Left Behind Occupant Recognition Based on Human Tremor Detection via Accelerometers Mounted at the Car Body
Abstract
The aim is an additional sensor system that is able to detect left behind occupants in parked cars. This should avoid the decrease of fatalities found in unattended or oversight individuals in vehicles. Based on acceleration measurements directly at the car chassis information about the occupancy is extracted. At the beginning of this paper the theory of the signal source, the used car model and the applied classification algorithms is shortly given. Afterwards several measurement results are presented and become analyzed with regard to a following automatic classification. The next step is the evaluation of different classification algorithms and the explanation of the performance on the acceleration datasets that were collected during this research. Many different classification algorithms are available, at this point the support vector machine (SVM), k-nearestneighbor (k-NN), probabilistic neural network (PNN), decision tree and clustering were observed.
Keywords
Support Vector Machine Essential Tremor Probabilistic Neural Network Acceleration Measurement Busy RoadPreview
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References
- [1]NHTSA, “Data Collection Study: Deaths and Injuries resulting from certain Non-Traffic and Non-Crash events”, 2004.Google Scholar
- [2]Engin, M., et al., “The classification of human tremor signals using artificial neural networks”, Expert Systems with Applications, Vol. 33, 2007.Google Scholar
- [3]Lyons, Kelly E., “Handbook of essential tremor and other tremor disorders”, Marcel Dekker Inc., 2005.Google Scholar
- [4]Albani, M., “Numerische Optimierung, Aufbau und Test eines Sensorlayouts zur Sitzbelegungserkennung mit Hilfe von unterschiedlichen Algorithmen unter Verwendung von verschiedenen Optimierungskriterien”, Faculty of Electrical, Information and Media Engineering, University of Wuppertal, Germany, 2005.Google Scholar
- [5]Mitschke, M., “Dynamik der Kraftfahrzeuge – Band B Schwingungen”, 2.Edition, 1984.Google Scholar
- [6]Spickenreuther, M., “Funktionsmodell der Karosserie zur Auslegung des Schwingungskomforts im Gesamtfahrzeug”, Dissertation, Lehrstuhl fuer Fahrzeugtechnik der Technischen Universitaet Muenchen, 2006.Google Scholar
- [7]Chamseddine, A., Noura, H., Ouladsine, M., “Sensor location for Actuator Fault Diagnosis in Vehicle Active Suspension”, 17th IEEE International Conference on Control Applications, San Antonia, Texas, USA, September 2008.Google Scholar
- [8]Draghici, B., “Development of a model for the identification of the occupants in automobiles relying on low-g acceleration measurements”, Faculty of Electrical, Information and Media Engineering, University of Wuppertal, Germany, 2008.Google Scholar
- [9]Strachan, S., Murray-Smith, R., “Muscle Tremor as an Input Mechanism”, UIST 2004, Santa Fe, 2004.Google Scholar
- [10]Bahadir, C., “Evaluation of different classification algorithms for occupancy detection systems in vehicles with low-g based acceleration measurements”, Faculty of Electrical, Information and Media Engineering, University of Wuppertal, Germany, 2008.Google Scholar
- [11]Jakubowski, J., et al., “Higher Order Statistics and Neural Network for Tremor Recognition”, IEEE Transactions on biomedical Engineering, Vol. 49, No. 2, February 2002.Google Scholar
- [12]Ye, N., et al., “The Handbook of Data Mining”, Lawrence Erlbaum Associates Inc., 2003.Google Scholar
- [13]Fischer, C., Fischer, T., Tibken, B., “Sensierung von zurückgelassenen Personen in geparkten Fahrzeugen mittels Beschleunigungsmessungen direkt an der Fahrzeugkarosserie”, 3.Tagung Sensoren im Automobil, 2009.Google Scholar