• Korean Journal of Applied Biomechanics
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• v.24 no.1
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• pp.43-50
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• 2014

The purpose of this study was to analyze of the GRF (ground reaction force) parameters according to the change of positions and weights of bag during downward stairs between dominant and non-dominant in upper & lower limbs. To perform this study, participants were selected 9 healthy women (age: $21.40{\pm}0.94yrs$, height: $166.50{\pm}2.68cm$, body mass: $57.00{\pm}3.61kg$, BMI: $20.53{\pm}1.03kg/m^2$), divided into 2 carrying bag positions (dominant arm/R, non-dominant arm/L) and walked with 3 type of bag weights (0, 3, 5 kg) respectively. One force-plate was used to collect GRF (AMTI OR6-7) data at a sample rate of 1000 Hz. The variables analyzed were consisted of the medial-lateral GRF (Fx), anterior-posterior GRF (Fy), vertical GRF (Fz), impact loading rate and center of pressure (COPx, COPy, COP area, COPy posterior peak time) during downward stairs. 1) The Fx, Fy, Fz, COPx, and COP area of GRF were not statistically significant between dominant leg and non-dominant leg, but non-dominant leg, that is, showed the higher COPy, and showed higher impact loading rate than that dominant leg during downward stairs. 2) In bag wearing to non-dominant arm, Fx, Fz, COPx, COPy, impact loading rate and COP area showed increase tendency according to increase of bag weights. Also, against bag wearing to dominant arm, non-dominant showed different mechanism according to increase of bag weights. The Ground Reaction Force parameters showed different characteristics according to the positions and weights of bag during downward stairs between dominant and non-dominant arm.

• Korean Journal of Remote Sensing
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• v.25 no.2
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• pp.95-105
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• 2009

Irregular temporal sampling is a common feature of geophysical and biological time series in remote sensing. This study proposes an on-line system for reconstructing observation image series including bad or missing observation that result from mechanical problems or sensing environmental condition. The surface parameters associated with the land are usually dependent on the climate, and many physical processes that are displayed in the image sensed from the land then exhibit temporal variation with seasonal periodicity. An adaptive feedback system proposed in this study reconstructs a sequence of images remotely sensed from the land surface having the physical processes with seasonal periodicity. The harmonic model is used to track seasonal variation through time, and a Gibbs random field (GRF) is used to represent the spatial dependency of digital image processes. In this study, the Normalized Difference Vegetation Index (NDVI) image was computed for one week composites of the Advanced Very High Resolution Radiometer (AVHRR) imagery over the Korean peninsula, and the adaptive reconstruction of harmonic model was then applied to the NDVI time series from 1996 to 2000 for tracking changes on the ground vegetation. The results show that the adaptive approach is potentially very effective for continuously monitoring changes on near-real time.