SBIR-STTR Award

The broader impact/commercial potential of this project is to enable continuous sensing applications in a wide range of energy-constrained devices like smartphones, smartwatches, and other wearables. Without dramatic innovations in the development of ultralow power sensory processing, continuous sensing will remain a niche application limited to environments with a stable and plentiful power source. The technology described in this proposal will demonstrate the viability and potential widespread deployment of continuous sensing devices in mobile or remote environments with strict energy constraints. An important immediate market for the proposed technology with significant customer base is the smartphone and wearables market, where many new and emerging end user applications could leverage environmental sensing to trigger context-based and anticipatory actions. The proposed technology is broadly applicable to a number of other markets and domains, including medical, health, and safety monitoring of critical patient sensors, personal fitness devices, military applications, and environmental monitors. The ability to flexibly deploy continuous sensing for these and other applications has the potential to revolutionize these markets and create entirely new and unforeseen application domains.

This Small Business Innovation Research (SBIR) Phase I project will develop a novel platform
technology--inspired by the mammalian neural pathways, which process sensory information--to enable
?always listening? audio processing capability within energy-constrained devices such as smartphones, tablets, smart watches, and other sensor-enabled wearable devices. The core technology, which provides the sensor processing capability, will model many of the computational properties and capabilities of the biological neurons that humans use to analyze information from their various senses. This research will allow a broad range of devices to continuously monitor their microphones, using streaming audio input to interpret various user commands, infer the current context and needs of the user from background noise, and even identify dangerous and harmful situations heard by the device. The research will target deployment of these capabilities on low power microprocessors and microcontrollers, often referred to as sensor hubs, which are included in many modern smartphones, tablets, and wearables. Targeting deployment on the sensor hubs will enable ?always listening? devices without compromising the battery life of the device.

The broader impact/commercial potential of this project is to enable continuous sensing applications in a wide range of energy-constrained sensor-enabled devices. Without dramatic innovations in the development of ultralow power sensory processing, continuous and accurate sensing will remain a niche application limited to environments with a stable and plentiful power source and significant computing resources. The technology described in this proposal will demonstrate the viability and potential widespread deployment of continuous sensing devices in mobile or remote environments with strict energy constraints. An important immediate market for the proposed technology with significant customer base is the smartphone and wearables market, where many new and emerging end user applications could leverage environmental sensing to trigger context-based and anticipatory actions. The proposed technology is broadly applicable to a number of other markets and domains, including medical, health, and safety monitoring of critical patient sensors, personal fitness devices, military applications, and environmental monitors. The ability to flexibly deploy continuous sensing for these and other applications has the potential to revolutionize these markets and create entirely new and unforeseen application domains. This Small Business Innovation Research Phase 2 Project plans to research and develop algorithms based on the properties of biological spiking neurons and the sensory processing capabilities of the human brain. The human brain is truly unique in its ability to use a basic computational element, the spiking neuron, and perform a broad variety of tasks. The brain has the ability to accurately classify sensory patterns from multiple modalities (touch, sight, etc.), to interpret the outside world, and to recognize the current context. A key intellectual merit of this project is a demonstration of how these novel neural algorithms can perform accurate, robust, and low power sensory analysis across multiple sensory domains. Just as the brain is capable of processing data from very different sensors. Researching and developing these neural algorithms will provide insight as to how the human brain learns to recognize important sensory information, how it is able to integrate information from such different sense modalities, and how it is able to perform complex analysis so efficiently.