Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Sběr senzorových dat tváří v tvář× | Sběr senzorových dat× | |
|---|---|---|
| Obor | Metodologie dotazníkových šetření | Metodologie dotazníkových šetření |
| Rodina | Process / pipeline | Process / pipeline |
| Rok vzniku≠ | 1990s–2000s (growth with wearable/biosensor technology) | 1990s–2000s (widespread deployment with IoT ~2000s) |
| Tvůrce≠ | Emerging from ambulatory assessment and wearable computing research communities | Multidisciplinary; sensor networks formalized in engineering and computer science from the 1990s onward |
| Typ≠ | Quantitative / mixed-methods data collection technique | Quantitative / mixed data collection technique |
| Původní zdroj≠ | 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 ↗ |
| Další názvy | in-person sensor data collection, proximate biosensor data collection, face-to-face ambulatory assessment, on-site sensor recording | sensor measurement, instrumented data collection, physical sensor logging, IoT data collection |
| Příbuzné≠ | 4 | 5 |
| Shrnutí≠ | 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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