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Published: 01 December 2016 Publication History
Abstract
Smart, interactive healthcare is necessary in the modern age. Several issues, such as accurate diagnosis, low-cost modeling, low-complexity design, seamless transmission, and sufficient storage, should be addressed while developing a complete healthcare framework. In this paper, we propose a patient state recognition system for the healthcare framework. We design the system in such a way that it provides good recognition accuracy, provides low-cost modeling, and is scalable. The system takes two main types of input, video and audio, which are captured in a multi-sensory environment. Speech and video input are processed separately during feature extraction and modeling; these two input modalities are merged at score level, where the scores are obtained from the models of different patients' states. For the experiments, 100 people were recruited to mimic a patient's states of normal, pain, and tensed. The experimental results show that the proposed system can achieve an average 98.2 % recognition accuracy.
References
[1]
Park, C., et al., M2M-based smart health service for human UI/UX using motion recognition. Cluster Comput. 18:221---232, 2015.
[2]
Chen, M., Ma, Y., Song, J., Lai, C., Hu, B., Smart clothing: connecting human with clouds and big data for sustainable health monitoring. ACM/Springer Mobile Netw. Appl., 2016.
[3]
Solanas, et al., Smart health: A context aware health paradigm within smart cities. IEEE Commun. Mag. 52(8):74---81, 2014.
[4]
Grunerbl, A., et al., Smartphone based recognition of States and state changes in bipolar disorder patients. IEEE J. Biomed. Health Inform. 19(1):140---148, 2015.
[5]
Hossain, M. S.: Patient status monitoring for smart home health-care. In: IEEE ICME 2016, Seattle (2016)
[6]
Muhammad, G., Automatic speech recognition using interlaced derivative pattern for cloud based healthcare system. Clust. Comput. 18(2):795---802, 2015.
[7]
Ma, Y., et al., Robot and cloud-assisted multi-modal healthcare system. Clust. Comput. 18(3):1295---1306, 2015.
[8]
Hassanalieragh, M., et al.: Health monitoring and management using internet-of-things (IoT) sensing with cloud-based processing: opportunities and challenges. In: IEEE International Conference on Services Computing, pp. 285---292 (2015)
[9]
Jara, J., Zamora-Izquierdo, M. A., Skarmeta, A. F., Interconnection framework for mHealth and remote monitoring based on the internet of things. IEEE J. Sel. Areas Commun. 31(9):47---65, 2013.
[10]
Hossain, M. S., and Muhammad, G., Cloud-assisted industrial internet of things (IIoT)-enabled framework for health monitoring. Comput. Netw. 101(2016):192---202, 2016.
[11]
Henze, M., Hermerschmidt, L., Kerpen, D., Häußling, R., Rumpe, B., Wehrle, K., A comprehensive approach to privacy in the cloud-based internet of things. Futur. Gener. Comput. Syst. 56(2016):701---718, 2016.
[12]
Hossain, M. S., Cloud-supported cyber---physical localization framework for patients monitoring. IEEE Syst. J., 1---10, 2015.
[13]
Rabiner, L., and Juang, B. H., Fundamentals of Speech Recognition. Englewood Cliffs: Prentice-Hall, 1993.
[14]
Campbell, J. P., Speaker recognition: a tutorial. Proc. IEEE 85(9):1437---1462, 1997.
[15]
Muhammad, G., et al.: Environment recognition using selected MPEG-7 audio features and mel-frequency cepstral coefficients. In: International Conference on Digital Telecommunications (ICDT10), pp. 11---16, Greece (2010)
[16]
Godino-Llorente, J. I., Gomes-Vilda, P., Blanco-Velasco, M., Dimensionality reduction of a pathological voice quality assessment system based on Gaussian mixture models and short-term cepstral parameters. IEEE Trans. Biomed. Eng. 53(10):1943---1953, 2006.
[17]
Muhammad, G., et al., Multi directional regression (MDR) based features for automatic voice disorder detection. J. Voice. 26(6):817.e19---817.e27, 2012.
[18]
Muhammad, G., et al., Spectro-temporal directional derivative based automatic speech recognition for a serious game scenario. Multimedia Tools Appl. 74(14):5313---5327, 2015.
[19]
Viola, P., and Jones, M. J., Robust real-time face detection. Int. J. Comput. Vis. 57(2):137---154, 2004.
[20]
Ahonen, T., Hadid, A., Pietikäinen, M., Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(12):2037---2041, 2006.
[21]
Chen, J., et al., WLD: A robust local image descriptor. IEEE Trans. Pattern Anal. Mach. Intell. 32(9): 1705---1720, 2010.
[22]
Tan, X., and Triggs, B., Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans. Image Process. 19(6):1635---1650, 2010.
[23]
Shi, X., et al., Towards interactive medical content delivery between simulated body sensor networks and practical data center. J. Med. Syst. 40:214, 2016.
[24]
Bishop, C., Pattern Recognition and Machine Learning. New York: Springer, 2006. ISBN 978-0-387-31073-2.
[25]
González-Valenzuela, S., Chen, M., Leung, V. C. M., Mobility support for health monitoring at home using wearable sensors. IEEE Trans. Inf. Technol. Biomed. 15(4):539---549, 2011.
[26]
Chen, M., Ma, Y., Wang, J., Dung, O. M., Song, E., Enabling comfortable sports therapy for patient: a novel lightweight durable and portable ECG monitoring system. Healthcom, 271---273, 2013.
[27]
Li, Y., Dai, W., Ming, Z., Qiu, M., Privacy protection for preventing data over collection in smart city. IEEE Trans. Comput. 610 65(5):1339---1350, 2016.
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