Σύγκριση μεθόδων
Εξετάστε τις επιλεγμένες μεθόδους δίπλα-δίπλα· οι γραμμές που διαφέρουν επισημαίνονται.
| Συλλογή Δεδομένων Αισθητήρων Πρόσωπο με Πρόσωπο× | Συλλογή Δεδομένων από Κινητούς Αισθητήρες× | |
|---|---|---|
| Πεδίο | Μεθοδολογία Επισκοπήσεων | Μεθοδολογία Επισκοπήσεων |
| Οικογένεια | Process / pipeline | Process / pipeline |
| Έτος προέλευσης≠ | 1990s–2000s (growth with wearable/biosensor technology) | Mid-2000s (smartphone-era formalization ~2006–2010) |
| Δημιουργός≠ | Emerging from ambulatory assessment and wearable computing research communities | Andrew Campbell, Tanzeem Choudhury, and colleagues (early smartphone sensing research); broader field of ubiquitous computing |
| Τύπος≠ | Quantitative / mixed-methods data collection technique | Passive and active quantitative data collection technique |
| Θεμελιώδης πηγή≠ | Trull, 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 ↗ |
| Εναλλακτικές ονομασίες | in-person sensor data collection, proximate biosensor data collection, face-to-face ambulatory assessment, on-site sensor recording | mobile sensing, smartphone sensor data collection, wearable sensor data collection, passive mobile data collection |
| Συναφείς | 4 | 4 |
| Σύνοψη≠ | 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. | 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. |
| ScholarGateΣύνολο δεδομένων ↗ |
|
|