Abstract
Abstract. Human activity recognitions have been widely used nowadays by end users thanks to extensive usage of smartphones. Smartphones, by self-containing low-cost sensing technology, can track our daily activities for serving healthcare, sport, interactive AR/VR games and so on. However, smartphone technology is evolving and the techniques of using the data that smartphones go through are also improving. In this study, we used built-in sensing technologies (accelerometer and gyroscope) available in nearly every smartphone to detect the most common 5 daily activities of human by taking the data of these sensors and extract the features for a Convolutional Neural Network (CNN) model. We prepare a dataset and use TensorFlow to train the collected data from the sensors then filtered it to be processed. We also discuss the differences in CNN model accuracy with different optimizers. To demonstrate the model, we developed an android application that successfully predict an activity. We believe that after improving this application, it can be used for especially lonely old people to immediately warn authorities in case of any daily incidents.
Highlights
Smartphones have been running rapidly in parallel with our life, all smartphones have some self-contained sensors, some of these sensors can record large amounts of data, many smartphone applications use this data for multiple purposes such as counting the number of steps, measuring the heart rate, heartbeat, or they can be used in the fitness field and so on
We have utilized two built-in sensors (3 axis acceleration and 3 axis gyro sensor data) available inside nearly in every smartphone to detect the daily activities of human by taking the data of these sensors and extract the features using Convolutional Neural Network (CNN) to recognize human activities
We show how to collect data from embedded sensors in smartphones how to filter the data to be processed and explain the strategy which we used to shape the dataset before and after processing it with TensorFlow and Keras libraries
Summary
Smartphones have been running rapidly in parallel with our life, all smartphones have some self-contained sensors, some of these sensors can record large amounts of data, many smartphone applications use this data for multiple purposes such as counting the number of steps, measuring the heart rate, heartbeat, or they can be used in the fitness field and so on. There exists plenty of feature extraction techniques classifying sensory data such as ANN, KNN and SVM (Ronao, C.A et al, 2014, Seera, M. et al, 2014, Eastwood, M et al, 2014 ). Unlike these conventional approaches, deep neural network learns features directly from the input data without requiring manual feature extraction operation. We show how to collect data from embedded sensors in smartphones how to filter the data to be processed and explain the strategy which we used to shape the dataset before and after processing it with TensorFlow and Keras libraries. Our implemented system works efficiently in selfcontained smartphones and provides adequate activity recognition (95.83%)
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