyses. A single-factor model provided a good fit across three musical selections with different motivational qualities (comparative fit index, CFI: 0.95 – 0.98; standardized root mean residual, SRMR: 0.03 – 0.05). The single-factor model also demonstrated acceptable fit across two independent samples and both sexes using one piece of music (CFI: 0.86 – 1.00; SRMR: 0.04 – 0.07). The BMRI was designed for experts in selecting music for exercise (e.g. dance aerobic instructors), whereas the BMRI-2 can be used both by exercise instructors and participants. The psychometric properties of the BMRI-2 are stronger than those of the BMRI and it is easier to use. The BMRI-2 provides a valid and internally consistent tool by which music can be selected to accompany a bout of exercise or a training session. Furthermore, the BMRI-2 enables researchers to standardize music in experimental protocols involving exercise-related tasks

Enhancing Perceived Safety in Human–Robot Collaborative Construction Using Immersive Virtual Environments

Advances in robotics now permit humans to work collaboratively with robots. However, humans often feel unsafe working alongside robots. Our knowledge of how to help humans overcome this issue is limited by two challenges. One, it is difficult, expensive and time-consuming to prototype robots and set up various work situations needed to conduct studies in this area. Two, we lack strong theoretical models to predict and explain perceived safety and its influence on human–robot work collaboration (HRWC). To address these issues, we introduce the Robot Acceptance Safety Model (RASM) and employ immersive virtual environments (IVEs) to examine perceived safety of working on tasks alongside a robot. Results from a between-subjects experiment done in an IVE show that separation of work areas between robots and humans increases perceived safety by promoting team identification and trust in the robot. In addition, the more participants felt it was safe to work with the robot, the more willing they were to work alongside the robot in the future.University of Michigan Mcubed Grant: Virtual Prototyping of Human-Robot Collaboration in Unstructured Construction EnvironmentsPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/145620/1/You et al. forthcoming in AutCon.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/145620/4/You et al. 2018.pdfDescription of You et al. 2018.pdf : Published Versio