Módszerek összehasonlítása

	Telefonnal támogatott szenzoradat-gyűjtés	Adatszedés szenzorokkal – Szenzorokon alapuló adatszedés
Tudományterület	Kérdőíves felmérések módszertana	Kérdőíves felmérések módszertana
Módszercsalád	Process / pipeline	Process / pipeline
Keletkezés éve	2000s–2010s (aligned with smartphone proliferation)	1990s–2000s (widespread deployment with IoT ~2000s)
Megalkotó	Emerging from ubiquitous computing and digital health research communities; no single originator	Multidisciplinary; sensor networks formalized in engineering and computer science from the 1990s onward
Típus	Passive and active data collection via telephone/smartphone sensors	Quantitative / mixed data collection technique
Alapmű	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.	Chong, C.-Y., & Kumar, S. P. (2003). Sensor networks: Evolution, opportunities, and challenges. Proceedings of the IEEE, 91(8), 1247–1256.
Alternatív nevek	phone-based sensor data collection, telephone-mediated sensor monitoring, mobile phone sensor data collection, TASDC	sensor measurement, instrumented data collection, physical sensor logging, IoT data collection
Kapcsolódó	5	5
Összefoglaló	Telephone-assisted sensor data collection uses participants' mobile phones as sensing platforms to gather continuous or triggered streams of physical and behavioral data — such as movement, location, and ambient sound — without requiring them to attend a lab. A research application installed on the phone captures sensor readings and transmits them to a central server, enabling large-scale, ecologically valid measurement of real-world behavior over days or weeks.	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.