Stephen Paul Linder

Using The Morphology of Photoplethysmogram Peaks to Detect Changes in Posture

The morphology of the pulsatile component of the photoplethysmogram (PPG) has been shown to vary with physiology, but changes in the morphology caused by the baroreflex response to orthostatic stress have not been investigated.Using two FDA approved Nonin® pulse oximeters placed on the finger and ear, we monitored 11 subjects, for three trials each, as they stood from a supine position. Each cardiac cycle was automatically extracted from the PPG waveform and characterized using statistics corresponding to normalized peak width, instantaneous heart rate, and amplitude of the pulsatile component of the ear PPG. A nonparametric Wilcoxon rank sum test was then used to detect in real-time changes in these features with p < 0.01.In all 33 trials, the standing event was detected as an abrupt change in at least two of these features, with only one false alarm. In 26 trials, an abrupt change was detected in all three features, with no false alarms. An increase in the normalize peak width was detected before an increase in heart rate, and in 21 trials a peak in the feature was detected before or as standing commenced. During standing, the pulse rate always increases, and then amplitude of the ear PPG constricts by a factor of two or more.We hypothesis that the baroreflex first reduces the percentage of time blood flow is stagnant during the cardiac cycle, then increases the hear rate, and finally vasoconstricts the peripheral tissue in order to reestablishing a nominal blood pressure. These three features therefore can be used as a detector of the baroreflex response to changes in posture or other forms of blood volume sequestration.

Tracking highly maneuverable targets with unknown behavior

Tracking of highly maneuvering targets with unknown behavior is a difficult problem in sequential state estimation. The performance of predictive-model-based Bayesian state estimators deteriorates quickly when their models are no longer accurate or their process noise is large. A data-driven approach to tracking, the segmenting track identifier (STI), is presented as an algorithm that operates well in environments where the measurement system is well understood but target motion is either or both highly unpredictable or poorly characterized. The STI achieves improved state estimates by the least-squares fitting of a motion model to a segment of data that has been partitioned from the total track such that it represents a single maneuver. Real-world STI tracking performance is demonstrated using sonar data collected from free-swimming fish, where the STI is shown to be effective at tracking highly maneuvering targets while relatively insensitive to its tuning parameters. Additionally, an extension of the STI to allow its use in the most common multiple target and cluttered environment data association frameworks is presented, and an STI-based joint probabilistic data association filter (STIJPDAF) is derived as a specific example. The STIJPDAF is shown by simulation to be effective at tracking a single fish in clutter and through empirical results from video data to be effective at simultaneously tracking multiple free-swimming fish.

Proportional integral adaptive observer for parameter and disturbance estimations

This paper proposes an adaptive observer structure which can be employed in simultaneous estimation of system parameters and disturbances. The starting point is the proportional integral (PI) observer which has been shown to be effective not only in loop transfer recovery (LTR) but also in estimating and accommodating disturbances. We show that PI structure can further be generalized to PI adaptive observer (PIAO) to expand the applicability of integral action to systems with unknown parameters. A systematic design procedure is developed for estimating the states, the parameters, and the unknown inputs of a dynamic system in open-loop and closed-loop settings. Simulation results support the theoretical developments.

A general purpose observer architecture with application to failure detection and isolation

Proposes a general observer structure which can be employed in various estimation and control problems. The starting point is the proportional integral (PI) observer which has been shown to be effective not only in loop transfer recovery (LTR) but also in estimating and accommodating disturbances. The connections of the PI observer to a disturbance observer (DO) and an unknown input observer (UIO) are established. We show that the PI structure can be further generalized to a PI adaptive observer and a PI observer with fading property. The PI adaptive (PIA) observer expands the applicability of integral action to systems with unknown parameters, while the PI fading (PIF) observer can also accommodate transitory disturbances of unknown origin. It is also shown that, when process and sensor noises are present, a PI Kalman filter can be used to achieve the same goal. A systematic design procedure is developed for estimating both the state and the unknown inputs of a dynamic system. The results presented are applicable directly to fault detection and isolation (FDI) of systems under sensor and/or actuator failures.

