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The algorithm detects intrusions and break-ins by analyzing environmental audio and visual cues such as breaking glass, crying babies, home alarms, car alarms, and more. Additionally, it can identify and analyze falls in real time, particularly for elderly individuals, without requiring wearable sensors. This application can be integrated into security surveillance systems to enhance emergency response times, whether triggered by an alarm or when someone is in pain or distress.

The algorithm detects signs of distress and aggression in human voices, as well as recognizable danger sounds and visual sense in the environment, such as screaming, gunshots, crying babies, breaking glass, barking dogs, and more. Once these sounds are classified, the system alerts security or law enforcement through visual and audio notifications, enabling swift response to prevent aggression or harm. Our algorithm can seamlessly integrate with video surveillance systems to provide automated detection of such incidents.

The algorithm’s compatibility with a wide range of platforms allows it to be installed on any smartphone. Once connected, the smart algorithm can link the phone to security systems, detecting household alarms such as doorbells, smoke alarms, theft alarms, appliance timers, and more. This application is designed primarily to assist deaf individuals by enabling them to “see” sounds through visual notifications.

Our algorithm, integrated into the SnoRelief app on a smartphone, analyzes snoring patterns to assess the potential risk of sleep apnea, helping users self-monitor and seek medical assistance if necessary. Additionally, it detects heartbeat and breathing sounds via a digital stethoscope, identifying any abnormal patterns and notifying doctors when needed. The algorithm also analyzes cough sounds, recognizing unique patterns that could indicate health issues, allowing for timely medical alerts to healthcare professionals.

The algorithm classifies environmental noises from domestic activities, identifying your location based on surrounding sounds such as parks, streets, restaurants, pubs, airplanes, or common household sounds like a TV, laughter, vacuum cleaner, shaving, cooking, showering, snoring, crying, and more.

Receive notifications when home appliances around you beep, such as egg timers, microwaves, oven timers, dishwashers, washing machines, stoves, and other kitchen appliances. The system ensures you never miss important alerts from your household devices.

Our smart acoustic sensors can detect potential faults in machinery by analyzing acoustic emissions generated by the machine before a failure occurs. By continuously monitoring sound patterns, movement, and vibrations in real time, the sensors can identify early warning signs of mechanical issues, such as wear and tear or misalignment. This predictive capability helps prevent costly breakdowns and downtime by allowing for timely maintenance. Additionally, the system provides detailed analytics and alerts, empowering businesses to optimize machinery performance and improve overall operational efficiency.

Our system detects and analyzes slab track failures in real time using acoustic and vibration sensors mounted on the train axle or tracks, enabling predictive maintenance and serving as an early warning system. Key features include a platform crossing detector to prevent illegal or dangerous track crossings. The algorithm and sensors monitor and analyze various track failures, such as gauge and geometry deviations at the sub-millimeter level, overhead line obstructions, downed or split wires, bearing damage, flat wheels, and braking system issues. Additionally, sensors on train platforms detect individuals attempting to cross or jump onto the tracks, alerting operators with lights and automated warnings to enhance safety.