Industry 5.0 Action Recognition: High-Dimensional Kinematic Streams for Scalable & Online Learning

📖 Project Description This capstone project challenges students to design scalable machine learning pipelines for time-series sensor data unsupervised learning under real-world big data constraints. The dataset consists of multi-sensor recordings capturing human production activities across multiple body segments. Each observation contains high-dimensional rotational and translational signals recorded over time. Students must demonstrate the ability to: Efficiently load and process large-scale time-series data Perform scalable exploratory data analysis (EDA) Engineer meaningful temporal and statistical features Build robust unsupervised learning models Implement online/streaming learning approaches Handle memory, computation, and scalability challenges ⚠️ Due to data privacy restrictions, this dataset is: Not publicly accessible Available only via approved academic request 🔐 Access Policy Access Type: Restricted Eligibility: Students enrolled in Big Data Analysis Requirement: Use institutional email Approval: Manual verification by instructor Usage: Strictly educational (no redistribution)