مقایسهٔ روشها
روشهای انتخابی خود را کنار هم مرور کنید؛ ردیفهای متفاوت برجسته شدهاند.
| گردآوری دادههای حسگر به صورت حضوری× | گردآوری دادههای حسگر× | |
|---|---|---|
| حوزه | روششناسی پیمایش | روششناسی پیمایش |
| خانواده | Process / pipeline | Process / pipeline |
| سال پیدایش≠ | 1990s–2000s (growth with wearable/biosensor technology) | 1990s–2000s (widespread deployment with IoT ~2000s) |
| پدیدآور≠ | Emerging from ambulatory assessment and wearable computing research communities | Multidisciplinary; sensor networks formalized in engineering and computer science from the 1990s onward |
| نوع≠ | Quantitative / mixed-methods data collection technique | Quantitative / mixed data collection technique |
| منبع بنیادین≠ | 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 ↗ |
| نامهای دیگر | 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 |
| مرتبط≠ | 4 | 5 |
| خلاصه≠ | 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. |
| ScholarGateمجموعهداده ↗ |
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