Battery-Powered AI/ML Based Smart Alarm System

By Sharad Bhowmick

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Infineon Technologies has released a battery-powered Smart Alarm System (SAS). The tech platform is the industry’s first battery-powered AI/ML-based acoustic event detection system with sensor fusion. The solution incorporates Infineon’s analog XENSIV MEMS microphone IM73A135V01, XENSIV digital pressure sensor DPS310 and PSoC 62 microcontroller. It features a low-power wake-on acoustic event detector that improves the battery life of the device. The compact design provides a high level of accuracy in detection and better battery life compared to that of acoustic-only alarm systems which are commonly used in smart buildings and homes, and other IoT applications.

“We are excited to enable a unique and differentiated approach to bring AI/ML capabilities to cost-sensitive, battery-powered home security sensor systems, without sacrificing battery life,” said Laurent Remont, Vice President of IoT and Sensor Solutions at Infineon’s Power & Sensor Systems Division. “Current home security solutions are unreliable for detecting events such as glass break. Our new solution combines a number of best-in-class technologies to create an alarm system that is smart, reliable and power efficient. We look forward to bringing more innovative solutions into the home security market.”

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According to the company, the technology platform achieves high accuracy and very low-power operation using sensor fusion based on artificial intelligence/machine learning (AI/ML). The solution incorporates Infineon’s high signal-to-noise ratio (SNR) analog XENSIV MEMS microphone IM73A135V01, XENSIV digital pressure sensor DPS310 and PSoC 62 microcontroller. Higher accuracy is achieved by utilising Infineon’s in-house sensor fusion software algorithm which is based on precisely trained AI/ML that combines acoustic and pressure sensor data to accurately differentiate between different types of sounds such as sharp sounds inside a home and distinctive audio/pressure event. These events can be created when glass is broken, a house alarm is triggered due to a smoke alarm, a carbon monoxide alarm or an intrusion is detected through a door or window. The AI/ML sensor fusion algorithm is also capable of eliminating many other background sounds or background pressure events that can generate false positives due to the similarities to alarm systems.


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