Human Activity Recognition on Smartphones Using a Multiclass Hardware-Friendly Support Vector Machine

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\u2019s 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

Lineking: Crowdsourced line wait-time estimation using smartphones

Abstract. This paper describes the design, implementation and deployment of LineKing (LK), a crowdsourced line wait-time monitoring service. LK consists of a smartphone component (that provides automatic, energy-efficient, and accurate wait-time detection), and a cloud backend (that uses the collected data to provide accurate wait-time estimation). LK is used on a daily basis by hundreds of users to monitor the wait-times of a coffee shop in our university campus. The novel wait-time estimation algorithms deployed at the cloud backend provide mean absolute errors of less than 2-3 minutes

Goal-Oriented Opportunistic Sensor Clouds

Activity- and context-aware systems, as they are known, established, and well evaluated in small-scale laboratory settings for years and decades, suffer from the fact, that they are limited concerning the underlying data delivering entities. The sensor systems are usually attached on the body, on objects, or in the environment, directly surrounding persons or groups whose activities or contextual information has to be detected. For sensors that are exploited in this kind of systems, it is essential that their modalities, positions and technical details are initially defined to ensure a stable and accurate system execution. In contrast to that, opportunistic sensing allows for selecting and utilizing sensors, as they happen to be accessible according to their spontaneous availability, without presumably defining the input modalities, on a goal-oriented principle. One major benefit thereby is the capability of utilizing sensors of different kinds and modalities, even immaterial sources of information like webservices, by abstracting low-level access details. This emerges the need to roll out the data federating entity as decentralized collecting point. Cloud-based technologies enable space- and time-free utilization of a vast amount of heterogeneous sensor devices reaching from simple physical devices (e.g., GPS, accelerometers, as they are conventionally included on today’s smart phones) to social media sensors, like Facebook, Twitter, or LinkedIn. This paper presents an opportunistic, cloud-based approach for large-scale activity- and context-recognition