ScholarGate
Assistent

Methoden vergelijken

Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.

Sensor Data Collection×Longitudinale verzameling van sensordata×
VakgebiedSurveymethodologieSurveymethodologie
FamilieProcess / pipelineProcess / pipeline
Jaar van ontstaan1990s–2000s (widespread deployment with IoT ~2000s)1990s–2000s (accelerated with IoT and wearable devices from ~2010)
GrondleggerMultidisciplinary; sensor networks formalized in engineering and computer science from the 1990s onwardEmerging from ambulatory assessment and wearable technology research communities
TypeQuantitative / mixed data collection techniqueLongitudinal quantitative/mixed data collection technique
Oorspronkelijke bronChong, C.-Y., & Kumar, S. P. (2003). Sensor networks: Evolution, opportunities, and challenges. Proceedings of the IEEE, 91(8), 1247–1256. DOI ↗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.] link ↗
Aliassensensor measurement, instrumented data collection, physical sensor logging, IoT data collectionlong-term sensor monitoring, longitudinal sensing, continuous sensor logging, repeated-measures sensor collection
Verwant53
SamenvattingSensor 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.
ScholarGateGegevensset
  1. v1
  2. 2 Bronnen
  3. PUBLISHED
  1. v1
  2. 2 Bronnen
  3. PUBLISHED

Naar zoeken Dia's downloaden

ScholarGateMethoden vergelijken: Sensor Data Collection · Longitudinal Sensor Data Collection. Geraadpleegd op 2026-06-15 via https://scholargate.app/nl/compare