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Starter Projects

Creating a model from a scratch can be challenging and time consuming. Instead of building a machine learning model from scratch, you can use the starter projects. Imagimob Studio provides a wide range of starter projects that help you to start training and deploying machine learning models quickly.

A starter project allows you to use a machine learning model that is already trained by Imagimob for different scenarios. You can use the starter project as:

  • a starting point and fine-tune the model as per your requirement
  • an inspiration and collect your own data to build a similar project
  • deploy the pre-trained model to an edge device and evaluate the performance

A starter project directory contains the following sub-folders, tools and resources:

  • Data folder: contains the sample data
  • Models folder: contains the trained model (.h5 Tensorflow model) and edge code ( neural network model and preprocessor translated to C code) ready for integration into the embedded firmware
  • PreprocessorTrack folder: contains the sample predictions from the model
  • Readme.md file: describes the project and data in detail
  • Project file: contains the necessary resources to build the machine learning model

The starter projects are intended to be a demonstration of how you can build a machine learning model using Imagimob Studio, different types of sensors and development boards.

Choose your Starter project

Imagimob provides the following pre-trained starter projects:

Infineon starter projects

The following starter project are built using the Infineon development boards.

  • Human activity recognition

The human activity recognition starter project provides a machine learning model that predicts human activites such as running, standing, walking, sitting, jumping. The project uses the supported Infineon MCU with a BMI160 Inertial Measurement Unit (IMU), setup to collect data at 50 Hz using ± 8g for the accelerometer scale and ± 500 dps for the gyroscope scale.

  • Baby cry detection

The baby cry detection starter project provides a machine learning model that detects a baby cry with a microphone. The project uses 16kHz mono to record different babies sounds and background noise.

  • Siren detection

The siren detection starter project provides a machine learning model that detects a siren. The project uses the Infineon AURIX™ TC375 Lite Kit Board + KITA2G Audio Shield Board which is ideally suitable for automotive and industrial applications. The project uses at 16-bit PCM mono to record siren sounds, non-siren sounds, and background noise with a sampling rate of 16KHz.

Generic and Syntiant starter projects

The following starter project are are built using the Syntiant and generic development boards.

  • Human activity recognition

The human activity recognition starter project provides a machine learning model that predicts human activites such as running, standing, walking, sitting, jumping. The project uses the BMI160 Inertial Measurement Unit (IMU), setup to collect data at 50 Hz using ± 8g for the accelerometer scale and ± 500 dps for the gyroscope scale.

  • Keyword spotter

The keyword spotter project provides a machine learning model that recognise keywords like Up and Down with a microphone. The project uses 16kHz mono to record multiple recordings from different people and/or background noise.

  • Fall detection

The fall detection starter project provides a machine learning model that detects fall using an Inertial Measurement Unit (IMU) mounted on the buckle of a belt. The project uses Bosh and ST-Microelectronics IMU to collect data at 50 Hz using ± 8g for the accelerometer scale and ± 500 dps for the gyroscope scale.

  • Indoor or outdoor detection

The indoor or outdoor detection starter project provides a machine learning model that detects whether a person is indoor or outdoor using the environmental sensors. The project uses the Nordic semiconductor, Nordic Thingy:91 to collect environmental sensor data such as air quality, air pressure, humidity and temperature at different locations in Sweden during the course of two weeks.

Acconeer radar gesture starter project

The Acconeer radar gesture starter project provides a machine learning model that recognize gestures such as push, wiggle and vertical finger rotation. The project uses the Acconeer A1 radar, which collects data at a sampling rate of 39 Hz. The radar sensor is alow power, high precision, pulsed short-range sensor with a footprint of only 29 mm[^2]. The sensor is configured for a distance of 7-20 cm.

Texas Instruments radar gesture starter project

The Texas Instruments radar gesture starter project provides a machine learning model that recognize gestures such hand swipe left to right and right to left, finger rotation clockwise and counter-clockwise. The project uses the Texas Instruments mmWave Radar, IWR6843AOP and AWR6843AOP, which are single chip 60 GHz to 64 GHz radar sensors integrating DSP, MCU and radar accelerator for automotive and industrial applications. The project uses incoming radar data (the cartesian coordinates and speed) of the closest objects to classify different gestures. The data is collected with a sampling rate of 10 Hz.

Renesas touchpad letter detection starter project

The Renesas touchpad letter detection starter project provides a machine learning model that recognise letters written on touchpad. The project uses the Renesas RA2L1 board with KT-CAP1-MATRIXPAD as a capacitive touch input. The capacitive touchpad is used to collect data at a rate of 44 Hz.

Power generation prediction starter project

The power generation prediction starter project provides a machine learning model that predicts the power generated by a wind turbine by analyzing various atmospheric and technical conditions. The original dataset for this project is sourced from Kaggle platform (opens in a new tab). To ensure the data quality and usability, we performed several preprocessing steps, including interpolating missing values, removing rows with missing values, selecting relevant features, normalizing feature data, and partitioning the data into blocks. The cleaned and organized data was then utilized to build and train the machine learning model, enhancing its predictive accuracy and reliability.