Enhancing Smart Homes with Pose Detection Using Neuromorphic Computing and Edge AI

Ravi Rao

22 May, 2024

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Pose detection technology leverages advanced machine learning algorithms to interpret human movements in real-time, enabling seamless, intuitive device control through simple gestures.

Artificial Intelligence

- Electronics

- Embedded Machine Learning

- Embedded Systems

- Microcontroller

Introduction

Edge AI transforms smart home technology by enabling real-time data processing directly on devices, reducing latency, and enhancing privacy. In home automation, this leads to more responsive and efficient control systems. One notable application is gesture recognition through pose detection, which allows users to control devices with simple movements.

This article features a project on developing a gesture-based appliance control system using the BrainChip Akida Neural Processor AKD1000 SoC and the Edge Impulse platform. We'll discuss hardware and software requirements, the setup process, data collection, model training, deployment, and practical demonstrations. Additionally, we'll explore integrating the system with Google Assistant for enhanced functionality.

Edge AI in Home Automation

In home automation, Edge AI enables smart devices to respond quickly to user inputs and environmental changes. This local processing power is crucial for applications requiring immediate feedback, such as security systems, smart lighting, and environmental controls.

By processing data at the edge, smart home devices can operate independently of an internet connection, ensuring continuous functionality. This also reduces the risk of data breaches as sensitive information remains within the local network.

Pose Detection with Edge AI

Pose detection is a technology that captures and analyzes human body movements and postures in real time. Using machine learning algorithms, pose detection systems identify key points on the human body, such as joints and limbs, and track their positions and movements. This data can then be used to recognize specific gestures and postures, enabling intuitive, hands-free interaction with various devices.

Pose detection typically involves several steps:

1. Image Capture: A camera or other sensor captures images or video of the user.

2. Preprocessing: The captured images are processed to enhance quality and remove noise.

3. Key Point Detection:Machine learning models identify and track key points on the body, such as elbows, knees, and shoulders.

4. Pose Estimation: The system estimates the user's pose by analyzing the positions and movements of the detected key points.

5. Gesture Recognition:Specific gestures are identified based on predefined patterns in the user's movements.

Pose detection has a wide range of applications beyond home automation, including:

• Gaming: Enhancing user experience with motion-controlled games.

• Healthcare: Monitoring patients' movements and posture for rehabilitation and physical therapy.

• Fitness: Providing real-time feedback on exercise form and performance.

• Security: Recognizing suspicious behavior in surveillance systems.

In home automation, pose detection can be particularly powerful, turning everyday tasks into seamless, interactive experiences, and enhancing the overall functionality and appeal of smart homes. In this context, the project "Gesture Appliances Control with Pose Detection" stands out as a great example of how pose detection can be used for home automation. Developed by Christopher Mendez, this innovative idea leverages the BrainChip AKD1000 to enable users to control household appliances with simple finger-pointing gestures.

Further reading: Gesture Recognition and Classification Using Infineon PSoC 6 and Edge AI

By combining neuromorphic processing with machine learning, the system achieves high accuracy and low power consumption, making it a practical and efficient solution for modern smart homes.

Gesture Appliances Control with Pose Detection - BrainChip AKD1000

Control your TV, Air Conditioner or Lightbulb by just pointing your finger at them, using the BrainChip AKD1000 achieving great accuracy and low power consumption.

Created By: Christopher Mendez

Public Project Link: Edge Impulse Experts / Brainchip-Appliances-Control-Full-Body

Introduction

Sometimes, it can be inconvenient to have to ask a personal assistant to turn our appliances on or off. It may be because it's simply too late at night to talk, or because we're watching our favorite movie and don't want annoying audio interrupting us.

This is why I thought, "What if we could control the whole house with just gestures?" It would be amazing to just point to the air conditioner and turn it on, turn off the light, and turn on our TV.

Hardware and Software Requirements

To develop this project, we will use a BrainChip Akida Development Kit, a Logitech BRIO 4K Webcam, and an Edge Impulse Machine Learning model for pose identification.

Fig. 1: Hardware required for the project

Akida Dev Kit

It should be noted that this kit is the main component of this project, thanks to some interesting characteristics that make it ideal for this use case. This kit consists of a Raspberry Pi Compute Module 4 with Wi-Fi and 8 GB RAM, its IO Board, which includes a PCIe interface to carry an Akida PCIe boardwith the AKD1000 Neuromorphic Hardware Accelerator.

Considering that our project will end up being one more smart device we will have at home, it's crucial that it can do its job efficiently and with very low energy consumption. This is where BrainChip's technology makes sense. Akida™ neuromorphic processor mimics the human brain to analyze only essential sensor inputs at the point of acquisition—processing data with unparalleled performance, precision, and energy economy.

Software

The whole system will be running independently identifying poses, if a desired pose is detected it will send an HTTP request to the Google Assistant SDK being hosted by a Raspberry Pi with Home Assistant OS