# ESP32-S3 Edge-AI｜Human Activity Recognition Using Accelerometer Data and ESP-DL

DevFeed: [ESP32-S3 Edge-AI｜Human Activity Recognition Using Accelerometer Data and ESP-DL](<https://devfeed.tech/articles/esp32-s3-edge-ai-human-activity-recognition-using-accelerometer-data-and-esp-dl-13861.md>)

Original publisher: [Read original article](<https://developer.espressif.com/blog/esp32-s3-edge-ai-human-activity-recognition-using-accelerometer-data-and-esp-dl/>)

Author: John Lee

Published: 2023-06-06T00:00:00Z

Content type: tutorial

Language: en

Sources: [Blog on Developer Portal](<https://devfeed.tech/sources/blog-on-developer-portal.md>)

Topics: [ESP32-S3](<https://devfeed.tech/topics/esp32-s3.md>), [On-device AI](<https://devfeed.tech/topics/on-device-ai.md>), [Deep neural networks](<https://devfeed.tech/topics/deep-neural-networks.md>), [ESP-IDF](<https://devfeed.tech/topics/esp-idf.md>), [Espressif](<https://devfeed.tech/topics/espressif.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [blog](<https://devfeed.tech/tags/blog.md>), [edge-ai](<https://devfeed.tech/tags/edge-ai.md>), [edge-computing](<https://devfeed.tech/tags/edge-computing.md>), [esp](<https://devfeed.tech/tags/esp.md>), [esp-dl](<https://devfeed.tech/tags/esp-dl.md>), [esp-idf](<https://devfeed.tech/tags/esp-idf.md>), [esp32-s3](<https://devfeed.tech/tags/esp32-s3.md>), [espressif](<https://devfeed.tech/tags/espressif.md>), [neural-networks](<https://devfeed.tech/tags/neural-networks.md>), [recognition](<https://devfeed.tech/tags/recognition.md>)

## AI overview

This tutorial explains how to read accelerometer data and deploy a deep-learning model with ESP-DL on an ESP32-S3 for human activity recognition. It covers prerequisites, ESP-IDF project structure, model definition, layer initialization, and layer construction.

## Source excerpt

Edge computing is a distributed computing paradigm that brings computation and data storage closer to the device's location. Edge Artificial Intelligence (edge-AI) is an exciting development within edge computing because it allows traditional technologies to run more efficiently, with higher performance and less power. Trained neural networks are used to make inferences on small devices.