手法を比較

	センサーデータ収集	縦断的センサーデータ収集
分野	調査方法論	調査方法論
系統	Process / pipeline	Process / pipeline
提唱年	1990s–2000s (widespread deployment with IoT ~2000s)	1990s–2000s (accelerated with IoT and wearable devices from ~2010)
提唱者	Multidisciplinary; sensor networks formalized in engineering and computer science from the 1990s onward	Emerging from ambulatory assessment and wearable technology research communities
種類	Quantitative / mixed data collection technique	Longitudinal quantitative/mixed data collection technique
原典	Chong, C.-Y., & Kumar, S. P. (2003). Sensor networks: Evolution, opportunities, and challenges. Proceedings of the IEEE, 91(8), 1247–1256.	Lanza, S. T., Collins, L. M., Lemmon, D. R., & Schafer, J. L. (2005). PROC LCA: A SAS procedure for latent class analysis. Structural Equation Modeling, 14(4), 671–694. [For longitudinal intensive repeated-measures designs context, see also: Shiffman, S., Stone, A. A., & Hufford, M. R. (2008). Ecological momentary assessment. Annual Review of Clinical Psychology, 4, 1–32.]
別名	sensor measurement, instrumented data collection, physical sensor logging, IoT data collection	long-term sensor monitoring, longitudinal sensing, continuous sensor logging, repeated-measures sensor collection
関連	5	3
概要	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.	Longitudinal sensor data collection deploys physical or digital sensors to record phenomena continuously or at regular intervals across an extended study period — days, months, or years. Unlike one-shot measurement, the repeated temporal structure captures change, trajectory, and variability in outcomes such as physical activity, environmental exposure, sleep, or physiological state. The approach combines the ecological validity of real-world sensing with the analytical power of longitudinal design.