Lakhasly

Activity-Based Computing [1] aims to capture the state of the user and its environment by exploiting heterogeneous sensors in order to provide adaptation to exogenous computing resources. When these sensors are attached to the subject’s body, they permit continuous monitoring of numerous physiological signals. This has appealing use in healthcare applications, e.g. the exploitation of Ambient Intelligence (AmI) in daily activity monitoring for elderly people. In this paper, we present a system for human physical Activity Recognition (AR) using smartphone inertial sensors. As these mobile phones are limited in terms of energy and computing power, we propose a novel hardware-friendly approach for multiclass classification. This method adapts the standard Support Vector Machine (SVM) and exploits fixed-point arithmetic for computational cost reduction. A comparison with the traditional SVM shows a significant improvement in terms of computational costs while maintaining similar accuracy, which can contribute to develop more sustainable systems for AmI.Since the appearance of the first commercial hand-held mobile phones in 1979, it has been observed an accelerated growth in the mobile phone market which has reached by 2011 near 80% of the world population [2]. This shows that in a very short time, mobile devices will become easily accessible to virtually everybody. Smartphones, which are a new generation of mobile phones, are now offering many other features such as multitasking and the deployment of a variety of sensors, in addition to the basic telephony. Current efforts attempt to incorporate all these features while maintaining similar battery lifespans and device dimensions. The integration of these mobile devices in our daily life is rapidly growing. It is envisioned that such devices will seamlessly keep track of our activities, learn from them, and subsequently help us to make better decisions regarding our future actions [3]. This is one key concepts in which AmI relies on. In this paper, we employ smartphones for human Activity Recognition with potential applications in assisted living technologies. We take into account current hardware limitations and propose a new alternative for AR that requires less computational resources to operate. AR aims to identify the actions carried out by a person given a set of observations of itself and the surrounding environment. Recognition can be accomplished, for example, by exploiting the information retrieved from inertial sensors such as accelerometers [4]. In some smartphones these sensors are embedded by default and we benefit from this to classify a set of physical activities (standing, walking, laying, walking, walking upstairs and walking downstairs) by processing inertial body signals through a supervised Machine Learning (ML) algorithm for hardware with limited resources. This paper is structured in the following way: The state of the art regarding previous work is depicted in Section 2. The description of the adopted methodology is presented in Section 3. There, the experimental set up for capturing the data and the mathematical description for the proposed Multiclass Hardware Friendly Support Vector Machine (MC-HF-SVM) are explained. Experimental results and conclusions of this research are described in Sections 4 and 5.The SVM algorithm was originally proposed only for binary classification problems but it has been adapted using different schemes for multiclass problems such as in [9]. In particular, we have chosen the One-Vs-All (OVA) method as its accuracy is comparable to other classification methods as demonstrated by Rifkin and Klautau in [14], and because its learned model uses less memory when compared for instance to the One-Vs-One (OVO) method. This is advantageous when used in limited resources hardware devices.In this paper, we proposed a new method for building a multiclass SVM using integer parameters. The MC-HF-SVM is an appealing approach for use in AmI systems for healthcare applications such as activity monitoring on smartphones. This alternative that employs fixed-point calculations, can be used for AR because it requires less memory, processor time and power consumption. Moreover, it provides accuracy levels comparable to traditional approaches such as the MC-SVM that uses floating-point arithmetic. The experimental results confirm that even with a reduction of bits equal to 6 for representing the learned MC-HF-SVM model parameter β, it is possible to substitute the standard MC-SVM. This outcome brings positive implications for smartphones because it could help to release system resources and reduce energy consumption. Future work will present a publicly available AR dataset to allow other researchers to test and compare different learning models.