Artificial Intelligence Prediction of Human Activity Recognition

Artificial Intelligence Prediction of Human Activity Recognition

Archit Jain¹, Aayush Ranjan², Mr. Ajay Kaushik³

¹Student, Dept of Information Technology, Maharaja Agrasen Institute of Technology, Delhi, India

²Student, Dept of Information Technology, Maharaja Agrasen Institute of Technology, Delhi, India

³Assistant Professor, Dept of Information Technology, Maharaja Agrasen Institute of Technology, Delhi, India

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Abstract - Wearable computing is becoming more and more incorporated into our daily lives. Wearable gadgets have recently attracted a lot of attention and widespread acceptance as a result of their compact size and decent processing power capabilities. These wearable gadgets with sensors (e.g. accelerometer, gyroscope, etc.) are excellent choices for tracking users' daily activities (e.g. walking, jogging, sleeping, and so on). Human Activity Recognition (HAR) has the potential to aid in the development of assistive technologies for the elderly, chronically ill, and those with special needs. Activity recognition can be used to offer information on patients' daily activities in order to aid the development of e-health systems such as Ambient Assisted Living (AAL). Despite the fact that human activity detection has been an active field for the development of context-aware systems for more than a decade, there are still critical issues that, if addressed, would represent a dramatic shift in how people interact with smartphones. A broad architecture of the essential components of any HAR system is described, as well as a data-gathering architecture for HAR systems. Machine learning techniques and technologies were used by HAR systems to generate patterns to characterize, evaluate, and predict data. Because a human activity recognition system should return a label such as walking, sitting, running, sleeping, falling, and so on, most HAR systems are supervised. The goal of this research is to use multiple machine learning methods on the UCI Human Activity Recognition dataset.

Key Words:  Human Activity Recognition, Artificial Intelligence, Wearable gadgets, HAR, Dense Layer