Examining Real-World Use of Collaboration Tools through Body Tracking Sensors

Researching hybrid work: the role of automation

• Research into work processes in professional contexts traditionally relies on ethnographic methods
• Can we use body tracking sensors (or other automated means) to gather meaningful long-term insight from such contexts, without requiring continuous researcher presence?
  • This was (more or less) the research question of our project “Investigation of the honeypot effect on (semi-)public interactive ambient displays in long-term field studies” (2021–2024, project report: Koch et al., 2024)
  • Answer: well... kind of? 🤔

What do body tracking sensors do anyway?

Schwarzer et al. (2023a)

See for yourself: poseviz.com / Fietkau (2023)

What have we successfully measured via body tracking data?

• Walking paths 👣 (Schwarzer et al., 2023b)
• Coffee mug effect ☕
• Honeypot effect 🍯 (kind of)

Biggest problem: the “wall of intent” – body tracking data does not show why people do what they do

Other issues: deanonymization, body normativity

Re-incorporating qualitative methods

Conclusions

• Body tracking data can be a rich source of descriptive/
  observational insight
• But: observation of movements leaves intentions in the dark
• We have high hopes for more sophisticated integrations of quantitative (sensor-based) insight with qualitative, ethnographic methods
• Future progress will depend on pragmatic factors (i.e. funding)