Methoden vergleichen
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| Mobile Sensor Data Collection× | Sensordatenerfassung× | |
|---|---|---|
| Fachgebiet | Umfragemethodik | Umfragemethodik |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | Mid-2000s (smartphone-era formalization ~2006–2010) | 1990s–2000s (widespread deployment with IoT ~2000s) |
| Urheber≠ | Andrew Campbell, Tanzeem Choudhury, and colleagues (early smartphone sensing research); broader field of ubiquitous computing | Multidisciplinary; sensor networks formalized in engineering and computer science from the 1990s onward |
| Typ≠ | Passive and active quantitative data collection technique | Quantitative / mixed data collection technique |
| Wegweisende Quelle≠ | 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 ↗ | Chong, C.-Y., & Kumar, S. P. (2003). Sensor networks: Evolution, opportunities, and challenges. Proceedings of the IEEE, 91(8), 1247–1256. DOI ↗ |
| Aliasnamen | mobile sensing, smartphone sensor data collection, wearable sensor data collection, passive mobile data collection | sensor measurement, instrumented data collection, physical sensor logging, IoT data collection |
| Verwandt≠ | 4 | 5 |
| Zusammenfassung≠ | 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. | 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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