Karl Voit  Mar 9

"A new #opensource edge #AI system called π #RuView is turning ordinary #WiFi infrastructure into a #throughwall #humansensing platform detecting body pose, vital signs, and #movementpatterns without a single #camera, raising urgent #security and #surveillance concerns." 🎥

This turns any wifi hotspot into an x-ray like live #camera that leaks your movements (identifying people through limb motion patterns) and emotions (pulse variations) to any attacker or data-vampire like the average megacorp.

Source:
WiFi Signals Reveal Human Activities Through Walls by Mapping Body Keypoints
https://cybersecuritynews.com/wifi-signals-reveal-human-activities/

WiFi Signals Reveal Human Activities Through Walls by Mapping Body Keypoints

A new open-source edge AI system called π RuView is turning ordinary WiFi infrastructure into a through-wall human-sensing platform detecting body pose, vital signs, and movement patterns without a single camera, raising urgent security and surveillance concerns.

Cyber Security News
MultiX-6G ProjectSep 23

🎉 #NewPaperAlert! MultiX research on human sensing with mmWave radar accepted.

⚡️We tackle open-set gait recognition with a privacy-preserving Edge #AI approach.

Dataset & code are public! 🚀
🔗 https://zenodo.org/records/15907211

#MultiX #6G #Research #HumanSensing #OpenScience

Martin LoidlJan 11, 2023

Happy to share that our most recent paper made it into the editor's choice list in #Sustainability. Hope you like it as well! https://doi.org/10.3390/su15010622

#mixedmethods #cycling #GIS #sociology #humansensing #planning

A Framework to Facilitate Advanced Mixed Methods Studies for Investigating Interventions in Road Space for Cycling

Cycling mobility contributes to better livability in cites, helps societies to reduce greenhouse gas emissions and their dependency on fossil fuels, and shows positive health effects. However, unattractive conditions, primarily inadequate infrastructure, hinder the further growth of cycling mobility. As interactions of cyclists with the (built) environment are complex, assessing potential impacts of an intervention aimed at improving physical conditions is not trivial. Despite a growing body of literature on various facets of cycling mobility, assessments are widely limited to a single method and thereby either focus on one detailed aspect or on one perspective. While multi-method and mixed methods studies are emerging, they are not embedded into a structured, integrated framework for assessing systemic effects of interventions yet. Therefore, we propose a conceptual integration of several relevant methods such as questionnaires, interviews, GIS analyses and human sensing. In this paper, we present a generic, extensible framework that offers guidance for developing and implementing case-specific mixed methods designs for multifaceted assessments of interventions. The framework supports domain experts and researchers across different stages of conducting a study. Results from this research further indicate the added value of mixed methods studies compared to single-method approaches.

MDPI