The humble RPi CM5 that powers Sensing, Automation, Analytics & Media at home
May 27, 2025
The Raspberry Pi Compute Module 5 (CM5) - This tiny energy efficient powerhouse has become the brain of my home automation system with Home Assistant.
The quad-core ARM Cortex-A76 processor, coupled with up to 8GB of LPDDR5 EEC RAM, makes it a a great piece of hardware. But what really sets it apart is its ability to handle complex tasks while maintaining energy efficiency for a 24/7 home automation system.
The system was responsive the system, even with dozens of automation rules and sensors running simultaneously.
Speaking of sensors, the CM5's versatility truly shines in how it handles multiple sensor inputs. Temperature and humidity sensors in each room help maintain optimal comfort levels, while motion sensors control lighting and security. I added camera integration, which uses our existing security cameras with AI vision capabilities. The LLM (Large Language Model) vision integration, my cameras can recognize and distinguish between different types of vehicles, make and model as well as description of people.
The AI vision system provides incredibly detailed descriptions of what it sees. For instance, instead of a simple "motion detected" alert, I receive notifications like "Person wearing a blue jacket approaching the front door with a package" or specific car models. color etc. This level of detail has been invaluable for both security and convenience. When a delivery person arrives, I get an instant notification with a description of their appearance and actions, complete with a snapshot of the event that is then alerted via automation task sent as a notification to my phone.
Another exciting aspect is the integration of Plex Media Server. Running on the CM5, it efficiently manages and streams my media collection throughout the house. The powerful processor handles transcoding tasks smoothly, ensuring a seamless streaming experience across different devices.
In my garage, I've been using an mmWave sensor, this radar technology can detect and analyze human movements with remarkable precision. It tracks my workout sessions, providing detailed data about movement patterns, and exercise duration.
To handle the substantial amount of data generated by various sensors, I've implemented MariaDB as my database solution. This database efficiently stores and manages data from all sensors, including exercise tracking information, temperature readings, security events, and automation logs.
Through home assistant, I also have the built-in Jupyter notebook integration. This allows me to perform sophisticated data analytics on the collected information. Using Python and various data science libraries, I can create detailed visualizations and reports about my home's operation. For instance, I can analyze patterns in temperature variations, study exercise habits over time, or identify trends in energy usage.
Environmental monitoring is another crucial aspect of my smart home system. I've incorporated VOC (Volatile Organic Compounds) and NOx (Nitrogen Oxides) sensors to maintain comprehensive air quality monitoring throughout the house. These sensors provide detailed data about indoor air pollutants that could affect our health. The VOC sensors detect a wide range of organic compounds from everyday sources like cleaning products, paint, and furniture, while the NOx sensors monitor pollution that might enter from outside, especially during high traffic periods.
The CM5 processes this environmental data in real-time, triggering automated responses when pollutant levels exceed predetermined thresholds.
For the future, there are possibilities to implement a system to analyze wind patterns and predicting potential storm-related power outages.