GroupBeaMR: Analyzing Collaborative Group Behavior in Mixed Reality Through Passive Sensing and Sociometry
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913685766668288 |
|---|---|
| author | Romero, Diana Chandio, Yasra Anwar, Fatima Elmalaki, Salma |
| author_facet | Romero, Diana Chandio, Yasra Anwar, Fatima Elmalaki, Salma |
| contents | Understanding group behavior is crucial for enhancing collaboration and productivity in mixed reality (MR). This paper introduces a framework for group behavior analysis in MR, or GroupBeaMR for short for analyzing group behavior in MR. GroupBeaMR leverages MR headsets' sensors to analyze group behavior through conversation, shared attention, and proximity, identifying cohesive, fragmented, and competitive interaction patterns. Using social network analysis, GroupBeaMR provides quantitative assessments of group dynamics, offering insights into collaboration structures. A user study with 48 participants across 12 groups validates the framework's ability to distinguish interaction patterns in MR environments. Our analyses show that group behavior is independent of task performance, emphasizing the significance of social interaction patterns. Our group-type assignments indicate that sensor-based assessments in MR can provide meaningful insights into collaborative experiences, supporting the design of systems that adapt and optimize group behaviors. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_05258 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GroupBeaMR: Analyzing Collaborative Group Behavior in Mixed Reality Through Passive Sensing and Sociometry Romero, Diana Chandio, Yasra Anwar, Fatima Elmalaki, Salma Human-Computer Interaction Emerging Technologies Understanding group behavior is crucial for enhancing collaboration and productivity in mixed reality (MR). This paper introduces a framework for group behavior analysis in MR, or GroupBeaMR for short for analyzing group behavior in MR. GroupBeaMR leverages MR headsets' sensors to analyze group behavior through conversation, shared attention, and proximity, identifying cohesive, fragmented, and competitive interaction patterns. Using social network analysis, GroupBeaMR provides quantitative assessments of group dynamics, offering insights into collaboration structures. A user study with 48 participants across 12 groups validates the framework's ability to distinguish interaction patterns in MR environments. Our analyses show that group behavior is independent of task performance, emphasizing the significance of social interaction patterns. Our group-type assignments indicate that sensor-based assessments in MR can provide meaningful insights into collaborative experiences, supporting the design of systems that adapt and optimize group behaviors. |
| title | GroupBeaMR: Analyzing Collaborative Group Behavior in Mixed Reality Through Passive Sensing and Sociometry |
| topic | Human-Computer Interaction Emerging Technologies |
| url | https://arxiv.org/abs/2411.05258 |