A 74-<i>μ</i>W 11-Mb/s Wireless Vital Signs Monitoring SoC for Three-Lead ECG, Respiration Rate, and Body Temperature

Luo, Yuxuan; Teng, Kok-Hin; Li, Yongfu; Mao, Wei; Lian, Yong; Heng, Chun-Huat

doi:10.1109/tbcas.2019.2922295

Cited by 36 publications

(7 citation statements)

References 31 publications

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“…The leakage power is limited by the nanoscale transistors in the implemented process and can be further improved with advanced techniques or process [33]. The temporal resolution is typically limited by the sampling period (e.g., 33 μs in [6] and [34]) in conventional frame-based sampling systems [35]. The temporal resolution in feature extraction mode is measured by overlaying multiple of extracted R labels of the CAP and measuring the timing uncertainty, as shown in Fig.…”

Section: Measurement Resultsmentioning

confidence: 99%

“…The high-precision raw sensor data sampled by the Nyquist ADCs are sent wirelessly to a remote hub to perform further data processing. However, such "frame-based" sampling produces a large amount of data from neural recording, which will consume high wireless transmission energy [6], [7]. In addition, performing the computation remotely may introduce potential privacy concerns.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Implantable Neuromorphic Sensing System Featuring Near-Sensor Computation and Send-on-Delta Transmission for Wireless Neural Sensing of Peripheral Nerves

Corradi

Shi

et al. 2022

IEEE J. Solid-State Circuits

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Section: Measurement Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Implantable Neuromorphic Sensing System Featuring Near-Sensor Computation and Send-on-Delta Transmission for Wireless Neural Sensing of Peripheral Nerves

Corradi

Shi

et al. 2022

IEEE J. Solid-State Circuits

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“…sensory and motor cortex interaction) require high channel counts of neural recording with fine spatial and temporal resolutions [18,19], generating vast amounts of recording data, which impose significant limitations on the real-time applicability of neural decoders, see figure 2. Transferring data from the intra-cortical neural sensors to an external system requires energy-intensive wireless transmission [31,32], and limited wireless transmission bandwidth can increase the system's latency [6,[33][34][35]. Moreover, the transfer of neural data for processing to an external computer raises privacy concerns.…”

Section: Constraints Of Closed-loop Neuromodulationmentioning

confidence: 99%

Benchmarking of hardware-efficient real-time neural decoding in brain–computer interfaces

Hueber,

Tang,

Sifalakis

et al. 2024

Neuromorph. Comput. Eng.

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Designing processors for implantable closed-loop neuromodulation systems presents a formidable challenge due to the constrained operational environment, requiring low latency and high energy efficiency. Previous benchmarks have provided limited insights into energy efficiency and latency. This paper, however, introduces algorithmic metrics that capture the potential and limitations of neural decoders for closed-loop intra-cortical brain-computer interfaces in the context of energy and hardware constraints. This study benchmarks common decoding methods for predicting a primate’s finger kinematics from the motor cortex, and explores the suitability for low latency and low compute neural decoding. The study finds that ANN-based decoders provide superior decoding accuracy, requiring a high latency and many operations to decode neural signals effectively. Spiking neural networks emerge as a solution, bridging this gap by achieving competitive decoding performance within sub-10ms while utilizing a fraction of the computational resources. This distinctive advantages of neuromorphic spiking neural networks, positions them as highly suitable for the challenging environment of closed-loop neural modulation. Their capacity to balance decoding accuracy and operational efficiency offers immense potential in reshaping the landscape of neural decoders, fostering greater understanding, and opening new frontiers in closed-loop intracortical human-machine interaction.

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“…Usually, the respiration rate is monitored using nonportable methods such as capnography [3], but new wearable alternatives have been published using sound [3], humidity sensors [4], fiber optical sensors to detect vibrations [5], or body bioimpedance [6]. Recently, it has emerged another technology option based on Triboelectric Nanogenerators (TENGs) composed of two dielectric layers.…”

Section: Introductionmentioning

confidence: 99%

Wearable Triboelectric Sensor for Respiration and Coughing Monitoring

Anaya

Yuce

2021

2021 IEEE Sensors

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The COVID-19 Pandemic has reminded society of the vital importance of breathing monitoring, especially in the adult population. Abnormalities in respiration rate may indicate problems in the respiratory organs caused by possible infections, obstructions, certain lung, heart, or neurological conditions, stress, or anxiety. On the other hand, coughing monitoring (another indicator of coronavirus infection) can benefit from unbiased assessment methods that complement the physician's expertise. This manuscript reports the design and experiment results of a portable, low weight, and easy-tomanufacture Triboelectric Nanogenerator-based (TENGbased) respiratory and coughing sensor. The Triboelectric Sensor's (TS) small size and versatile design allow it to be attached to an elastic band or a hook-and-loop band and placed in different body parts or inside a disposable mask. When placed on the body, the triboelectric effect in the sensor layers, resulting from the vibration and movement of the chest, upper abdomen, or neck, is translated into an electric voltage, which is wirelessly transmitted using Low-power Bluetooth (BLE) communication. The results show the good response of the sensor to slow, fast and deep breaths while sitting and standing. The sensor also detects low, fast, and intense coughing events while sitting, standing, and walking. This demonstrates the potential of this technology scarcely used for breath and cough monitoring in literature.

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A 74-μW 11-Mb/s Wireless Vital Signs Monitoring SoC for Three-Lead ECG, Respiration Rate, and Body Temperature

Cited by 36 publications

References 31 publications

An Implantable Neuromorphic Sensing System Featuring Near-Sensor Computation and Send-on-Delta Transmission for Wireless Neural Sensing of Peripheral Nerves

An Implantable Neuromorphic Sensing System Featuring Near-Sensor Computation and Send-on-Delta Transmission for Wireless Neural Sensing of Peripheral Nerves

Benchmarking of hardware-efficient real-time neural decoding in brain–computer interfaces

Wearable Triboelectric Sensor for Respiration and Coughing Monitoring

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