28.4 A battery-powered efficient multi-sensor acquisition system with simultaneous ECG, BIO-Z, GSR, and PPG

Konijnenburg, Mario; Stanzione, Stefano; Yan, Lei; Jee, Dong-Woo; Pettine, Julia; Wegberg, Roland van; Kim, Hye-Jung; Liempd, Chris van; Fish, Ram; Schluessler, James; Groot, Harmke de; Hoof, Chris Van; Yazıcıoğlu, Refet Fırat; Helleputte, Nick Van

doi:10.1109/isscc.2016.7418116

Cited by 35 publications

(21 citation statements)

References 4 publications

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“…Dedicated hardware accelerators, coupled with a simple processor for control, can be used for specific tasks offering the ultimate energy efficiency. As an example, a battery-powered multi-sensor acquisition system which is controlled by an ARM Cortex M0 and contains hardware accelerators for heart rate detection has been proposed by Konijnenburg et al [4]. Ickes et al propose another system with FFT and FIR filter accelerators which are controlled by a 16b CPU and achieves an energy efficiency of 10 pJ/op at 0.45V [25].…”

Section: Related Workmentioning

confidence: 99%

“…An example of using dotp instructions is given below: Finally, the bit manipulation part of the presented ISA-extensions fits well into GCC since most instructions have already an internal GCC counterpart. 4…”

Section: Toolchain Supportmentioning

confidence: 99%

“…A. Traber is now with Advanced Signal Pursuit (ACP), Zürich, Switzerland D. Rossi simple MCU is very efficient for controlling purposes and lightweight processing, but not powerful nor efficient enough to run more complex algorithms on parallel sensor data streams [4]. One approach to achieve a higher energy efficiency and performance is to equip the MCU with digital signal processing (DSP) engines which allow to e.g.…”

Section: Introductionmentioning

confidence: 99%

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Near-Threshold RISC-V Core With DSP Extensions for Scalable IoT Endpoint Devices

Gautschi

Schiavone

Traber

et al. 2017

IEEE Trans. VLSI Syst.

373

270

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Endpoint devices for Internet-of-Things not only need to work under extremely tight power envelope of a few milliwatts, but also need to be flexible in their computing capabilities, from a few kOPS to GOPS. Near-threshold (NT) operation can achieve higher energy efficiency, and the performance scalability can be gained through parallelism. In this paper we describe the design of an opensource RISC-V processor core specifically designed for NT operation in tightly coupled multi-core clusters. We introduce instructionextensions and microarchitectural optimizations to increase the computational density and to minimize the pressure towards the shared memory hierarchy. For typical data-intensive sensor processing workloads the proposed core is on average 3.5× faster and 3.2× more energy-efficient, thanks to a smart L0 buffer to reduce cache access contentions and support for compressed instructions. SIMD extensions, such as dot-products, and a built-in L0 storage further reduce the shared memory accesses by 8× reducing contentions by 3.2×. With four NT-optimized cores, the cluster is operational from 0.6 V to 1.2 V achieving a peak efficiency of 67 MOPS/mW in a low-cost 65 nm bulk CMOS technology. In a low power 28 nm FDSOI process a peak efficiency of 193 MOPS/mW (40 MHz, 1 mW) can be achieved.Index Terms-Internet-of-Things, Ultra-low-power, Multi-core, RISC-V, ISA-extensions.

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Section: Related Workmentioning

confidence: 99%

Section: Toolchain Supportmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Near-Threshold RISC-V Core With DSP Extensions for Scalable IoT Endpoint Devices

Gautschi

Schiavone

Traber

et al. 2017

IEEE Trans. VLSI Syst.

373

270

View full text Add to dashboard Cite

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“…Detected peaks are then converted into ECG signatures and are classified using the trained neural network. This online phase can also be integrated within a ECG acquisition system [16,17].…”

Section: Proposed Approachmentioning

confidence: 99%

Heartbeat classification in wearables using multi-layer perceptron and time-frequency joint distribution of ECG

Das

Catthoor

Schaafsma

2018

Proceedings of the 2018 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologi

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Heartbeat classification using electrocardiogram (ECG) data is a vital assistive technology for wearable health solutions. We propose heartbeat feature classification based on a novel sparse representation using time-frequency joint distribution of ECG. Fundamental to this is a multi-layer perceptron, which incorporates these signatures to detect cardiac arrhythmia. This approach is validated with ECG data from MIT-BIH arrhythmia database. Results show that our approach has an average 95.7% accuracy, an improvement of 22% over state-of-the-art approaches. Additionally, ECG sparse distributed representations generates only 3.7% false negatives, reduction of 89% with respect to existing ECG signal classification techniques. KEYWORDSSpiking neural network (SNN), global synapse, particle swarm optimization (PSO), CxQuad, spike disorder count, inter-spike distortion * Dr. Francky Catthoor is also associated with the ESAT department of KU Leuven, Belgium.

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“…For example, an electropotential (EP) mode is used in applications including electrocardiogram (ECG), electromyogram (EMG), and electroencephalogram (EEG) [3,4,5]; impedance (IMP) mode is used for applications including body fat based on body impedance analysis [6,7], respiration rate [8,9], and galvanic skin response [10]; electrochemical (EC) mode is used in applications including blood glucose monitoring [11]; and photoelectric (PE) mode is used in applications including photoplethysmography (PPG) [12,13] and blood oxygen saturation [14,15]. Moreover, recently, as the requirements on integrated multifunctional biosignal acquisition systems are increasing, some research on multiparameter biosignal acquisition in a single system on a chip (SoC) have been reported [16,17,18]. …”

Section: Introductionmentioning

confidence: 99%

Reconfigurable Multiparameter Biosignal Acquisition SoC for Low Power Wearable Platform

Kim

2016

Sensors

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A low power and low noise reconfigurable analog front-end (AFE) system on a chip (SoC) for biosignal acquisition is presented. The presented AFE can be reconfigured for use in electropotential, bioimpedance, electrochemical, and photoelectrical modes. The advanced healthcare services based on multiparameter physiological biosignals can be easily implemented with these multimodal and highly reconfigurable features of the proposed system. The reconfigurable gain and input referred noise of the core instrumentation amplifier block are 25 dB to 52 dB, and 1 μVRMS, respectively. The power consumption of the analog blocks in one readout channel is less than 52 μW. The reconfigurable capability among various modes of applications including electrocardiogram, blood glucose concentration, respiration, and photoplethysmography are shown experimentally.

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28.4 A battery-powered efficient multi-sensor acquisition system with simultaneous ECG, BIO-Z, GSR, and PPG

Cited by 35 publications

References 4 publications

Near-Threshold RISC-V Core With DSP Extensions for Scalable IoT Endpoint Devices

Near-Threshold RISC-V Core With DSP Extensions for Scalable IoT Endpoint Devices

Heartbeat classification in wearables using multi-layer perceptron and time-frequency joint distribution of ECG

Reconfigurable Multiparameter Biosignal Acquisition SoC for Low Power Wearable Platform

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