Vol.:(0123456789) 1 3 Journal of Ambient Intelligence and Humanized Computing https://doi.org/10.1007/s12652-020-02351-x ORIGINAL RESEARCH Human activity recognition based on smartphone using fast feature dimensionality reduction technique B. A. Mohammed Hashim 1  · R. Amutha 2 Received: 7 January 2020 / Accepted: 17 July 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020 Abstract Human activity recognition aims to identify the activities carried out by a person. Recognition is possible by using informa- tion that is retrieved from numerous physiological signals by attaching sensors to the subject’s body. Lately, sensors like accelerometer and gyroscope are built-in inside the Smartphone itself, which makes activity recognition very simple. To make the activity recognition system work properly in smartphone which has power constraint, it is very essential to use an optimization technique which can reduce the number of features used in the dataset with less time consumption. In this paper, we have proposed a dimensionality reduction technique called fast feature dimensionality reduction technique (FFDRT). A dataset (UCI HAR repository) available in the public domain is used in this work. Results from this study shows that the fast feature dimensionality reduction technique applied for the dataset has reduced the number of features from 561 to 66, while maintaining the activity recognition accuracy at 98.72% using random forest classifer and time consumption in dimensionality reduction stage using FFDRT is much below the state of the art techniques. Keywords Activity recognition · Smartphone · Accelerometer · FFDRT · Random forest 1 Introduction Since 1979, the year in which the frst ever commercial handheld mobile phones were introduced, there has been no stopping in the growth of it. The mobile phones were used by 80% of the world population by the year 2011. In a very short period of time, mobile phones have become part of our life. The new generation smartphones are equipped with many features with the help of many sensors. With the use of these features in the smartphones, it is very easy to keep track of our daily activities. These kinds of assis- tive technologies can be useful for remote health care, for the disabled, the elderly and to those with important needs. Human activity recognition is a very active research feld in which techniques for understanding human activities by interpreting attributes taken from motion, physiological sig- nals, location and environmental information etc., Human activity recognition identifes the actions done by a person given a set of observations. The recognition can be done by using the information taken from sensors. The main objective of activity recognition system is to iden- tify the actions performed by humans, from the collected data through sensors. The latest smartphones have motion, acceleration or inertial sensors, and by using the information taken from the sensors, recognition of activities is possible. Since it is possible to collect a large amount of data through the features available in smartphones but the challenge lies in processing the data and implementing it in real time appli- cations. So, there is an important need for new data mining schemes to end these kinds of challenges. Since we have many features derived from sensors like accelerometer and gyroscope, it is essential to use feature selection algorithms to simplify the computing process by reducing the number of features. mRMR feature selection algorithm has been used in a study to reduce the dimensionality of features (Doewes 2017). Accordingly, in this paper fast feature dimensionality reduction technique has been employed for dimensionality * B. A. Mohammed Hashim hashimba.ece@cahcet.edu.in R. Amutha amuthar@ssn.edu.in 1 Department of Electronics and Communication Engineering, C. Abdul Hakeem College of Engineering and Technology, Hakeem Nagar, Melvisharam, Ranipet, Tamil Nadu, India 2 Department of Electronics and Communication Engineering, SSN College of Engineering, Kalavakkam, Chennai, Tamil Nadu, India