8 skills found
andrewcmyers / ConstrainResponsive, animated figures in JavaScript/HTML canvases
mahmoodsh36 / Org Xoppembed xournalpp figures/pages in emacs' org-mode
dfm / SavefigSave matplotlib figures with embedded metadata for reproducibility and profit
abdallahkhairy / GP Data Analysis And MLHuman locomotion affects our daily living activities. Losing limbs or having neurological disorders with motor deficits could affect the quality of life. Gait analysis is a systematic study of human locomotion, which is defined as body movements through aerial, aquatic, or terrestrial space. This analysis has been used to study people ambulation, registration, and reconstruction of physical location and orientation of individual limbs used to quantify and characterize human locomotion using different gait parameters including gait activities such as walking, stairs ascending/descending, … etc., phases, and spatiotemporal parameters of human gait. Additionally, gait analysis parameters can be used to evaluate the functionality of patients and wearable system users. The evaluation is based on patient's stability, energy consumption, gait symmetry, ability to recover from perturbations, and ability to perform activities of daily living. Many companies develop assistive, wearable, and rehabilitation devices for patients with lower limb neurological disorders. These devices are tested and evaluated inside controlled lab environments. However, they don’t have enough data on the patient's performance in real world and harsh environments. Collecting large datasets of device users and their gait performance data in real environment are notoriously difficult. Additionally, collecting data on less prevalent or on gait activities other than level walking, stair ascending/descending, sitting, standing, …etc. on hard surfaces is rarely attempted. However, the scope for collecting gait data from alternative sources other than traditional gait labs could be attained with the help of IoT data collection embedded on the wearable and assistive devices and well-established cloud platforms equipped with big-data analytics and data visualization capabilities. This project aims to develop a cloud platform capable of collect data from wearable and assistive devices such as prostheses, exoskeleton, gait analysis wearable sensors, …etc. using IoT technologies. This platform is capable of automatically use data mining and visualization tools. Additionally, it uses statistical and machine learning techniques to estimate gait events, gait symmetry, gait speed, gait activities, stability, energy consumption, …etc. Also, it is capable of predicting patient's progress over time. The project will be composed of two major components, hardware component and software component. In hardware component, the students will design and implement the IoT that collects the different readings for gait analysis and send them to the cloud. Meanwhile, in the software component, the students will design and implement a set of algorithms to visualize the collected data, then design and implement data analytics to automatically analyze the collected data, so that we can estimate gait events, gait symmetry, gait speed, classify gait activities, stability, energy consumption, …etc. and predicting patient's progress over time. By analyzing the collected data, the patient's progress can be predicted over time. Additionally, these data can be used through manufacturers of prostheses legs to improve their products, as well as through health-care centers to assess the patient's performance. The following figures describe the main modules of our graduation project.
AdaptiveMotorControlLab / Cebra FiguresFigures, tables and stats for Schneider, Lee and Mathis 2022: Learnable latent embeddings for joint behavioral and neural analysis.
IgniteUI / Autosales Dashboard SampleThe Auto Sales Tracking sample is an example application showcasing some of the most powerful Ignite UI controls including the map, grid, and various charts. The map control shows the geographical region represented in the sales data. Bullet graphs, data charts, and pie charts show sales figures over time and in relation to target figures. Sales are detailed using the grid control by dealership and manufacturer and bullet graphs embedded in the grid provide glanceable sales summaries. The application demonstrates how Ignite UI controls are used together to build an immersive and attractive user experience.
mje-nz / PythontexfiguresEmbed matplotlib figures into LaTeX documents using PythonTeX
scidam / Django MatplotlibDjango matplotlib field: embed matplotlib figures into django driven web-applications
