Imagimob, a Swedish startup driving innovation at the forefront of Edge AI is using Texas Instruments mmWave Radar Sensors and *tinyML for two new applications, fall detection and gesture recognition. The fall detection algorithm uses a low-cost, low-power radar sensor placed on the wall in a room or an appliance, and the tinyML will detect if a person in the room falls down.
The gesture recognition application recognises 6 different predefined gestures that can be used for human machine interface in automotive and industrial settings.
Fall detection adds value to different appliances and products by adding health monitoring benefits. It can be used in products inside nursing homes, factories or personal homes. Fall detection application-enabled equipment can help save lives.
Gesture recognition enables functionality with a touchless interface. Traditional interfaces require buttons / surfaces which take space and physical touch to provide inputs. This also results in breakdowns due to wear and tear and requires cleaning. Instead, one can use a compact radar and gesture recognition application to eliminate the hassle while also enjoying activating the functionality from a distance.
The applications are supported by the two companies using the Imagimob tinyML platform and IWR6843 mmWave radar from Texas Instruments. The performance of the applications is very good, and the purpose of the applications is to give customers a head-start and significantly shorten the time to make the applications production-ready.
A user can download Imagimob AI for free from the Imagimob website, and the two applications are included in the platform as starter projects. The user can be up and running in minutes developing and testing the applications. TI mmWave evaluation boards for IWR6843 and IWR6843AOP are also available for purchase.
Imagimob AI is a tinyML end-to-end development platform for machine learning on edge devices. It allows developers to go from data collection to deployment on an edge device in minutes. Imagimob AI is used by many customers to build production-ready models for a range of use cases including Sound Event Detection, audio, gesture recognition, human motion, predictive maintenance, material detection and many more.
*tinyML is an abbreviation for tiny machine learning and means that machine learning algorithms are processed locally on embedded devices using the smallest microcontrollers (MCU’s).