Ambient Assisted Living: A Research on Human Activity Recognition and Vital Health Sign Monitoring using Deep Learning Approaches
Manoj T1, Thyagaraju G S2

1Manoj T, Department of Computer Science Engineering, Shri Madhwa Vadiraja Institute of Technology and Management, Bantakal, Udupi, Karnataka, India.

2Dr. Thyagaraju G S, Department of Computer Science Engineering, SDM Institute of Technology, Ujire, Dakshina Kannada, Karnataka, India.

Manuscript received on 08 April 2019 | Revised Manuscript received on 15 April 2019 | Manuscript Published on 26 July 2019 | PP: 531-540 | Volume-8 Issue-6S4 April 2019 | Retrieval Number: F11110486S419/19©BEIESP | DOI: 10.35940/ijitee.F1111.0486S419

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Abstract: The rise in life expectancy rate and dwindled birth rate in new age society has led to the phenomenon of population ageing which is being witnessed across the world from past few decades. India is also a part of this demographic transition which will have the direct impact on the societal and economic conditions of the country. In order to effectively deal with the prevailing phenomenon, stakeholders involved are coming up with the Information and Communication Technology (ICT) based ecosystem to address the needs of elderly people such as independent living, activity recognition, vital health sign monitoring, prevention from social isolation etc. Ambient Assisted Living (AAL) is one such ecosystem which is capable of providing safe and secured living environment for the elderly and disabled people. In this paper we will focus on reviewing the sensor based Human Activity Recognition (HAR) and Vital Health Sign Monitoring (VHSM) which is applicable for AAL environments. At first we generally describe the AAL environment. Next we present brief insights into sensor modalities and different deep learning architectures. Later, we survey the existing literature for HAR and VHSM based on sensor modality and deep learning approach used.

Keywords: Ambient Assisted Living; Deep Learning; Human Activity Recognition; Vital Health Sign Monitoring.
Scope of the Article: Computer Science and Its Applications