Designing Low Power of Sigma Delta Modulator for Biomedical Application

Lee, Ho-Yin; Hsu, Chen-Ming; Huang, Sheng-Chia; Shih, Yi-Wei; Luo, Ching‐Hsing

doi:10.4015/s1016237205000287

Cited by 19 publications

(9 citation statements)

References 5 publications

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“…The solution used here is the delta-modulator ADC algorithm, based on a sigma-delta modulator circuit (Corradi and Indiveri, 2015 ). This mechanism is particularly used in low frequency, high performance and low power applications (Lee et al, 2005 ), such as biomedical circuits. Moreover, this modulator represents a good interface for neuromorphic devices because it has much less circuit complexity and lower power consumption than multi-bit ADCs.…”

Section: Methodsmentioning

confidence: 99%

Hand-Gesture Recognition Based on EMG and Event-Based Camera Sensor Fusion: A Benchmark in Neuromorphic Computing

et al. 2020

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Hand gestures are a form of non-verbal communication used by individuals in conjunction with speech to communicate. Nowadays, with the increasing use of technology, hand-gesture recognition is considered to be an important aspect of Human-Machine Interaction (HMI), allowing the machine to capture and interpret the user's intent and to respond accordingly. The ability to discriminate between human gestures can help in several applications, such as assisted living, healthcare, neuro-rehabilitation, and sports. Recently, multi-sensor data fusion mechanisms have been investigated to improve discrimination accuracy. In this paper, we present a sensor fusion framework that integrates complementary systems: the electromyography (EMG) signal from muscles and visual information. This multi-sensor approach, while improving accuracy and robustness, introduces the disadvantage of high computational cost, which grows exponentially with the number of sensors and the number of measurements. Furthermore, this huge amount of data to process can affect the classification latency which can be crucial in real-case scenarios, such as prosthetic control. Neuromorphic technologies can be deployed to overcome these limitations since they allow real-time processing in parallel at low power consumption. In this paper, we present a fully neuromorphic sensor fusion approach for hand-gesture recognition comprised of an event-based vision sensor and three different neuromorphic processors. In particular, we used the event-based camera, called DVS, and two neuromorphic platforms, Loihi and ODIN + MorphIC. The EMG signals were recorded using traditional electrodes and then converted into spikes to be fed into the chips. We collected a dataset of five gestures from sign language where visual and electromyography signals are synchronized. We compared a fully neuromorphic approach to a baseline implemented using traditional machine learning approaches on a portable GPU system. According to the chip's constraints, we designed specific spiking neural networks (SNNs) for sensor fusion that showed classification accuracy comparable to the software baseline. These neuromorphic alternatives have increased inference time, between 20 and 40%, Ceolini et al. Sensor Fusion Neuromorphic Benchmark with respect to the GPU system but have a significantly smaller energy-delay product (EDP) which makes them between 30× and 600× more efficient. The proposed work represents a new benchmark that moves neuromorphic computing toward a real-world scenario.

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Section: Methodsmentioning

confidence: 99%

Hand-Gesture Recognition Based on EMG and Event-Based Camera Sensor Fusion: A Benchmark in Neuromorphic Computing

et al. 2020

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“…Here, we apply a small-world RSNN implemented on the Mosaic to the detection of arrhythmic heartbeats from ECG signals 41 (see Methods). First, we encode the continuous ECG time-series into trains of spikes using a delta-modulation technique, which describes the relative changes in signal magnitude 42,43 . These spikes are then fed as input into the Mosaic small-world RSNN.…”

Section: Application To Ecg Anomaly Detectionmentioning

confidence: 99%

The neuromorphic Mosaic: re-configurable in-memory small-world graphs

Dalgaty

Moro

Pra

et al. 2021

Preprint

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Thanks to their non-volatile and multi-bit properties, memristors have been extensively used as synaptic weight elements in neuromorphic architectures. However, their use to define and re-program the network connectivity has been overlooked. Here, we propose, implement and experimentally demonstrate Mosaic, a neuromorphic architecture based on a systolic array of memristor crossbars. For the first time, we use distributed non-volatile memristors not only for computation, but also for routing (i.e., to define the network connectivity). Mosaic is particularly well-suited for the implementation of re-configurable small-world graphical models, with dense local and sparse global connectivity - found extensively in the brain. We mathematically show that, as the networks scale up, the Mosaic requires less memory than in conventional memristor approaches. We map a spiking recurrent neural network on the Mosaic to solve an Electrocardiogram (ECG) anomaly detection task. While the performance is either equivalent or better than software models, the advantage of the Mosaic was clearly seen in respective one and two orders of magnitude reduction in energy requirements, compared to a micro-controller and address-event representation-based processor. Mosaic promises to open up a new approach to designing neuromorphic hardware based on graph-theoretic principles with less memory and energy.

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“…The way a signal is collected is seen important when multiple feature extractions from an EEG signal are made by a PXA 270 processor [62,64,73–76]. …”

Section: To Use Modified Ica Capture More Precise Physiological Signalsmentioning

confidence: 99%

“…It is requested that the number of linear mixed measurement signal be no less than that of uncorrelated components, a requirement that cannot be fulfilled in most ICA theoretic analyses and applications. For this sake, the excessive basal Independent component analysis is proposed as a means to address such issue with a higher computational complexity [62,64]. …”

Section: To Use Modified Ica Capture More Precise Physiological Signalsmentioning

confidence: 99%

Study on a Real-Time BEAM System for Diagnosis Assistance Based on a System on Chips Design

Sung

Chen

Chang

2013

Sensors

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As an innovative as well as an interdisciplinary research project, this study performed an analysis of brain signals so as to establish BrainIC as an auxiliary tool for physician diagnosis. Cognition behavior sciences, embedded technology, system on chips (SOC) design and physiological signal processing are integrated in this work. Moreover, a chip is built for real-time electroencephalography (EEG) processing purposes and a Brain Electrical Activity Mapping (BEAM) system, and a knowledge database is constructed to diagnose psychosis and body challenges in learning various behaviors and signals antithesis by a fuzzy inference engine. This work is completed with a medical support system developed for the mentally disabled or the elderly abled.

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Designing Low Power of Sigma Delta Modulator for Biomedical Application

Abstract: ABSTRACT

Cited by 19 publications

References 5 publications

Hand-Gesture Recognition Based on EMG and Event-Based Camera Sensor Fusion: A Benchmark in Neuromorphic Computing

Hand-Gesture Recognition Based on EMG and Event-Based Camera Sensor Fusion: A Benchmark in Neuromorphic Computing

The neuromorphic Mosaic: re-configurable in-memory small-world graphs

Study on a Real-Time BEAM System for Diagnosis Assistance Based on a System on Chips Design

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