MSDS696_PracticumII - Enhancing Longevity through Mobility: The Role of Strength and Bodyweight Training

High-Level Description of the Project: This project investigates the effectiveness of primal movement workouts—mimicking natural human movements like crawling, squatting, and climbing—in improving flexibility and range of motion (ROM).

Objective: Explore the relationship between pre-intervention stretching, strength gains, falls in older adults, and bodyweight training engagement.

Importance: Highlight the significance of mobility in promoting health and longevity.

Type of Data Science Task: Measurement using Time Series Analysis: To track changes in flexibility and ROM over time. Classification using Supervised Learning: To predict the benefits of primal movements contributing most to flexibility improvement. Data Visualization: To visualize improvement trajectories and effectiveness of various primal movements. Exploratory Data Analysis (EDA): Clean, preprocess the data, and identify patterns and relationships.

Data: This section outlines different sources where publicly available fitness and health-related datasets can be found, such as Kaggle, UCI Machine Learning Repository, government health agencies, university repositories, and the National Institutes of Health. It also mentions the possibility of web scraping data from fitness forums, blogs, academic publications, and other sources.

Analysis Plan: This section discusses the preprocessing steps for cleaning and standardizing data and the methods for analysis, including machine learning techniques like time series analysis, and classification models. Business intelligence to identify effective primal movements and workout optimization.

Data and Web Scraping Possibilities: This section sources for data and web scraping online fitness communities like Reddit and bodybuilding.com, fitness blogs, health and sports science peer-reviewed publications.