Traditionally, monitoring biomechanics parameters requires a significant amount of sensors to track exercises such as gait. Both research and clinical studies have relied on intricate motion capture studios to yield precise measurements of movement. We propose a method that captures motion independently of optical hardware with the specific goal of identifying the phases of gait using joint angle measurement approaches like IMU (inertial measurement units) sensors. We are proposing a machine learning approach to progressively reduce the feature number (joint angles) required to classify the phases of gait without a significant drop in accuracy. We found that reducing the feature number from six (every joint used) to three reduces the mean classification accuracy by only 4.04%, while reducing the feature number from three to two drops mean classification accuracy by 7.46%. We extended gait phase classification by using the biomechanics simulation package, OpenSim, to generalize a set of required maximum joint moments to transition between phases. We believe this method could be used for applications other than monitoring the phases of gait with direct application to medical and assistive technology fields.
This paper presents a threshold color image segmentation methodology based on Self-Organizing Maps (SOM) Neural Network. The objective of segmentation methodology is to determine the minimum number of color features in six seed lines ("nh1", "nh2", "nh3", "nh4", "nh5" y "nh6") of seed castor (Ricinus comunnis L.) images for future seed characterization. Seed castor lines are characterized for pigmentation regions that not allow an optimum segmentation process. In some cases, seed pigmentation regions are similar to background make difficult their segmentation characterization. Methodology proposes to segment the seed image in a SOM-based idea in an increasing way until to some of SOM neuron not have allocated none of the image pixels. Several experiments were carried out with others two standard test images ("House" and "Girl") and results are presented both visual and numerical way. Keywords. Image segmentation, neural network, selforganizing maps. 15. Ortiz, A., Górriz, J. M., Ramírez, J., Salas-González, D., & Llamas-Elvira, J. M. (2013). Two fully-unsupervised methods for MR brain image segmentation using SOM-based strategies. Applied
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