Front Elevation

14,000,000 Leading Edge Experts on the ideXlab platform

Scan Science and Technology

Contact Leading Edge Experts & Companies

Scan Science and Technology

Contact Leading Edge Experts & Companies

The Experts below are selected from a list of 234 Experts worldwide ranked by ideXlab platform

David S Castle - One of the best experts on this subject based on the ideXlab platform.

K M Yogesh - One of the best experts on this subject based on the ideXlab platform.

  • ICACCI - Instance based human physical activity(hpa) recognition using shimmer2 wearable sensor data sets
    2017 International Conference on Advances in Computing Communications and Informatics (ICACCI), 2017
    Co-Authors: Doreswamy, K M Yogesh
    Abstract:

    Human physical activity (HPA) recognition is one of the a large amount emerging fields of research in pervasive summing. In wearable computing scenarios, human physical activities such as standing still, sitting and relaxing, lying down, walking, climbing stairs, waist bends forward, Front Elevation of arms, knee bending, cycling, jogging, running and jump Front and back can be implicit from sensor data provided by shimmer2 acceleration sensors. In such scenarios, most methods use a one or two dimensional features, nevertheless of which activity to be identified. This paper we can identified how to predict human physical activity using tri-accelerometer three dimensional data generated by shimmer2 wearable sensor device. We represent a efficient sensor data analysis of features computed from a realistic accelerometer sensor data set and different classifiers are studied on instances based data sets. This shows that the choice of time domain feature and the window dimension accomplished on which the computed features that transform the activity accuracy rates for different huamn physical activities.

  • Instance based human physical activity(hpa) recognition using shimmer2 wearable sensor data sets
    2017 International Conference on Advances in Computing Communications and Informatics (ICACCI), 2017
    Co-Authors: K M Yogesh
    Abstract:

    Human physical activity (HPA) recognition is one of the a large amount emerging fields of research in pervasive summing. In wearable computing scenarios, human physical activities such as standing still, sitting and relaxing, lying down, walking, climbing stairs, waist bends forward, Front Elevation of arms, knee bending, cycling, jogging, running and jump Front and back can be implicit from sensor data provided by shimmer2 acceleration sensors. In such scenarios, most methods use a one or two dimensional features, nevertheless of which activity to be identified. This paper we can identified how to predict human physical activity using tri-accelerometer three dimensional data generated by shimmer2 wearable sensor device. We represent a efficient sensor data analysis of features computed from a realistic accelerometer sensor data set and different classifiers are studied on instances based data sets. This shows that the choice of time domain feature and the window dimension accomplished on which the computed features that transform the activity accuracy rates for different huamn physical activities.

Publieke Werke Dept. - One of the best experts on this subject based on the ideXlab platform.

Raiford L. Stripling - One of the best experts on this subject based on the ideXlab platform.

S.o.r. Moheimani - One of the best experts on this subject based on the ideXlab platform.

  • Spatial system identification of a simply supported beam and a trapezoidal cantilever plate
    Proceedings of the 41st IEEE Conference on Decision and Control 2002., 2002
    Co-Authors: A. Fleming, S.o.r. Moheimani
    Abstract:

    Dynamic models of structural and acoustic systems are usually obtained by means of modal analysis or finite element modelling. To their detriment, both techniques rely on a comprehensive knowledge of the system's physical properties. Experimental data and a nonlinear optimization is often required to refine the model. For the purpose of control, system identification is often employed to estimate the dynamics from disturbance and command inputs to a set of outputs. Such discretization of a spatially distributed system places further unknown weightings on the control objective, in many cases, contradicting the original goal of optimal control. This paper introduces a frequency domain system identification technique aimed at obtaining spatially continuous models for a class of distributed parameter systems. The technique is demonstrated by identifying a simply supported beam and trapezoidal cantilever plate, both with bonded piezoelectric transducers. The plate's dimensions are based on the scaled Front Elevation of a McDonnell Douglas FA-18 vertical stabilizer.