Internet of Things (IoT) Technologies for HealthCare. 4th International Conference, HealthyIoT 2017, Angers, France, October 24-25, 2017, Proceedings

Research Article

How Accurate Are Smartphone Accelerometers to Identify Intermittent Claudication?

Download
90 downloads
  • @INPROCEEDINGS{10.1007/978-3-319-76213-5_3,
        author={Carole Frindel and David Rousseau},
        title={How Accurate Are Smartphone Accelerometers to Identify Intermittent Claudication?},
        proceedings={Internet of Things (IoT) Technologies for HealthCare. 4th International Conference, HealthyIoT 2017, Angers, France, October 24-25, 2017, Proceedings},
        proceedings_a={HEALTHYIOT},
        year={2018},
        month={2},
        keywords={Intermittent claudication Smartphone Accelerometer Human motion Gait analysis Motion tracking},
        doi={10.1007/978-3-319-76213-5_3}
    }
    
  • Carole Frindel
    David Rousseau
    Year: 2018
    How Accurate Are Smartphone Accelerometers to Identify Intermittent Claudication?
    HEALTHYIOT
    Springer
    DOI: 10.1007/978-3-319-76213-5_3
Carole Frindel1,*, David Rousseau1
  • 1: Univ. Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1206
*Contact email: carole.frindel@creatis.insa-lyon.fr

Abstract

Claudication is a cramping pain that is worsened by walking and relieved with rest. It is caused by inadequate blood flow to the leg muscles because of atherosclerosis. Recently, smartphones and their sensors have been proposed in the context of mobile health to monitor gait. However, their use remains disputed: objections concern the quality of the collected data. Therefore, the work presented in this paper proposes to study three main sources of noise observed in smartphone accelerometers and to objectively assess their impact on claudication detection. To do so, we first observe three noise sources in four different smartphones to get an idea of their ranges; we second compare the smartphones’ signals to a ground truth from a vision-based system and third propose to detect claudication by estimating duty cycle from the vertical accelerometer signal and to evaluate the impact of the three noise sources on this basis.