Robust human activity recognition using lesser number of wearable sensors

In recent years, research on the recognition of human physical activities solely using wearable sensors has received more and more attention. Compared to other types of sensory devices such as surveillance cameras, wearable sensors are preferred in most activity recognition applications mainly due to their non-intrusiveness and pervasiveness. However, many existing activity recognition applications or experiments using wearable sensors were conducted in the confined laboratory settings using specifically developed gadgets. These gadgets may be useful for a small group of people in certain specific scenarios, but probably will not gain their popularity because they introduce additional costs and they are unusual in everyday life. Alternatively, commercial devices such as smart phones and smart watches can be better utilized for robust activity recognitions. However, only few prior studies focused on activity recognitions using multiple commercial devices. In this paper, we present our feature extraction strategy and compare the performance of our feature set against other feature sets using the same classifiers. We conduct various experiments on a subset of a public dataset named PAMAP2. Specifically, we only select two sensors out of the thirteen used in PAMAP2. Experimental results show that our feature extraction strategy performs better than the others. This paper provides the necessary foundation towards robust activity recognition using only the commercial wearable devices.NRF (Natl Research Foundation, S’pore)Accepted versio

Human activity recognition through wearable devices

Human activity recognition technology has received increasing attention from researchers in the recent years. This technology has a wide range of possible applications such as healthcare, security, sports, etc. As such, many researchers have developed activity recognition systems using various types of devices such as specially made sensors, video surveillance, and smartphones for data collection. With the recent introduction of smartwatch, some researchers have started collecting sensory data using both smartphones and smartwatches due to their non-intrusive nature and high penetration rate. In this project, a robust human activity recognition framework which can detect not only basic activities (sitting, standing, running, etc.) but also hand-based activities (writing, typing, reading, etc.) is proposed. The new framework is named the Robust Activity Recognition using Smartphone and Smartwatch (RARSS). This new framework will be extensively tested using datasets collected from 15 subjects. There are two testing techniques employed, namely 5-fold cross validation and Leave-One-Person-Out (LOPO) testing. Furthermore, extracted features in RARSS’ will be compared with two sets of features proposed by other researchers. There are five main findings presented in this report as follows: (i) Sensory data collected from the smartwatch provides additional information for better distinguishing hand-based activities. (ii) Combining sensory data from both smartphone and smartwatch improves the models’ performance. (iii) Sensory data collected from barometer and gyroscope sensor provides additional useful information for differentiating certain activities. (iv) RARSS’ features are empirically shown to be better than the other two benchmarking feature sets. (v) Most importantly, a technique called data mean deduction, which is applied on the collected sensory data, can significantly reduce the differences embedded in the sensory data collected for the same activity but from different subjects. It is shown that using the mean deducted data together with the original sensory data can significantly improve the performance of the activity recognition models. A real-time human activity recognition system is also developed in this project. The system is built based on the RARSS framework so that it can accurately predict human activities in real- time. The system has a web application for displaying the latest predicted activity with an activity history list. The web application also employs a smoothing function on the activity history list to cater for activity transitions.Bachelor of Engineering (Computer Science