• Journal of Biomedical Engineering Research
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• v.31 no.3
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• pp.217-226
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• 2010

The lower limb orthosis with a pneumatic rubber actuator, which is intended for the assistance and the enhancement of muscular activities of lower limbs was developed in this study. Compared to other knee extension assistive devices being developed by other researchers, our device is designed especially for the elderly people and intended only for slight assistance so that the subjects can keep their muscular strength. For the effectiveness of system, muscular activities of major muscles in lower limbs during sit-to-stand (STS) and squat motion were measured and analyzed. Subjects were performed the STS and squat motion with and without lower limb orthosis. We made comparison muscular activities between with and without lower limb orthosis. Lower limb orthosis was controlled using muscular stiffness force feedback that is controlled by muscular activities of the measured muscle from force sensor. For analysis of muscular activities, electromyography of the subjects was measured during STS and squat motion, and these were measured using MP 150(BIOPAC Systems, Inc.). Muscles of interest were rectus femoris(RF), vastus lateralis(VL), vastus medialis(VM) and vastus intermedius(VI) muscles in lower limbs of the right side. A biodex dynamometer was used to measure the maximal concentric isokinetic strength of the knee extensors of wearing and not wearing orthosis on right side. The test were performed using the concentric isokinetic mode of test with the velocity set at 60°/s for muscles around the knee joints. The experimental result showed that muscular activities in lower limbs wearing orthosis using muscular stiffness force of a vastus medialis muscle was reduced and knee extension torque of an knee joint wearing lower limb orthosis was increased. With this, we confirmed the effectiveness of the developed lower limb orthosis.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1341-1352
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• 2023

The normalized difference vegetation index (NDVI) derived from satellite images is a crucial tool to monitor forests and agriculture for broad areas because the periodic acquisition of the data is ensured. However, optical sensor-based vegetation indices(VI) are not accessible in some areas covered by clouds. This paper presented a synthetic aperture radar (SAR) based approach to retrieval of the optical sensor-based NDVI using machine learning. SAR system can observe the land surface day and night in all weather conditions. Radar vegetation indices (RVI) from the Sentinel-1 vertical-vertical (VV) and vertical-horizontal (VH) polarizations, surface elevation, and air temperature are used as the input features for an automated machine learning (AutoML) model to conduct the gap-filling of the Sentinel-2 NDVI. The mean bias error (MAE) was 7.214E-05, and the correlation coefficient (CC) was 0.878, demonstrating the feasibility of the proposed method. This approach can be applied to gap-free nationwide NDVI construction using Sentinel-1 and Sentinel-2 images for environmental monitoring and resource management.