Progressive Rehabilitation Based on EMG Gesture Classification and an MPC-Driven Exoskeleton.

Stroke is a leading cause of disability and death worldwide, with a prevalence of 200 millions of cases worldwide. Motor disability is presented in 80% of patients. In this context, physical rehabilitation plays a fundamental role for gradually recovery of mobility. In this work, we designed a robotic hand exoskeleton to support rehabilitation of patients after a stroke episode. The system acquires electromyographic (EMG) signals in the forearm, and automatically estimates the movement intention for five gestures. Subsequently, we developed a predictive adaptive control of the exoskeleton to compensate for three different levels of muscle fatigue during the rehabilitation therapy exercises. The proposed system could be used to assist the rehabilitation therapy of the patients by providing a repetitive, intense, and adaptive assistance.

Characterization of Rice Yield Based on Biomass and SPAD-Based Leaf Nitrogen for Large Genotype Plots.

The use of Unmanned Aerial Vehicle (UAV) images for biomass and nitrogen estimation offers multiple opportunities for improving rice yields. UAV images provide detailed, high-resolution visual information about vegetation properties, enabling the identification of phenotypic characteristics for selecting the best varieties, improving yield predictions, and supporting ecosystem monitoring and conservation efforts. In this study, an analysis of biomass and nitrogen is conducted on 59 rice plots selected at random from a more extensive trial comprising 400 rice genotypes. A UAV acquires multispectral reflectance channels across a rice field of subplots containing different genotypes. Based on the ground-truth data, yields are characterized for the 59 plots and correlated with the Vegetation Indices (VIs) calculated from the photogrammetric mapping. The VIs are weighted by the segmentation of the plants from the soil and used as a feature matrix to estimate, via machine learning models, the biomass and nitrogen of the selected rice genotypes. The genotype IR 93346 presented the highest yield with a biomass gain of 10,252.78 kg/ha and an average daily biomass gain above 49.92 g/day. The VIs with the highest correlations with the ground-truth variables were NDVI and SAVI for wet biomass, GNDVI and NDVI for dry biomass, GNDVI and SAVI for height, and NDVI and ARVI for nitrogen. The machine learning model that performed best in estimating the variables of the 59 plots was the Gaussian Process Regression (GPR) model with a correlation factor of 0.98 for wet biomass, 0.99 for dry biomass, and 1 for nitrogen. The results presented demonstrate that it is possible to characterize the yields of rice plots containing different genotypes through ground-truth data and VIs.

Mechanical Performance of Commercially Available Premix UHPC-Based 3D Printable Concrete.

Several recent studies have attempted to formulate printable cementitious materials to meet the printing requirements, but these materials are designed to work with specific printing equipment and printing configurations. This paper aims to systematically develop and perform characterization of a commercially available ultra-high-performance concrete-class material (UHPC) modified to be printable. Four percentages of superplasticizer were used (100%, 94%, 88%, 82%) to adjust the UHPC mixture for 3D-printing requirements. A superplasticizer amount of 88% was considered adequate to meet the requirements. Several fresh and hardened properties of UHPC were measured experimentally: shape-retention ability and green strength were investigated in fresh state, and compressive and flexural strength were evaluated in three loading directions to evaluate the anisotropic effects. Furthermore, the strength of the interlayer bond was investigated. The UHPC developed in this study met the criteria for extrudability, buildability, and shape retention to ensure printability. In comparison with mold-cast UHPC, printed UHPC exhibited superior flexural performance (15-18%), but reduced compressive strength (32-56%). Finally, the results demonstrated that a commercially available UHPC-class material can be used for 3DCP, which possesses all necessary properties, both fresh and hardened.

A novel NIR-image segmentation method for the precise estimation of above-ground biomass in rice crops.

Traditional methods to measure spatio-temporal variations in biomass rely on a labor-intensive destructive sampling of the crop. In this paper, we present a high-throughput phenotyping approach for the estimation of Above-Ground Biomass Dynamics (AGBD) using an unmanned aerial system. Multispectral imagery was acquired and processed by using the proposed segmentation method called GFKuts, that optimally labels the plot canopy based on a Gaussian mixture model, a Montecarlo based K-means, and a guided image filtering. Accurate plot segmentation results enabled the extraction of several canopy features associated with biomass yield. Machine learning algorithms were trained to estimate the AGBD according to the growth stages of the crop and the physiological response of two rice genotypes under lowland and upland production systems. Results report AGBD estimation correlations with an average of r = 0.95 and R2 = 0.91 according to the experimental data. We compared our segmentation method against a traditional technique based on clustering. A comprehensive improvement of 13% in the biomass correlation was obtained thanks to the segmentation method proposed herein.