Umer Fareed
ABSTRACT
With recent advancements in the tele-monitoring and ambient assisted living technology, human activity recognition (HAR) has proven enormously important in elderly healthcare. With the rapid increase in the use of smartphones embedded with a wide variety of latest locomotion sensors in our daily life, a new role for smartphones as the performance evaluator for physical activity recognition has emerged. HAR by using the fusion of smartphone sensors data is comparatively a new area for exploration. In this paper, we have evaluated different classification algorithms for recognition of eight physical activities performed by individuals using the smartphone tri-axial accelerometer, gyroscope and magnetometer sensors. Our analyses of collected data indicate that sensor combination improves the overall performance of the classifiers to the maximum compared to their individual performances especially for walking upstairs and downstairs activities. Moreover, we propose the use of sensor fusion for activity monitoring and diagnostic suitable for heart failure patients.
- Adil, M. K.; Muhammed, H. S.; and Seok, W. L. Exploratory data analysis of acceleration signals to select light-weight and accuratefeatures for real-time activity recognition on smartphones. Sensors, vol. 13, no. 10, pp. 13099--13122, 2013.Google Scholar
Cross Ref
- Alvina, A.; and Muhammad, U. I. Activity recognition using smartphone sensors. In Proceedings of the 2013 IEEE Consumer Communications and Networking Conference (CCNC), Las Vegas, NA, USA, 11-14 January 2013; pp. 914--919.Google Scholar
- Amarnag, S.; Alvin, R.; Jeff, B.; Dieter, F. Recognizing activities and spatial context using wearable sensors. In Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, 2006.Google Scholar
- Corin, K. S. Activity recognition and step detection with smartphones: Towards terminal based indoor positioning system. In Proceedings of the 2012 IEEE 23rd International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC), Sydney, Australia, 9-12 September 2012; pp. 2454--2459.Google Scholar
- David, A.; Alessandro, G.; Luca, O.; Xavier, P.; and Jorge, L. R. Human Activity Recognition on Smartphones using a Multiclass Hardware-Friendly Support Vector Machine. In International Workshop of Ambient Assisted Living (IWAAL), 2012. Google ScholarDigital Library
- Dean, M. K.; Michael, R. N.; Merryn, M.; Nigel, H. L.; and Branko, G. C. Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring. Info. Tech. in Biomedicine, IEEE Trans. on, vol. 10, no. 1, pp. 156--167, 2006. Google ScholarDigital Library
- Emmanuel, M. T.; Stephen, S. I.; William, H.; Kent, L.; Julie, W.; Abby, K..; and Robert, F. Real-Time recognition of physical activities and their intensities using wireless accelerometers and a heart rate monitor. In Proceedings of the 2007 11th IEEE International Symposium on Wearable Computers, pp.37--40, 2007. Google ScholarDigital Library
- Figo, D.; Diniz, P.C.; Ferreira, D.R.; Cardoso, J.M.P. Preprocessing techniques for context recognition from accelerometer data. Pers. Ubiquitous Comput. 2010, 14, 645--662. Google ScholarDigital Library
- Frank, J.; Mannor, S.; Precup, D. Activity Recognition with Mobile Phones. Lect. Notes Comput. Sci. 2011, 6913, 630--633. Google ScholarDigital Library
- Garner, S. R. WEKA: The Waikato Environment for Knowledge Analysis. In Proceedings of the New Zealand Computer Science Research Students Conference, Hamilton, New Zealand, 18-21 April 1995; p. 5764.Google Scholar
- Jun, Y. Toward Physical Activity Diary : Motion Recognition Using Simple Acceleration Features with Mobile Phones. Data Processing, pp. 1--9, 2009.Google Scholar