Design of optimal gains for the proportional integral Kalman filter with application to single particle tracking

In this paper, we introduce an optimal design strategy for the proportional-integral (PI) Kalman filter. The design of the PI Kalman filter involves the design of four matrices: The proportional and integral gains, the fading constant and the integral effect coefficient. The method in this paper provides optimal proportional and integral gains. Guidelines for the design of the fading constant and the integral effect coefficient are discussed as well. The suggested algorithm was tested as a single particle tracking system, for a maneuvering target.

Rejecting disturbances to flexible structures using PI Kalman filters

The proportional Kalman filter (PKF) is formulated with integral action and generalized to the proportional-integral Kalman filter (PIKF). The PIKF can be utilized to improve the disturbance rejection property of the Kalman filter-based controller. Through extensive simulations, as applied to the lightly damped flexible structure of the 1992 ACC Benchmark, we show that the integral term of the PIKF allows effective estimation and rejection of arbitrary disturbances. As a result, a single PIKF-based controller robustly achieves full loop transfer recovery even when perturbed by an unknown shaped disturbance.

Robust PFI Kalman filters

The proportional fading-integral (PFI) Kalman filter is a generalization of the proportional-integral (PI) Kalman filter. We show that integral action allows for the accurate estimate of disturbances caused by unknown inputs and plant perturbations, dramatically improving the rejection properties of the Kalman filter. The fading integral of the PFI Kalman filter further extends the robustness of the filter by increasing the filters stability margin, while allowing the rejection of transitory disturbances with unknown distribution matrices. Through extensive simulations, as applied to the 1992 ACC robust control benchmark, we show that the fading integral doubles the stability margin of a Kalman filter-based state feedback regulator when used to estimate the rank one perturbation caused by a mismatched spring constant.

Robotic Rock Climbing using Computer Vision and Force Feedback

Climbing robots that climb flat structures using suction cups or magnets are commonly described in the literature. However, robots that can autonomously find randomly placed handholds, and then plan and climb a route up the walls using those handholds, have not been described. A low cost robot has been designed by Dartmouth College students to climb a near-vertical indoor climbing wall using computer vision to locate handholds, and force feedback to maintain pressure on handholds. Using a variation of the probabilistic roadmap algorithm, a climbing route up the wall is planned and then executed.

Qualitative robust fuzzy control with applications to 1992 ACC benchmark

Robust control has long been the purview of quantitative linear control techniques, while qualitative symbolic control has been deemed more suitable to obtaining complex control objectives that require only low-output precision. The intelligent techniques of fuzzy control have, however, shown promise in obtaining results comparable to those obtained from H/sub /spl infin// and H/sub 2/ robust control techniques. Often though, these fuzzy control techniques ignore the original intent of fuzzy logic: implementation of symbolic linguistic control laws based on qualitative models of the plant and control behaviors. We show that robust control objectives, even for simple plants, can be achieved by first developing qualitative behaviors that stabilize the plant and then superimposing tracking behaviors that achieve control objectives. Specifically, by superimposing qualitative stability and tracking behaviors, we can achieve robustness and tracking stability comparable to the best published linear compensators for the 1992 ACC robust control benchmark.

Simulation environments for the design and test of an intelligent controller for autonomous underwater vehicles

Intelligent controllers usually consist of a hybrid system that includes both discrete and continuous processes. This hybrid construction poses difficulties in validating and verifying their design. As the use of intelligent controllers proliferates throughout society, the development of simulation techniques that support both the construction and testing of these controllers becomes increasingly important. At the Applied Research Laboratory (ARL) of the Pennsylvania State University, we have gained insight over the last ten years (1989-99) into the design, implementation and testing of intelligent controllers for Autonomous Underwater Vehicles (AUV). However, as AUV missions become more complex, simulation environments must be provided that achieve complete state coverage of the discrete processes of the controller while still fully exercising the continuous processes with high fidelity Monte Carlo simulations. As an illustrative example, the paper describes the current utilization of simulation in the development and testing of intelligent mission controllers for AUVs using ARLs own intelligent control architecture. Then our new paradigm for simulation based design and testing for intelligent controllers is formulated and discussed.