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面对面传感器数据收集×传感器数据收集×
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方法族Process / pipelineProcess / pipeline
起源年份1990s–2000s (growth with wearable/biosensor technology)1990s–2000s (widespread deployment with IoT ~2000s)
提出者Emerging from ambulatory assessment and wearable computing research communitiesMultidisciplinary; sensor networks formalized in engineering and computer science from the 1990s onward
类型Quantitative / mixed-methods data collection techniqueQuantitative / mixed data collection technique
开创性文献Trull, T. J., & Ebner-Priemer, U. (2013). Ambulatory assessment. Annual Review of Clinical Psychology, 9, 151–176. DOI ↗Chong, C.-Y., & Kumar, S. P. (2003). Sensor networks: Evolution, opportunities, and challenges. Proceedings of the IEEE, 91(8), 1247–1256. DOI ↗
别名in-person sensor data collection, proximate biosensor data collection, face-to-face ambulatory assessment, on-site sensor recordingsensor measurement, instrumented data collection, physical sensor logging, IoT data collection
相关45
摘要Face-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.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.
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ScholarGate方法对比: Face-to-face Sensor Data Collection · Sensor Data Collection. 于 2026-06-17 检索自 https://scholargate.app/zh/compare