Project Idea: An AI-Powered Desk Lamp That Learns Your Habits

DIYson next generation

Have you ever wished your desk lamp just knew when you needed it? Inspiration for such projects can come from various places, including advancements in smart home technology, innovative commercial lighting solutions (like Dyson lamps), and various creative tech demonstrations or project ideas one might find online (see for example: Project Inspiration 1 and Project Inspiration 2).

This post outlines a project idea: building an AI-powered desk lamp. The concept involves creating a lamp that learns your typical usage patterns based on sensor data and then automates the on/off process. This is an exploration of what could be done, a blueprint for a fascinating DIY project. I have not embarked on yet as I consider the path too long given my current skills and experience, hardware projects are better left to those who are able to work with hardware development.

The Core Idea: The goal of such a project would be to collect environmental data (light, sound, motion) along with manual lamp interactions (like button presses). This data could then be used to train a machine learning model, enabling the lamp to intelligently predict when it should turn on or off.

Potential Components for Such a Build:

If one were to embark on this project, the following components would likely be essential:

Raspberry Pi: To act as the central processing unit (a Raspberry Pi 3 or 4 would be suitable).
Desk Lamp: A standard lamp that can be modified for electronic control (e.g., via a relay) or a smart lamp compatible with Raspberry Pi.
Physical Button: For manual override and, crucially, to provide labeled training data for the AI.
Light Sensor: To measure ambient light levels (e.g., a photoresistor with an ADC, or a digital sensor like TSL2561/BH1750).
Microphone: To detect sound activity (a USB microphone or an I2S microphone module could be used).
PIR (Passive Infrared) Sensor: To detect motion.
Relay Module (if modifying a standard lamp): To allow the Raspberry Pi to safely switch the lamp’s AC power. Working with mains voltage is dangerous, and appropriate caution and expertise are paramount.
Jumper Wires and Breadboard: For prototyping and connecting components.
SD Card: For the Raspberry Pi’s OS.
Power Supply: For the Raspberry Pi.

Conceptual System Architecture:

The system could be designed to function as follows:

Sensor Data Input: The light, microphone, and PIR sensors would continuously monitor the environment.
Manual User Feedback: The physical button would allow the user to explicitly indicate their lighting preference, serving as direct input for the learning process.
Data Logging by Raspberry Pi: The Raspberry Pi would read sensor data and button states, timestamping each entry and recording the lamp’s current state.
Dataset Creation: Over time, this logged data would form a comprehensive dataset reflecting usage patterns.
Machine Learning Model Training: This dataset would be used to train a machine learning model. The model’s task would be to learn the relationship between sensor readings, button presses, and the desired lamp state.
Automated Control: Once trained, the model could run on the Raspberry Pi, taking real-time sensor data to predict and control the lamp’s on/off state.
Continuous Learning & Override: The physical button would always allow manual override. These overrides could ideally be logged as new training examples, allowing the model to be refined over time.

A Potential Roadmap for Implementation:

Bringing this idea to life would likely involve several key stages:

Stage 1: Hardware Setup

Raspberry Pi Configuration: This would involve installing Raspberry Pi OS and ensuring basic functionality and remote access (SSH or direct connection).
Sensor Interfacing:
- PIR Sensor: Connecting VCC, GND, and Data pins to Raspberry Pi GPIOs.
- Light Sensor: If using a photoresistor, this would necessitate a voltage divider and an ADC (Analog-to-Digital Converter) module connected to the Pi (e.g., via I2C or SPI). Digital light sensors could connect directly via I2C.
- Microphone: A USB microphone would connect to a USB port, while an I2S module would require connection to specific I2S GPIO pins.
- Physical Button: Wiring the button to a GPIO pin, likely with a pull-up or pull-down resistor configuration.
- Relay Module: Connecting the relay’s control pins to the Pi. The high-voltage side for the lamp would require careful and safe wiring, always with the lamp unplugged during setup and with a strong understanding of electrical safety.

Stage 2: Software Environment

Installing Libraries: Key Python libraries such as RPi.GPIO (for GPIOs), smbus (for I2C), pyaudio (for audio), pandas (for data management), and a machine learning library (scikit-learn or TensorFlow Lite) would need to be installed.
Individual Sensor Testing: Writing small Python scripts to confirm each sensor is working and data can be read correctly would be an important preliminary step. This includes testing relay control.

Stage 3: Data Collection Strategy

This phase is crucial for the AI’s effectiveness.

Developing a Data Logging Script: A Python script would be needed to:
- Periodically (e.g., every few seconds) read data from all sensors.
- Record the state of the physical button.
- Log the current lamp state (initially manual, based on button presses).
- Timestamp each entry.
- Save data to a structured file, like a CSV (e.g., columns: timestamp, light_level, sound_level, motion_detected, button_state, lamp_state).
Gathering Training Data: Running the logging script for an extended period (days or weeks) while using the lamp normally with the physical button would generate the necessary dataset. The button presses act as the «labels» for the AI.

Stage 4: Model Development and Training

Choosing a Model: Suitable classification models for a Raspberry Pi could include Logistic Regression, Decision Trees, Random Forests, or a lightweight Neural Network (potentially for TensorFlow Lite).
Data Preparation: This would involve cleaning the collected data (handling missing values), normalizing sensor readings, potentially engineering new features (e.g., time of day, average sound over a window), and splitting the data into training and testing sets.
Model Training: Using the prepared training data to train the chosen model. This could be done on the Pi for simpler models or on a more powerful machine for complex ones, with the trained model then transferred to the Pi.
Model Evaluation: Testing the model on the unseen test data to assess its predictive accuracy and identify areas for improvement.

Stage 5: Deployment and Automation

Loading the Model on Pi: Integrating the trained model into a Python script on the Raspberry Pi.
Creating an Inference Script: This script would:
- Continuously read and preprocess live sensor data.
- Feed data to the model to get a lamp state prediction.
- Control the lamp via the relay based on the prediction.
- Incorporate logic for the physical button to override the AI and potentially log this feedback for future retraining.
Automating Execution: Setting up the inference script to run automatically when the Raspberry Pi starts.

Ideas for Further Enhancement:

This core concept could be expanded with more advanced features:

Time-Based Context: Incorporating the time of day or day of the week as features for the model, as lighting needs often follow schedules.
Refined User Feedback Loop: Designing a system where manual overrides more directly contribute to periodic model retraining, allowing the lamp to adapt to changing habits.
Advanced Audio Analysis: Moving beyond simple sound levels to detect specific types of sounds (e.g., speech, keyboard activity) if relevant.
Optional Web Interface: A simple web interface could be developed for monitoring sensor data, viewing predictions, or manually controlling the lamp remotely.

This project idea, while ambitious, offers a fantastic opportunity to dive into data collection, feature engineering, machine learning, and embedded systems. It’s a journey of exploration and learning, culminating in a truly smart and personalized piece of desk equipment!