Módszerek összehasonlítása

	Személyes szenzoradat-gyűjtés	Mobil szenzoradat-gyűjtés
Tudományterület	Kérdőíves felmérések módszertana	Kérdőíves felmérések módszertana
Módszercsalád	Process / pipeline	Process / pipeline
Keletkezés éve	1990s–2000s (growth with wearable/biosensor technology)	Mid-2000s (smartphone-era formalization ~2006–2010)
Megalkotó	Emerging from ambulatory assessment and wearable computing research communities	Andrew Campbell, Tanzeem Choudhury, and colleagues (early smartphone sensing research); broader field of ubiquitous computing
Típus	Quantitative / mixed-methods data collection technique	Passive and active quantitative data collection technique
Alapmű	Trull, T. J., & Ebner-Priemer, U. (2013). Ambulatory assessment. Annual Review of Clinical Psychology, 9, 151–176.	Lane, N. D., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., & Campbell, A. T. (2010). A survey of mobile phone sensing. IEEE Communications Magazine, 48(9), 140–150.
Alternatív nevek	in-person sensor data collection, proximate biosensor data collection, face-to-face ambulatory assessment, on-site sensor recording	mobile sensing, smartphone sensor data collection, wearable sensor data collection, passive mobile data collection
Kapcsolódó	4	4
Összefoglaló	Face-to-face sensor data collection involves attaching or deploying sensors — physiological, motion, environmental, or proximity-based — on or around participants during in-person research sessions. The co-present setting allows direct researcher oversight of equipment, real-time signal monitoring, and immediate troubleshooting, yielding high-fidelity continuous or event-triggered data streams that capture objective behavioral and physiological indicators as they unfold.	Mobile sensor data collection uses the built-in sensors of smartphones, tablets, or wearable devices to capture behavioral, physiological, and environmental data in real-world settings. Sensors such as accelerometers, GPS, heart rate monitors, ambient light detectors, and microphones record data passively or on demand, enabling researchers to study human behavior with high temporal resolution outside the laboratory.