We present the design of a communication system that enables two devices to communicate using ambient RF as the only source of power. Our approach leverages existing TV and cellular transmissions to eliminate the need for wires and batteries, thus enabling ubiquitous communication where devices can communicate among themselves at unprecedented scales and in locations that were previously inaccessible.To achieve this, we introduce ambient backscatter, a new communication primitive where devices communicate by backscattering ambient RF signals. Our design avoids the expensive process of generating radio waves; backscatter communication is orders of magnitude more power-efficient than traditional radio communication. Further, since it leverages the ambient RF signals that are already around us, it does not require a dedicated power infrastructure as in traditional backscatter communication. To show the feasibility of our design, we prototype ambient backscatter devices in hardware and achieve information rates of 1 kbps over distances of 2.5 feet and 1.5 feet, while operating outdoors and indoors respectively. We use our hardware prototype to implement proof-of-concepts for two previously infeasible ubiquitous communication applications.

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Ambient backscatter: wireless communication out of thin air

Recent demand and interest in wireless, mobile-based healthcare has driven significant interest towards developing alternative biopotential electrodes for patient physiological monitoring. The conventional wet adhesive Ag/AgCl electrodes used almost universally in clinical applications today provide an excellent signal but are cumbersome and irritating for mobile use. While electrodes that operate without gels, adhesives and even skin contact have been known for many decades, they have yet to achieve any acceptance for medical use. In addition, detailed knowledge and comparisons between different electrodes are not well known in the literature. In this paper, we explore the use of dry/noncontact electrodes for clinical use by first explaining the electrical models for dry, insulated and noncontact electrodes and show the performance limits, along with measured data. The theory and data show that the common practice of minimizing electrode resistance may not always be necessary and actually lead to increased noise depending on coupling capacitance. Theoretical analysis is followed by an extensive review of the latest dry electrode developments in the literature. The paper concludes with highlighting some of the novel systems that dry electrode technology has enabled for cardiac and neural monitoring followed by a discussion of the current challenges and a roadmap going forward.

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Dry-Contact and Noncontact Biopotential Electrodes: Methodological Review

This paper presents a low-power SoC that performs EEG acquisition and feature extraction required for continuous detection of seizure onset in epilepsy patients. The SoC corresponds to one EEG channel, and, depending on the patient, up to 18 channels may be worn to detect seizures as part of a chronic treatment system. The SoC integrates an instrumentation amplifier, ADC, and digital processor that streams features-vectors to a central device where seizure detection is performed via a machine-learning classifier. The instrumentation-amplifier uses chopper-stabilization in a topology that achieves high input-impedance and rejects large electrode-offsets while operating at 1 V; the ADC employs power-gating for low energy-per-conversion while using static-biasing for comparator precision; the EEG feature extraction processor employs low-power hardware whose parameters are determined through validation via patient data. The integration of sensing and local processing lowers system power by 14× by reducing the rate of wireless EEG data transmission. Feature vectors are derived at a rate of 0.5 Hz, and the complete one-channel SoC operates from a 1 V supply, consuming 9 ? J per feature vector.

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A Micro-Power EEG Acquisition SoC With Integrated Feature Extraction Processor for a Chronic Seizure Detection System

This paper presents a low-power SoC that performs EEG acquisition and feature extraction required for continuous detection of seizure onset in epilepsy patients. The SoC corresponds to one EEG channel, and, depending on the patient, up to 18 channels may be worn to detect seizures as part of a chronic treatment system. The SoC integrates an instrumentation amplifier, ADC, and digital processor that streams features-vectors to a central device where seizure detection is performed via a machine-learning classifier. The instrumentation-amplifier uses chopper-stabilization in a topology that achieves high input-impedance and rejects large electrode-offsets while operating at 1 V; the ADC employs power-gating for low energy-per-conversion while using static-biasing for comparator precision; the EEG feature extraction processor employs low-power hardware whose parameters are determined through validation via patient data. The integration of sensing and local processing lowers system power by 14x by reducing the rate of wireless EEG data transmission. Feature vectors are derived at a rate of 0.5 Hz, and the complete one-channel SoC operates from a 1 V supply, consuming 9 μJ per feature vector.

