05/10/22

Imagimob Announces tinyML for Fall Detection and Gesture Recognition Applications using Texas Instruments mmWave Radar Sensors

Imagimob announces tinyML for two new applications based on mmWave radar sensors from Texas Instruments, 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 recognizes 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 an 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, 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). 

 

Imagimob will demonstrate the applications at Embedded World 2022, Hall 4, Stand 133 that is held on June 21-23 in Nuremburg, Germany.

 

Contact:

Imagimob: Anders Hardebring, CEO and Co-founder, email: anders@imagimob.com, phone: +46 705910614

 

About Imagimob

Imagimob is a fast growing startup driving innovation at the forefront of Edge AI and tinyML—and enabling the intelligent products of the future. Based in Stockholm, Sweden, the company has been serving global customers within the automotive, manufacturing, healthcare, and lifestyle industries since 2013. In 2020, Imagimob launched Imagimob AI – a development platform for machine learning on edge devices. Imagimob AI guides and empowers users throughout the entire development journey, resulting in game-changing productivity and faster time-to-market. Learn more at www.imagimob.com

 

About Texas Instruments

Texas Instruments Incorporated (Nasdaq: TXN) is a global semiconductor company that designs, manufactures, tests and sells analog and embedded processing chips for markets such as industrial, automotive, personal electronics, communications equipment and enterprise systems. Our passion to create a better world by making electronics more affordable through semiconductors is alive today, as each generation of innovation builds upon the last to make our technology smaller, more efficient, more reliable and more affordable – making it possible for semiconductors to go into electronics everywhere. We think of this as Engineering Progress. It’s what we do and have been doing for decades. Learn more at TI.com.

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