- Klasnja, P.; Pratt, W. Healthcare in the pocket: Mapping the space of mobile-phone health interventions. J. Biomed. Inf. 2012, 45, 184--198. Google ScholarDigital Library
- Lane, N.; Mohammod, M.; Lin, M.; Yang, X.; Lu, H.; Ali, S.; Doryab, A.; Berke, E.; Choudhury, T.; Campbell, A. BeWell: A Smartphone Application to Monitor, Model and Promote Wellbeing. In Proceedings of 5th International ICST Conference on Pervasive Computing Technologies for Healthcare, Dublin, Ireland, 23-26 May 2011.Google ScholarCross Ref
- Lau, S.L.; Konig, I.; David, K.; Parandian, B.; Carius-Dussel, C.; Schultz, M. Supporting patient monitoring using activity recognition with a smartphone. In Proceedings of the 2010 7th International Symposium on Wireless Communication Systems (ISWCS), York, UK, 19-22 September 2010; pp. 810--814Google ScholarCross Ref
- Lin, S.; Daqing, Z.; and Nan, L. Physical Activity Monitoring with Mobile Phones. Lecture Notes in Computer Science, Volume 6719, 2011, pp 104--111 Google ScholarDigital Library
- Ling, B.; and Stephen, S. I.; "Activity recognition from user-annotated acceleration data," Pervasive Computing, Lecture Notes in Computer Science, vol. 3001, pp. 1--17, 2004.Google ScholarCross Ref
- Martin, H.; Bernardos, A.M.; Iglesias, J.; Casar, J.R. Activity logging using lightweight classification techniques in mobile devices. Pers. Ubiquitous Comput. 2013, 17, 675--695. Google ScholarDigital Library
- Muhammad, S.; Stephan, B.; Ozlem, D. I.; Hans, S.; and Paul, J. M. H. Fusion of Smartphone Motion Sensors for Physical Activity Recognition. Sensors, 14, 10146--10176.Google Scholar
- Nishkam, R.; Nikhil, D.; Preetham, M.; Michael, L. Activity Recognition from Accelerometer Data. Proceedings of the Seventeenth Conference on Innovative Applications of Artificial Intelligence (IAAI/AAAI 2005). Google ScholarDigital Library
- Paul, L.; Jamie, A. W.; Holger, J.; Mathias, S.; Gerhard, T.; Amin A.; and Thad, S. Recognizing workshop activity using body worn microphones and accelerometers. International Conference Pervasive Computing, pp 18--22, 2004.Google Scholar
- Seon-Woo, L.; and Kenji M. Activity and location recognition using wearable sensors. In IEEE Pervasive Computing, 1(3):24--32, 2002. Google ScholarDigital Library
- Sian, L. L.; Klaus, D. Movement Recognition using the Accelerometer in Smartphones. Communication, pp. 1--9, 2010.Google Scholar
- Sian, L. L.; Klaus, D. Movement recognition using the accelerometer in smartphones. In Proceedings of Future Network & MobileSummit 2010, Florence, Italy, June 2010Google Scholar
- Simon, K.; Hristijan, G.; Matjaž, G.; Mitja, L. Efficient activity recognition and fall detection using accelerometers, in Evaluating AAL Systems Through Competitive Benchmarking. Springer, 2013, pp. 13--23.Google Scholar
- Wanmin, W.; Sanjoy, D.; Ernesto, E. R.; Carlyn, P.; Gregory, J. N. Classification accuracies of physical activities using smartphone motion sensors. J. Med. Internet Res. 2012, 14, e130Google Scholar
- Yunus, E. U.; Ozlem, D. I.; Cem, E. User, Device and Orientation Independent Human Activity Recognition on Mobile Phones: Challenges and a Proposal. In Proceedings of the 2013 ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication (UbiComp '13 Adjunct), New York, NY, USA, 8-12 September 2013; pp. 1427--1436. Google ScholarDigital Library
- Worldwide Mobile Phone Users: H1 2014 Forecast and Comparative Estimates. http://www.emarketer.com/Article/Smartphone-Users-Worldwide-Will-Total-175-Billion-2014/1010536Google Scholar
Index Terms
- Smartphone Sensor Fusion based Activity Recognition System for Elderly Healthcare
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