In networked estimation architectures, event-based sensing and communication can contribute to a more efficient resource allocation in general, and improved utilization of communication resources, in particular. In or...
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ISBN:
(数字)9781737749721
ISBN:
(纸本)9781665489416
In networked estimation architectures, event-based sensing and communication can contribute to a more efficient resource allocation in general, and improved utilization of communication resources, in particular. In order to tap the full potential of event-based scheduling, the design of transmission triggers and estimators need to be closely coupled while two directions are promising: First, the remote estimator can exploit the absence of transmissions and translate it into implicit information about the sensor data. Second, an intelligent trigger mechanism at the sensor that predicts future sensor readings can decrease transmission rates while rendering the implicit information more valuable. Such an intelligent trigger has been developed in a recent paper based on a Finite Impulse Response filter, which requires the sensor to transmit an additional estimate alongside the measurement. In the present paper, the communication demand is further reduced by only transmitting the estimate. The remote estimator exploits correlations to incorporate the received information. In doing so, the estimation quality is also improved, which is confirmed by simulations.
User localization is crucial for the development of context aware applications for intelligent environments. Wearable systems based on accelerometry have been proposed in recent years to palliate the drawbacks posed b...
User localization is crucial for the development of context aware applications for intelligent environments. Wearable systems based on accelerometry have been proposed in recent years to palliate the drawbacks posed by infrastructure based approaches. In this paper, we address the estimation of the displacement length of a user when walking straight ahead along flat surfaces by directly processing the raw accelerations from the body center of gravity. We analyze and compare two approaches from the state of the art. The effect of gender and excursion velocity on the performance of the estimators are especially addressed.
Step length estimation is an important issue in areas such as gait analysis, sport training or pedestrian localization. It has been shown that the mean step length can be computed by means of a triaxial accelerometer ...
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Step length estimation is an important issue in areas such as gait analysis, sport training or pedestrian localization. It has been shown that the mean step length can be computed by means of a triaxial accelerometer placed near the center of gravity of the human body. Estimations based on the inverted pendulum model are prone to underestimate the step length, and must be corrected by calibration. In this paper we present a modified pendulum model in which all the parameters correspond to anthropometric data of the individual. The method has been tested with a set of volunteers, both males and females. Experimental results show that this method provides an unbiased estimation of the actual displacement with a standard deviation lower than 2.1%.
Walking distance estimation is an important issue in areas such as gait analysis, sport training or pedestrian localization. A natural location for portable inertial sensors for gait monitoring is to attach them to th...
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Walking distance estimation is an important issue in areas such as gait analysis, sport training or pedestrian localization. A natural location for portable inertial sensors for gait monitoring is to attach them to the user shoes. Step length can be computed by means of a biaxial accelerometer and a gyroscope on the sagital plane. But estimations based on the direct signal integration are prone to error. This paper shows the results achieved by using a multisensor model approach to reduce uncertainty. Unbounded growth of error is reduced by means of sensor fusion techniques. The method has been tested, and early experimental results show that it provides an estimation of the walking distance with a standard deviation smaller than with single IMU similar systems.
Wearable accelerometry provides easily portable systems that supply real-time data adequate for gait analysis. When they do not provide direct measurement of a spatio-temporal parameter of interest, such as step lengt...
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Wearable accelerometry provides easily portable systems that supply real-time data adequate for gait analysis. When they do not provide direct measurement of a spatio-temporal parameter of interest, such as step length, it has to be estimated with a mathematical model from indirect sensor measurements. In this work we are concerned with the accelerometry-based estimation of the step length in straight line human walking. We compare five step length estimators. Measurements were taken from a group of four adult men, adding up a total of 800 m per individual of walking data. Also modifications to these estimators are proposed, based on biomechanical considerations. Results show that this modifications lead to improvements of interest over previous methods
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