9th International Conference on Body Area Networks

Research Article

Reconfigurable, Wearable Sensors to Enable Long-Duration Circadian Biomedical Studies

  • @INPROCEEDINGS{10.4108/icst.bodynets.2014.257078,
        author={David Burnett and Benjamin Smarr and Sahar Mesri and Lance Kriegsfeld and Kristofer Pister},
        title={Reconfigurable, Wearable Sensors to Enable Long-Duration Circadian Biomedical Studies},
        proceedings={9th International Conference on Body Area Networks},
        publisher={ICST},
        proceedings_a={BODYNETS},
        year={2014},
        month={11},
        keywords={biomedical monitoring wearable sensors biological rhythms translational health metrics},
        doi={10.4108/icst.bodynets.2014.257078}
    }
    
  • David Burnett
    Benjamin Smarr
    Sahar Mesri
    Lance Kriegsfeld
    Kristofer Pister
    Year: 2014
    Reconfigurable, Wearable Sensors to Enable Long-Duration Circadian Biomedical Studies
    BODYNETS
    ACM
    DOI: 10.4108/icst.bodynets.2014.257078
David Burnett1,*, Benjamin Smarr1, Sahar Mesri1, Lance Kriegsfeld1, Kristofer Pister1
  • 1: University of California, Berkeley
*Contact email: db@eecs.berkeley.edu

Abstract

The last 10 years have seen the emergence of wearable personal health tracking devices as a mainstream industry; however, they remain limited by battery lifetime, specific sensor selection, and a market motivated by a focus on short-term fitness metrics (e.g., steps/day). This hampers the development of a potentially much broader application area based on optimization around biomedical theory for long-term diagnostic discovery. As new biometric sensors come online, the ideal platform enabling the gathering of long-term diagnostic data would have the built-in extensibility to allow testing of different sensor combinations in different research settings to discover what kinds of data can be most useful for specific biomedical applications. Here we present the first generation of a reconfigurable wrist-mounted sensor device measuring 7x4x2cm and weighing 51g with battery (29g without). In its current configuration, it has recorded skin temperature, acceleration, and light exposure; these three variables allow prediction of internal circadian rhythms, as an example of the application of biological theory to enhance pattern detection. This generation is capable of operating long-term with minimal day-to-day disruption via easily exchangeable batteries, and has enough space for several months of data sampling to gather long-term diagnostic metrics. Future developments will include the addition of energy scavenging and a wireless mesh network for ambient data collection.