INNOVATION PROJECTS: PARKOUR AI
Organisations face several problems.
Automation and artificial intelligence has been seen by many to provide the solution.
-
Employees stealing clients in client-facing roles
-
Employees learn trade secrets and then build rival companies.
-
Need a system that is pandemic proof (reduced human interaction).
-
Automation allows for a greater ability to scale operations
-
Reduced human error in service delivery.
-
Issues with skills gap - humans require training time and may not perform the role as desired.
-
Saves time and money for the organisation as repetitive tasks can be carried out by a computer at a fraction of the time.
-
Software automation prevents business systems from becoming geopolitically locked.
🚀 AI-Based Skill Assessment & Adaptive Training System
Overview
Designed and built an end-to-end system that captures real-world sensor data, processes it, and uses a custom-built neural network to assess user skill level and adapt training accordingly.
Problem
Traditional training systems apply a one-size-fits-all approach, failing to adapt to individual ability. This project explores how real-time data and machine learning can be used to automatically assess skill level and personalise training.
System Architecture
-
Data Capture:
Built a hardware-based data collection system using myo sensors and microcontrollers. Initially developed with Arduino, then transitioned to ESP32 for improved reliability and scalability.
Implemented firmware in C++ after identifying limitations in Python-based approaches (PyFirmata).
-
Data Pipeline & Storage:
Streamed continuous sensor data into a time-series database (InfluxDB) deployed via Docker.
Developed Python scripts to ingest, segment, and prepare data for training and testing workflows.
-
Machine Learning Engine:
Implemented a neural network from scratch in Python (NumPy) without relying on high-level libraries (e.g., TensorFlow).
Adapted learning algorithms to work with structured CSV data rather than standard image datasets.
Applied object-oriented design to support training, testing, and prediction pipelines
.
Key Technical Decisions
-
Switched from Arduino → ESP32 to improve hardware robustness
-
Moved from SQL → InfluxDB to support continuous time-series data
-
Built neural network from first principles to gain full control over model behaviour
-
Used NumPy for vectorised computation to optimise performance
Outcome
-
Functional end-to-end pipeline: sensor → database → model → prediction
-
Successfully processed and structured real-world data for machine learning use
-
Developed a flexible system capable of adapting training based on user performance
What This Demonstrates
-
End-to-end system design (hardware + data + ML)
-
Ability to learn and apply new technologies (C++, embedded systems, time-series DBs)
-
Strong understanding of machine learning fundamentals
-
Engineering decision-making and problem-solving under constraints
Future Work
-
Deploy model in real-time for live feedback
-
Expand dataset and improve model accuracy
-
Integrate into a user-facing application
-
Explore reinforcement learning for dynamic training adaptation
