Application Library

Explore SensiML's reference applications, demos, and datasets

Audio Anomaly Detection

In this tutorial, we are going to build a predictive maintenance application with an unknown anomaly state for a fan that can run entirely on a cortex-M4 microcontroller using SensiML Analytics Toolkit.

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Audio Cough Detection

In this tutorial, we are going to build a Cough Detection application that can run entirely on a cortex-M4 microcontroller using SensiML Analytics Toolkit.

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Canine Activity Recognition Collar

SensiML's canine activity recognition reference dataset uses a collar-mounted wearable device to classify 9 different activities for over 40 dog breeds using IMU motion data.

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Boxing Punch Activity Recognition

We build a Boxing punch recognition application that can run entirely on a Cortex-M4 microcontroller using SensiML and TensorFlow Lite for Microcontrollers.

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Guitar Note Audio Recognition

We use SensiML Analytics Toolkit to build a model that can run entirely on a microcontroller to classify guitar tuning notes.

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Keyword Spotting

This tutorial is a step-by-step guide on how to build an audio keyword spotting application using the SensiML keyword spotting pipeline.

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Fan State Condition Monitoring

This demo illustrates the overall process of using SensiML Analytics Toolkit to build a fan state recognition model for an axial cooling fan.

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Robot Arm Motion Recognition and Anomaly Detection

A robotic motion analysis device that can be trained to alert in real-time to unprogrammed movements, unexcepted collisions, human interference, or other motion path anomalies.

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Smart Lock Audio Recognition

This tutorial is a guide towards building a smart lock model that can be executed on a cortex-M4 microcontroller.

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Vibration Anomaly Detection

We build a predictive maintenance application with an unknown anomaly state for a fan that can run entirely on a cortex-M4 microcontroller.

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Wizard Wand Gesture Recogition Game

SensiML's Wizard Wand game provides an example of a complete application built around on-device, ML-based gesture recognition as the central feature of the game.

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