This paper proposes a light-weight online classification method to detect smarthpone user's postural actions, such as sitting, standing, walking, and running. These actions are named as “user states” since they are inferred after the analysis of data acquired from the smartphones equipped accelerometer sensors. To differentiate one user state from another, many studies can be found in the literature. However, this study differs from all others by offering a computational lightweight and online classification method without knowing any priori information. Moreover, the proposed method not only provides a standalone solution in differentiation of user states, but also it assists other widely used offline supervised classification methods by automatically generating training data classes and/or input system matrices. Furthermore, we improve these existing methods for the purpose of online processing by reducing the required computational burden. Extensive experimental results show that the proposed method makes a solid differentiation in user states even when the sensor is being operated under slower sampling frequencies.

This paper presents a novel framework that includes an inhomogeneous (time-variant) Hidden Markov Model (HMM) and learning from data concepts. The framework either recognizes or estimates user contextual inferences called `user states' within the concept of Human Activity Recognition (HAR) for future context-aware applications. Context-aware applications require continuous data acquisition and interpretation from one or more sensor reading(s). Therefore, device battery lifetimes need to be extended due to the fact that constantly running built-in sensors deplete device batteries rapidly. In this sense, a framework is constructed to fulfill requirements needed by applications and to prolong device battery lifetimes. The ultimate goal of this paper is to present an accurate user state representation model, and to maximize power efficiency while the model operates. Most importantly, this research intends to create and clarify a generic framework to guide the development of future context-aware applications. Moreover, topics such as user profile adaptability and variant sensory sampling operations are examined. The proposed framework is validated by simulations and implemented in a HAR-based application by the smartphone accelerometer. According to the results, the proposed framework shows an increase in power efficiency of 60% for an accuracy range from 75% up to 96%, depending on user profiles.

Mobile device based sensing provides a vast variety of user contextual information. To continuously acquire, classify and reason any context through built-in sensors cause mobile device batteries to deplete rapidly. Therefore, an urgent need has arisen to apply power saving approaches at the low level sensor operations while inferring user relevant context. In this paper, an adaptive sensing strategy is proposed to fill the mentioned deficiency within the realm of Human Activity Recognition (HAR). The strategy aims at inferring any user relevant postural activities through smartphone accelerometer by providing two intuitive power saving methods, which assign adaptive pairs of sampling frequencies and duty cycles during sensor operations. The proposed methods show from 20% up to 50% efficiency enhancement in power consumption caused by the accelerometer with respect to overall 15% accuracy decrease in context inference depending on the degree of user activity dynamics.