Módszerek összehasonlítása

	Személyes szenzoradat-gyűjtés	Adatszedés szenzorokkal – Szenzorokon alapuló adatszedés
Tudományterület	Kérdőíves felmérések módszertana	Kérdőíves felmérések módszertana
Módszercsalád	Process / pipeline	Process / pipeline
Keletkezés éve	1990s–2000s (growth with wearable/biosensor technology)	1990s–2000s (widespread deployment with IoT ~2000s)
Megalkotó	Emerging from ambulatory assessment and wearable computing research communities	Multidisciplinary; sensor networks formalized in engineering and computer science from the 1990s onward
Típus	Quantitative / mixed-methods data collection technique	Quantitative / mixed data collection technique
Alapmű	Trull, T. J., & Ebner-Priemer, U. (2013). Ambulatory assessment. Annual Review of Clinical Psychology, 9, 151–176.	Chong, C.-Y., & Kumar, S. P. (2003). Sensor networks: Evolution, opportunities, and challenges. Proceedings of the IEEE, 91(8), 1247–1256.
Alternatív nevek	in-person sensor data collection, proximate biosensor data collection, face-to-face ambulatory assessment, on-site sensor recording	sensor measurement, instrumented data collection, physical sensor logging, IoT data collection
Kapcsolódó	4	5
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.	Sensor data collection uses physical or digital instruments to automatically capture quantitative measurements from the environment, human bodies, or machines over time. Common sensors measure temperature, motion, heart rate, location, light, sound, or chemical properties. Because the recording is automated and continuous, the method can produce high-frequency datasets with minimal researcher burden, making it central to IoT, environmental monitoring, wearable research, and behavioral studies.