Linganisha mbinu

	Uzoefu wa Simu ya Mkononi	Ukusanyaji wa Data za Kihisi
Nyanja	Metodolojia ya Dodoso	Metodolojia ya Dodoso
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	1983	1990s–2000s (widespread deployment with IoT ~2000s)
Mwanzilishi	Mihaly Csikszentmihalyi & Reed Larson	Multidisciplinary; sensor networks formalized in engineering and computer science from the 1990s onward
Aina	Intensive longitudinal data collection technique	Quantitative / mixed data collection technique
Chanzo asilia	Csikszentmihalyi, M., & Larson, R. (1987). Validity and reliability of the Experience-Sampling Method. Journal of Nervous and Mental Disease, 175(9), 526–536.	Chong, C.-Y., & Kumar, S. P. (2003). Sensor networks: Evolution, opportunities, and challenges. Proceedings of the IEEE, 91(8), 1247–1256.
Majina mbadala	ESM, Experience Sampling Method, Ecological Momentary Assessment, EMA	sensor measurement, instrumented data collection, physical sensor logging, IoT data collection
Zinazohusiana	5	5
Muhtasari	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.	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.