Analog Ultra Low-Power Acoustic Wake-Up System Based on Frequency Detection

Fourniol, Manon; Giès, Valentin; Barchasz, Valentin; Kussener, Edith; Barthélémy, H.; Vauché, R.; Glotin, Hervé

doi:10.1109/iotais.2018.8600849

Cited by 8 publications

(8 citation statements)

References 23 publications

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“…Sound wake-up detector relies on detecting sound waves and can be based on frequency detection [ 55 ]. We consider two scenarios for sound wake-up: trigger from the person or the environment and device to device trigger.…”

Section: Remote and Environment Triggeringmentioning

confidence: 99%

“…This microphone has sleep mode with a power consumption of only 6

. While the wake-up signal is lost after the wake-up circuit is activated [ 55 ], the sound generated during an accident is one of the most valuable pieces of information. Losing this sound limits the system’s ability to identify the event.…”

Section: Remote and Environment Triggeringmentioning

confidence: 99%

“…Instead, they require proximity for the sound signal to travel from the source and be detected by the target device. However, the solution presented in [ 55 ], which meets the power requirements, is too complex and costly to be implemented in the simple sensor modules.…”

Section: Remote and Environment Triggeringmentioning

confidence: 99%

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Rural Healthcare IoT Architecture Based on Low-Energy LoRa

Dimitrievski

Filiposka

Melero

et al. 2021

IJERPH

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Connected health is expected to introduce an improvement in providing healthcare and doctor-patient communication while at the same time reducing cost. Connected health would introduce an even more significant gap between healthcare quality for urban areas with physical proximity and better communication to providers and the portion of rural areas with numerous connectivity issues. We identify these challenges using user scenarios and propose LoRa based architecture for addressing these challenges. We focus on the energy management of battery-powered, affordable IoT devices for long-term operation, providing important information about the care receivers’ well-being. Using an external ultra-low-power timer, we extended the battery life in the order of tens of times, compared to relying on low power modes of the microcontroller.

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Section: Remote and Environment Triggeringmentioning

confidence: 99%

“…This microphone has sleep mode with a power consumption of only 6

Section: Remote and Environment Triggeringmentioning

confidence: 99%

See 1 more Smart Citation

Rural Healthcare IoT Architecture Based on Low-Energy LoRa

Dimitrievski

Filiposka

Melero

et al. 2021

IJERPH

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“…Refs. [25,26] have a wide input frequency range, but are based on digital or analog discrete circuits making their power consumption higher than previous implementations (34 µW). However, they can be used for detecting an adjustable specific frequency in a narrow band, making them interesting for several applications, but cannot implement cetacean detection expert rules.…”

Section: Keys To Ultra-low Power Monitoring Systemmentioning

confidence: 99%

A 30 μW Embedded Real-Time Cetacean Smart Detector

et al. 2021

Self Cite

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Cetacean monitoring is key to their protection. Understanding their behavior relies on multi-channel and high-sampling-rate underwater acoustic recordings for identifying and tracking them in a passive way. However, a lot of energy and data storage is required, requiring frequent human maintenance operations. To cope with these constraints, an ultra-low power mixed-signal always-on wake-up is proposed. Based on pulse-pattern analysis, it can be used for triggering a multi-channel high-performance recorder only when cetacean clicks are detected, thus increasing autonomy and saving storage space. This detector is implemented as a mixed architecture making the most of analog and digital primitives: this combination drastically improves power consumption by processing high-frequency data using analog features and lower-frequency ones in a digital way. Furthermore, a bioacoustic expert system is proposed for improving detection accuracy (in ultra-low-power) via state machines. Power consumption of the system is lower than 30 μW in always-on mode, allowing an autonomy of 2 years on a single CR2032 battery cell with a high detection accuracy. The receiver operating characteristic (ROC) curve obtained has an area under curve of 85% using expert rules and 75% without it. This implementation provides an excellent trade-off between detection accuracy and power consumption. Focused on sperm whales, it can be tuned to detect other species emitting pulse trains. This approach facilitates biodiversity studies, reducing maintenance operations and allowing the use of lighter, more compact and portable recording equipment, as large batteries are no longer required. Additionally, recording only useful data helps to reduce the dataset labeling time.

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“…The key emphasis in the design of wake-up detectors is on low power consumption, cheap, simple design, and accurate detection [7,[11][12][13][14] to ensure low false detection rates, even in the most adverse conditions, as false event detections increase the overall system's power consumption by causing unnecessary activations of the main stage.…”

Section: Introductionmentioning

confidence: 99%

Features and Always-On Wake-Up Detectors for Sparse Acoustic Event Detection

2022

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The need to understand and manage our surroundings has led to increased interest in sensor networks for the continuous monitoring of events and processes of interest. To reduce the power consumption required for continuous monitoring, dedicated always-on wake-up detectors have been designed, with an emphasis on their low power consumption, simple and robust design, and reliable and accurate detection. An especially interesting application of these wake‑up detectors is in detecting acoustic signals. In this paper, we present a study on the features and detectors applicable for the detection of sporadic acoustic events. We perform a state-of-the-art acoustic detector analysis, grouping the detectors based on the features they utilize and their implementations. This analysis shows that acoustic wake-up detectors predominantly utilize spectro-temporal (56%) and temporal features (36%). Following the state-of-the-art analysis, we select two detector architecture candidates for a case study on passing motor vehicle detection. We utilize our previously developed spectro-temporal decomposition detector and develop a novel level-crossing rate detector. The results of the case study shows that the proposed level‑crossing rate detector has lower component count (44 compared to 70) and power consumption (9.1 µW compared to 34.6 µW) and is an optimal solution for SNRs over 0 dB.

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Analog Ultra Low-Power Acoustic Wake-Up System Based on Frequency Detection

Cited by 8 publications

References 23 publications

Rural Healthcare IoT Architecture Based on Low-Energy LoRa

Rural Healthcare IoT Architecture Based on Low-Energy LoRa

A 30 μW Embedded Real-Time Cetacean Smart Detector

Features and Always-On Wake-Up Detectors for Sparse Acoustic Event Detection

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