Salīdzināt metodes

	Sensoru datu vākšana	Mobilā pieredzes izlase
Nozare	Aptauju metodoloģija	Aptauju metodoloģija
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	1990s–2000s (widespread deployment with IoT ~2000s)	1983
Autors	Multidisciplinary; sensor networks formalized in engineering and computer science from the 1990s onward	Mihaly Csikszentmihalyi & Reed Larson
Tips	Quantitative / mixed data collection technique	Intensive longitudinal data collection technique
Pirmavots	Chong, C.-Y., & Kumar, S. P. (2003). Sensor networks: Evolution, opportunities, and challenges. Proceedings of the IEEE, 91(8), 1247–1256.	Csikszentmihalyi, M., & Larson, R. (1987). Validity and reliability of the Experience-Sampling Method. Journal of Nervous and Mental Disease, 175(9), 526–536.
Citi nosaukumi	sensor measurement, instrumented data collection, physical sensor logging, IoT data collection	ESM, Experience Sampling Method, Ecological Momentary Assessment, EMA
Saistītās	5	5
Kopsavilkums	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.	Mobile Experience Sampling (ESM) is an intensive longitudinal data-collection technique in which participants respond to brief, repeated questionnaires delivered to their smartphones at random or scheduled intervals throughout the day. By capturing thoughts, feelings, behaviors, and context at or near the moment they occur, ESM minimises retrospective recall bias and provides a high-resolution picture of psychological and behavioral fluctuations in everyday life.