BSP Lab Research

Current and Past Research

The BSP lab has many research projects underway. Short summaries of these projects are given below. Please see our Publications page for a more complete account of the work that we have done and especially our prior work. Code and data sets used in our research projects can be found at Downloads.

  • Sensor Technology for Elder Care
    We are developing algorithms and systems to support seniors living independently in their home through unobtrusive monitoring of location, mobility, and activities of daily living. We have developed unique approaches for indoor tracking that operates without requiring the person to wear a device or tag. Supported by grants from NIH and the Alzheimer’s Association.
  • Integrated Navigation Systems (INS) for Pedestrian Tracking and Body Joint Angle Estimation
    INS systems involve the use of state-space estimation methods to combine various inertial sensors (i.e., MEMs gyroscopes and accelerometers) with external observations (e.g., GPS or indoor beacon technology) in order to estimate both the position and pose of the object being tracked. Prior work focused on Unmanned Aerial Vehicles. These methods are now being applied to the growing field of wearables for tracking people indoors or for dynamically estimating body joint angles.

  • Ambulatory Monitoring of Parkinson's Disease
    Parkinson's disease afflicts well over 500,000 Americans and over 4 million world wide. We are investigating new methods for ambulatory monitoring of Parkinson's disease and other movement disorders.
  • Cardiovascular Dynamics of Intracranial Pressure for Traumatic Brain Injury
    We have several projects that relate to advancing our knowledge of cardiovascular dynamics as it relates to intracranial pressure in children with traumatic brain injury.
  • Microelectrode Analysis
    We are analyzing microelectrode recordings (MER) acquired from patients with Parkinson's disease (PD) and essential tremor (ET) during stereotactic surgery to help localize critical structures and improve this type of therapy.

  • New Methods for Nonlinear Kalman Filtering and Recursive Bayesian Estimation
    Our core research on recursive Bayesian estimation and state-space methods cuts across multiple research projects and has found wide use in many related fields. Algorithms that have been developed include the Unscented/Sigma-Point Kalman Filter (UKF/SPKF), SPKF smoothers, and particle filter hybrids.  See also ReBEL download for available software.
  • Frequency Tracking
    We have many applications that contain quasi-periodic non-sinusoidal signals with time-varying amplitudes. We are investigating new approaches to these problems based on the latest methods for state space estimation.
  • Noninvasive Blood Glucose Monitoring using Otoacoustic Emissions
    The objective of this research was to determine whether otoacoustic emissions (OAE) can be used to measure blood glucose noninvasively in diabetic subjects. An OAE is a low-intensity sound generated by the cochlea in response to acoustic stimuli.

  • Automatic Classification of Flying Insects
    This project involved research and development of remote optical instrumentation capable of automatically counting and classifying insects in flight. Neither acoustic or image based, the instrument uses a solar cell as an unconventional sensor to record rapid fluctuations in light intensity caused by the shadow or reflection of a flying insect. Applications include monitoring for integrated pest management, for public health (e.g. tracking outbreaks of the West Niles virus or Malaria), and for assessing biodiversity.
  • Heart Function Characterization Combining Acoustic and Electrocardiogram Signals
    Working with Inovise Medical Inc., this project applied adaptive signal processing and machine learning techniques to the problem of heart function characterization using combined acoustic and ECG signals.