Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine

Activity-Based Computing 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.Peer ReviewedPostprint (author's final draft

An initial performance review of software components for a heterogeneous computing platform

The design of embedded systems is a complex activity that involves a lot of decisions. With high performance demands of present day usage scenarios and software, they often involve energy hungry state-of-the-art computing units. While focusing on power consumption of computing units, the physical properties of software are often ignored. Recently, there has been a growing interest to quantify and model the physical footprint of software (e.g. consumed power, generated heat, execution time, etc.), and a component based approach facilitates methods for describing such properties. Based on these, software architects can make energy-efficient software design solutions. This paper presents power consumption and execution time profiling of a component software that can be allocated on heterogeneous computing units (CPU, GPU, FPGA) of a tracked robot

Activity-based computing: computational management of activities reflecting human intention

An important research topic in artificial intelligence is automatic sensing and inferencing of contextual information, which is used to build computer models of the user’s activity. One approach to build such activity-aware systems is the notion of activity-based computing (ABC). ABC is a computing paradigm that has been applied in personal information management applications as well as in ubiquitous, multidevice, and interactive surface computing. ABC has emerged as a response to the traditional application- and file-centered computing paradigm, which is oblivious to a notion of a user’s activity context spanning heterogeneous devices, multiple applications, services, and information sources. In this article, we present ABC as an approach to contextualize information, and present our research into designing activity-centric computing technologies

First observational application of a connectivity--based helicity flux density

Measuring the magnetic helicity distribution in the solar corona can help in understanding the trigger of solar eruptive events because magnetic helicity is believed to play a key role in solar activity due to its conservation property. A new method for computing the photospheric distribution of the helicity flux was recently developed. This method takes into account the magnetic field connectivity whereas previous methods were based on photospheric signatures only. This novel method maps the true injection of magnetic helicity in active regions. We applied this method for the first time to an observed active region, NOAA 11158, which was the source of intense flaring activity. We used high-resolution vector magnetograms from the SDO/HMI instrument to compute the photospheric flux transport velocities and to perform a nonlinear force-free magnetic field extrapolation. We determined and compared the magnetic helicity flux distribution using a purely photospheric as well as a connectivity-based method. While the new connectivity-based method confirms the mixed pattern of the helicity flux in NOAA 11158, it also reveals a different, and more correct, distribution of the helicity injection. This distribution can be important for explaining the likelihood of an eruption from the active region. The connectivity-based approach is a robust method for computing the magnetic helicity flux, which can be used to study the link between magnetic helicity and eruptivity of observed active regions.Comment: 4 pages, 3 figures; published online in A&A 555, L6 (2013