Sensor Fusion and Resource Management for Physiological Monitoring in the Swarm
Viswam Nathan, Ilge Akkaya, Roozbeh Jafari

Citation
Viswam Nathan, Ilge Akkaya, Roozbeh Jafari. "Sensor Fusion and Resource Management for Physiological Monitoring in the Swarm". Talk or presentation, 14, October, 2015; Poster presented at the 2015 TerraSwarm Annual Meeting.

Abstract
Ubiquitous and pervasive physiological monitoring in the context of the swarm incurs the obstacle of potentially very noisy signals, for example caused by motion artifacts on wearable sensors. The system would also need to recognize when one or more sensors is not being used or improperly placed so that the estimate can come from any remaining sensors. Finally, ensuring efficient usage of power and computational resources in a system of multiple sensors on the body and in the environment is of primary importance. We use a particle filter to ensure robust estimation of target phenomenon in noisy scenarios. This probabilistic technique has several advantages, among which are the fact that it does not require linear models of signal and noise to operate and also the fact that it can track multiple possible states of the system as it converges on a posterior probability distribution of the true state. We established the efficacy of the filter by showing that it can estimate the true heart rate in real motion affected electrocardiogram (ECG) data with an error of less than 5 beats per minute (5bpm) under SNR conditions as low as -6dB. We then present ongoing research that includes models for how the observations from multiple sensors can be fused to improve the accuracy, as well as an information theoretic approach to dynamically minimize the number of sensors being used to save on resources while maintaining estimation accuracy. We are testing these models on a real multi-modal dataset (ECG + Photoplethysmogram + Accelerometer) from the Signal Processing Cup 2015 and the initial results are promising in terms of improved estimation accuracy. This framework can prove very useful to ensure effective exploitation of multiple sensors and modalities to counteract noisy signals, while also maintaining awareness of resource usage to facilitate extended use of the sensors for long term monitoring.

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Citation formats

• HTML

  Viswam Nathan, Ilge Akkaya, Roozbeh Jafari. <a
  href="http://www.terraswarm.org/pubs/657.html"><i>Sensor
  Fusion and Resource Management for Physiological Monitoring
  in the Swarm</i></a>, Talk or presentation,  14,
  October, 2015; Poster presented at the <a
  href="http://terraswarm.org/conferences/15/annual"
  >2015 TerraSwarm Annual Meeting</a>.

• Plain text

  Viswam Nathan, Ilge Akkaya, Roozbeh Jafari. "Sensor
  Fusion and Resource Management for Physiological Monitoring
  in the Swarm". Talk or presentation,  14, October,
  2015; Poster presented at the <a
  href="http://terraswarm.org/conferences/15/annual"
  >2015 TerraSwarm Annual Meeting</a>.

• BibTeX

  @presentation{NathanAkkayaJafari15_SensorFusionResourceManagementForPhysiologicalMonitoring,
      author = {Viswam Nathan and Ilge Akkaya and Roozbeh Jafari},
      title = {Sensor Fusion and Resource Management for
                Physiological Monitoring in the Swarm},
      day = {14},
      month = {October},
      year = {2015},
      note = {Poster presented at the <a
                href="http://terraswarm.org/conferences/15/annual"
                >2015 TerraSwarm Annual Meeting</a>},
      abstract = {Ubiquitous and pervasive physiological monitoring
                in the context of the swarm incurs the obstacle of
                potentially very noisy signals, for example caused
                by motion artifacts on wearable sensors. The
                system would also need to recognize when one or
                more sensors is not being used or improperly
                placed so that the estimate can come from any
                remaining sensors. Finally, ensuring efficient
                usage of power and computational resources in a
                system of multiple sensors on the body and in the
                environment is of primary importance. We use a
                particle filter to ensure robust estimation of
                target phenomenon in noisy scenarios. This
                probabilistic technique has several advantages,
                among which are the fact that it does not require
                linear models of signal and noise to operate and
                also the fact that it can track multiple possible
                states of the system as it converges on a
                posterior probability distribution of the true
                state. We established the efficacy of the filter
                by showing that it can estimate the true heart
                rate in real motion affected electrocardiogram
                (ECG) data with an error of less than 5 beats per
                minute (5bpm) under SNR conditions as low as -6dB.
                We then present ongoing research that includes
                models for how the observations from multiple
                sensors can be fused to improve the accuracy, as
                well as an information theoretic approach to
                dynamically minimize the number of sensors being
                used to save on resources while maintaining
                estimation accuracy. We are testing these models
                on a real multi-modal dataset (ECG +
                Photoplethysmogram + Accelerometer) from the
                Signal Processing Cup 2015 and the initial results
                are promising in terms of improved estimation
                accuracy. This framework can prove very useful to
                ensure effective exploitation of multiple sensors
                and modalities to counteract noisy signals, while
                also maintaining awareness of resource usage to
                facilitate extended use of the sensors for long
                term monitoring.},
      URL = {http://terraswarm.org/pubs/657.html}
  }
  

Posted by Viswam Nathan on 10 Oct 2015.
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