This work introduces the use of compressed sensing (CS) algorithms for data compression in wireless sensors to address the energy and telemetry bandwidth constraints common to wireless sensor nodes. Circuit models of both analog and digital implementations of the CS system are presented that enable analysis of the power/performance costs associated with the design space for any potential CS application, including analog-to-information converters (AIC). Results of the analysis show that a digital implementation is significantly more energy-efficient for the wireless sensor space where signals require high gain and medium to high resolutions. The resulting circuit architecture is implemented in a 90 nm CMOS process. Measured power results correlate well with the circuit models, and the test system demonstrates continuous, on-the-fly data processing, resulting in more than an order of magnitude compression for electroencephalography (EEG) signals while consuming only 1.9 μW at 0.6 V for sub-20 kS/s sampling rates. The design and measurement of the proposed architecture is presented in the context of medical sensors, however the tools and insights are generally applicable to any sparse data acquisition.

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Design and Analysis of a Hardware-Efficient Compressed Sensing Architecture for Data Compression in Wireless Sensors

Recent advances in the medical field are spurring the need for ultra-low power transceivers for wireless communication with medical implants. To deal with the growing demand for medical telemetry, the FCC commissioned the medical implant communications services (MICS) standard in 1999 in the 402-405 MHz band. This paper presents a 350 muW FSK/MSK direct modulation transmitter and a 400 muW OOK super-regenerative receiver (SRR) specifically optimized for medical implant communications. The transceiver is implemented in 90 nm CMOS and digitally tunes 24 MHz in frequency steps smaller than 2 kHz. The transmitter meets MICS mask specifications with data rates up to 120 kb/s consuming only 2.9 nJ/bit; the receiver has a sensitivity better than -99 dBm with a data rate of 40 kb/s or -93 dBm with a data rate of 120 kb/s consuming 3.3 nJ/bit. A frequency correction loop incorporating the base-station is prototyped to eliminate the need for a frequency synthesizer in the implant while still achieving frequency stability of less than 3 ppm.

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A 350 $\mu$ W CMOS MSK Transmitter and 400 $\mu$ W OOK Super-Regenerative Receiver for Medical Implant Communications

A 350 muW MSK direct modulation transmitter and a 400 muW OOK super-regenerative receiver (SRR) are implemented in 90 nm CMOS technology. The transceiver tunes 24 MHz in frequency steps smaller than 2 kHz and is designed to meet the specifications of the Medical Implant Communications Service (MICS) standard in the 402-405 MHz band. The transmitter meets MICS mask specifications with data rates up to 120 kbps, and the receiver has a sensitivity better than -99 dBm with a data rate of 40 kbps or -93 dBm with a data rate of 120 kbps.

A 350μW CMOS MSK transmitter and 400μW OOK super-regenerative receiver for Medical Implant Communications

A compact, low-power, digitally-assisted sensor interface for biomedical applications is presented. It exploits oversampling and mixed-signal feedback to reduce system area and power, while making the system more robust to interferers. Antialiasing is achieved using a charge-sampling filter with a sinc frequency response and programmable gain. A mixed-signal feedback loop creates a sharp, programmable notch for interference cancellation. A prototype was implemented in a 0.18-μm CMOS process. The on-chip blocks operate from a 1.5-V supply and consume between 255 nW and 2.5 μW depending on noise and bandwidth requirements.

Jose L. Bohorquez

Papers

A Micro-Power EEG Acquisition SoC With Integrated Feature Extraction Processor for a Chronic Seizure Detection System

A Micro-Power EEG Acquisition SoC With Integrated Feature Extraction Processor for a Chronic Seizure Detection System

A 350 $\mu$ W CMOS MSK Transmitter and 400 $\mu$ W OOK Super-Regenerative Receiver for Medical Implant Communications

A 350μW CMOS MSK transmitter and 400μW OOK super-regenerative receiver for Medical Implant Communications

A Biomedical Sensor Interface With a sinc Filter and Interference Cancellation