Enhancing Non-Invasive Human Activity Recognition by Fusioning Electrical Load and Vibrational Measurements: Unterschied zwischen den Versionen
(Die Seite wurde neu angelegt: „{{Vortrag |vortragender=Andreas Foitzik |email=upfle@student.kit.edu |vortragstyp=Masterarbeit |betreuer=Klemens Böhm |termin=Institutsseminar/2019-08-09 |kur…“) |
Keine Bearbeitungszusammenfassung |
||
| Zeile 4: | Zeile 4: | ||
|vortragstyp=Masterarbeit | |vortragstyp=Masterarbeit | ||
|betreuer=Klemens Böhm | |betreuer=Klemens Böhm | ||
|termin=Institutsseminar/2019-08-09 | |termin=Institutsseminar/2019-08-09 (Raumverlegung wegen Klausuransicht) | ||
|kurzfassung=Professional installation of stationary sensors burdens the adoption of Activity Recognition Systems in households. This can be circumvented by utilizing sensors that are cheap, easy to set up and adaptable to a variety of homes. Since 72% of European consumers will have Smart Meters by 2020, it provides an omnipresent basis for Activity Recognition. | |kurzfassung=Professional installation of stationary sensors burdens the adoption of Activity Recognition Systems in households. This can be circumvented by utilizing sensors that are cheap, easy to set up and adaptable to a variety of homes. Since 72% of European consumers will have Smart Meters by 2020, it provides an omnipresent basis for Activity Recognition. | ||
This thesis investigates, how a Smart Meter’s limited recognition of appliance involving activities can be extended by Vibration Sensors. We provide an experimental setup to aggregate a dedicated dataset with a sampling frequency of 25,600 Hz. We evaluate the impact of combining a Smart Meter and Vibration Sensors on a system’s accuracy, by means of four developed Activity Recognition Systems. This results in the quantification of the impact. We found out that through combining these sensors, the accuracy of an Activity Recognition System rather strives towards the highest accuracy of a single underlying sensor, than jointly surpassing it. | This thesis investigates, how a Smart Meter’s limited recognition of appliance involving activities can be extended by Vibration Sensors. We provide an experimental setup to aggregate a dedicated dataset with a sampling frequency of 25,600 Hz. We evaluate the impact of combining a Smart Meter and Vibration Sensors on a system’s accuracy, by means of four developed Activity Recognition Systems. This results in the quantification of the impact. We found out that through combining these sensors, the accuracy of an Activity Recognition System rather strives towards the highest accuracy of a single underlying sensor, than jointly surpassing it. | ||
}} | }} | ||
Version vom 31. Juli 2019, 16:15 Uhr
| Vortragende(r) | Andreas Foitzik | |
|---|---|---|
| Vortragstyp | Masterarbeit | |
| Betreuer(in) | Klemens Böhm | |
| Termin | [[Institutsseminar/2019-08-09 (Raumverlegung wegen Klausuransicht)|]] | |
| Vortragssprache | ||
| Vortragsmodus | ||
| Kurzfassung | Professional installation of stationary sensors burdens the adoption of Activity Recognition Systems in households. This can be circumvented by utilizing sensors that are cheap, easy to set up and adaptable to a variety of homes. Since 72% of European consumers will have Smart Meters by 2020, it provides an omnipresent basis for Activity Recognition.
This thesis investigates, how a Smart Meter’s limited recognition of appliance involving activities can be extended by Vibration Sensors. We provide an experimental setup to aggregate a dedicated dataset with a sampling frequency of 25,600 Hz. We evaluate the impact of combining a Smart Meter and Vibration Sensors on a system’s accuracy, by means of four developed Activity Recognition Systems. This results in the quantification of the impact. We found out that through combining these sensors, the accuracy of an Activity Recognition System rather strives towards the highest accuracy of a single underlying sensor, than jointly surpassing it. | |