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분야조사방법론조사방법론
계열Process / pipelineProcess / pipeline
기원 연도1990s–2000s (growth with wearable/biosensor technology)Mid-2000s (smartphone-era formalization ~2006–2010)
창시자Emerging from ambulatory assessment and wearable computing research communitiesAndrew Campbell, Tanzeem Choudhury, and colleagues (early smartphone sensing research); broader field of ubiquitous computing
유형Quantitative / mixed-methods data collection techniquePassive and active quantitative data collection technique
원전Trull, T. J., & Ebner-Priemer, U. (2013). Ambulatory assessment. Annual Review of Clinical Psychology, 9, 151–176. DOI ↗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. DOI ↗
별칭in-person sensor data collection, proximate biosensor data collection, face-to-face ambulatory assessment, on-site sensor recordingmobile sensing, smartphone sensor data collection, wearable sensor data collection, passive mobile data collection
관련44
요약Face-to-face sensor data collection involves attaching or deploying sensors — physiological, motion, environmental, or proximity-based — on or around participants during in-person research sessions. The co-present setting allows direct researcher oversight of equipment, real-time signal monitoring, and immediate troubleshooting, yielding high-fidelity continuous or event-triggered data streams that capture objective behavioral and physiological indicators as they unfold.Mobile sensor data collection uses the built-in sensors of smartphones, tablets, or wearable devices to capture behavioral, physiological, and environmental data in real-world settings. Sensors such as accelerometers, GPS, heart rate monitors, ambient light detectors, and microphones record data passively or on demand, enabling researchers to study human behavior with high temporal resolution outside the laboratory.
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ScholarGate방법 비교: Face-to-face Sensor Data Collection · Mobile Sensor Data Collection. 2026-06-17에 다음에서 검색함: https://scholargate.app/ko/compare