Design on a wearable armband device for assessing the motion function of upper limbs

Gao, Xiupeng; Yin, Yilong

doi:10.1016/j.comcom.2020.01.074

Cited by 7 publications

(2 citation statements)

References 19 publications

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“…The use of LIF neurons and local spike-driven plasticity rules opens up the possibility of implementing such architecture on neuromorphic chips, leading to an even more efficient online and wearable implementation. We considered muscles in the forearm (extrinsic), to target the use of myoelectric armbands and bracelets for monitoring users' activity [37,38], and in the hand (intrinsic), so far considered mainly in neurophysiology [36,39,40].…”

Section: Discussionmentioning

confidence: 99%

Neuromorphic Decoding of Spinal Motor Neuron Behaviour during Natural Hand Movements for a New Generation of Wearable Neural Interfaces

Tanzarella¹,

Iacono

Donati

et al. 2022

Preprint

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We propose a neuromorphic framework to process the activity of human spinal motor neurons for movement intention recognition. This framework is integrated in a non-invasive interface that decodes the activity of motor neurons innervating intrinsic and extrinsic hand muscles. One of the main limitations of current neural interfaces is that machine learning models cannot exploit the efficiency of the spike encoding operated by the nervous system. Spiking-based pattern recognition would detect the spatio-temporal sparse activity of a neuronal pool and lead to adaptive and compact implementations, eventually running locally in embedded systems. Emergent Spiking Neural Networks (SNN) have not yet been used for processing the activity of in-vivo human neurons. Here we developed a convolutional SNN to process a total of 467 spinal motor neurons whose activity was identified in 5 participants while executing 10 hand movements. The classification accuracy approached 0.95 ± 0.14 for both isometric and non-isometric contractions. These results show for the first time the potential of highly accurate motion intent detection by combining non-invasive neural interfaces and SNN.

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Section: Discussionmentioning

confidence: 99%

Neuromorphic Decoding of Spinal Motor Neuron Behaviour during Natural Hand Movements for a New Generation of Wearable Neural Interfaces

Tanzarella¹,

Iacono

Donati

et al. 2022

Preprint

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“…Therefore, the analysis of the components of the PPG signal is essential in many applications [4,6,7].…”

Section: Introductionmentioning

confidence: 99%

Signal Quality Valuation with Optimal Power Consumption and High Compression Ratio Method for Estimate Heart Rate Applied to Real-time PPG Measurement

Bui¹,

Minh²,

Nguyen³

et al. 2022

Preprint

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Nowadays, wearable devices for human health monitoring are increasingly become popular and widely used. Typically, the wearable device is small size and operates with batteries. Therefore, the wearable device acquires bio-signals and transfers to smartphones or personal computers (PC) via WiFi/ Bluetooth for processing data. To reduce power consumption is one of the most important challenges of designing wearable devices. To solve this problem, the proposed signal quality valuation (SQV) method can be select the high-quality signal and reduce the transfer time to other devices. In this paper, the proposed SQV and data compression method rely on real-time PPG signals analysis to retain important information of PPG signal, improve performance and power consumption of PPG devices. Besides, we also proposed Heuristic rules for heart rate (HR) estimation with compression data. The experimental results show that the highest compression ratio (CR) is 387.8 with BIDMC Physionet database (sampling frequencies of 125 Hz) and HR error as 1.43 bpm for averaging absolute error (avAE), the standard deviation absolute error (sdAE as 0.4) and relative error mean (avRE as 0.019). The proposed real-time PPG measurement system (sampling frequency as 100/ 200/ 400 Hz) reduce power consumption and open the new structure for healthcare application systems

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Biomedical Sensors and Applications of Wearable Technologies on Arm and Hand

Seçkin

Gençer

2022

Biomedical Materials & Devices

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Design on a wearable armband device for assessing the motion function of upper limbs

Cited by 7 publications

References 19 publications

Neuromorphic Decoding of Spinal Motor Neuron Behaviour during Natural Hand Movements for a New Generation of Wearable Neural Interfaces

Neuromorphic Decoding of Spinal Motor Neuron Behaviour during Natural Hand Movements for a New Generation of Wearable Neural Interfaces

Signal Quality Valuation with Optimal Power Consumption and High Compression Ratio Method for Estimate Heart Rate Applied to Real-time PPG Measurement

Biomedical Sensors and Applications of Wearable Technologies on Arm and Hand

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