A Wearable Wireless General Purpose Bio-signal Acquisition Prototype System for Home Healthcare

Duan, Junwei; Chen, Changhao; Pun, Sio Hang; Wan, Feng; Mak, Peng Un; Hu, Yong Hong; Mak, Pui In; Vai, Mang I

doi:10.1109/icbeb.2012.46

Cited by 6 publications

(8 citation statements)

References 6 publications

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“…In this new arrangement, the data readings had always maximum priority in the time-sharing of kernel task management, leaving the rest of the time available for the other purposes such as data processing, saving and transmission. In comparison to other systems previously reported and presented in Table 1 [5,[9][10][11]13], the proposed system consumes more power, due to the implementation of a more expensive communication protocol. Although other platforms consume less energy, they also provide fewer channels, less signal resolution and lower sampling rates, which renders in a data package much smaller than the data package transmitted by the proposed platform.…”

Section: Comparison With State-of-the-art Platformsmentioning

confidence: 87%

“…Other studies have been applying different wireless communication protocols (Table 1) [5,[9][10][11][12][13]. These systems provide 16 channels or less, typically less than 24 bits resolution per channel and sampling frequencies of less than 1000 SPS.…”

Section: Comparison With State-of-the-art Platformsmentioning

confidence: 99%

“…Additionally, these systems can rarely operate with the maximum number of channels and the maximum sampling frequency simultaneously, since the highest number of channels usually implies a lower sampling frequency, in compliance with the available transmission bandwidth. Usually platforms use microcontrollers (ARM cortex M3 [13], or proprietary architectures such as MSP430 or Atmega [10][11][12]) with low clock speed and low power consumption [5,[9][10][11]13]. Although ambulatory systems should have low power consumption, the computational requirements for local data processing (such as digital signal processing and decision making algorithms) should meet a high processing power feature.…”

Section: Comparison With State-of-the-art Platformsmentioning

confidence: 99%

See 2 more Smart Citations

Wireless and wearable eeg acquisition platform for ambulatory monitoring

Pinho

Correia

Sousa

et al. 2014

2014 IEEE 3nd International Conference on Serious Games and Applications for Health (SeGAH)

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Section: Comparison With State-of-the-art Platformsmentioning

confidence: 87%

Section: Comparison With State-of-the-art Platformsmentioning

confidence: 99%

Section: Comparison With State-of-the-art Platformsmentioning

confidence: 99%

See 1 more Smart Citation

Wireless and wearable eeg acquisition platform for ambulatory monitoring

Pinho

Correia

Sousa

et al. 2014

2014 IEEE 3nd International Conference on Serious Games and Applications for Health (SeGAH)

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“…Each EMG channel is filtered through a sixth order bandpass filter (1)(2)(3)(4)(5) with one pole at low frequency (3Hz) to remove the motion artifacts and five poles to achieve a Butterworth filter at 500Hz.…”

Section: A the Filtering Subsystemmentioning

confidence: 99%

“…To date biometric devices can be grouped into of three classes: High performance/quality EEG/EMG/ECG devices which are instrumented as laboratory tools [1], [2] or as small distance portable systems [3], [4], [5]; Wearable motion capture systems [6] provided with different flavours of sensing and wireless options [7], [8]; and low cost combined motion and EMG sensors which recently appeared on the market (Liveathos TM , and Myo TM ). Laboratory tools are very accurate and can be scaled to complex situations, but their settling prevent the portability of the system.…”

Section: Introductionmentioning

confidence: 99%

A novel wearable biometric capture system

Avizzano

Ruffaldi

Bergamasco

2014

22nd Mediterranean Conference on Control and Automation

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In the present paper we introduce a novel wireless system capable of simultaneously track and monitor the motion of the body and the associated muscular stress. The device provides with an array of four inertial and magnetic measurement units combined with up to 32 independent high sensitivity ADC channels. An embedded computing system does allow the high speed (4 KHz) EMG acquisition and filtering, combined with (100Hz) motion reconstruction and relevant features (20Hz) detection. The device connects to any receiving host using a portable server architecture and an extensible computing system which uses a web interface and a plugin architecture based on shared library.The device has been conceived to monitor body activities in application on-the-go, where the use of wired devices is not suitable and the need for a strict synchronizations among the motion reconstruction and the myo-electric signals is fundamental. In what follows we will describe the general architecture of the system, the embedded algorithms for motion and EMG reconstruction and the software architecture of the host system.

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myBrain: a novel EEG embedded system for epilepsy monitoring

Pinho

Cerqueira

Correia

et al. 2017

Journal of Medical Engineering & Technology

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The World Health Organisation has pointed that a successful health care delivery, requires effective medical devices as tools for prevention, diagnosis, treatment and rehabilitation. Several studies have concluded that longer monitoring periods and outpatient settings might increase diagnosis accuracy and success rate of treatment selection. The long-term monitoring of epileptic patients through electroencephalography (EEG) has been considered a powerful tool to improve the diagnosis, disease classification, and treatment of patients with such condition. This work presents the development of a wireless and wearable EEG acquisition platform suitable for both long-term and short-term monitoring in inpatient and outpatient settings. The developed platform features 32 passive dry electrodes, analogue-to-digital signal conversion with 24-bit resolution and a variable sampling frequency from 250 Hz to 1000 Hz per channel, embedded in a stand-alone module. A computer-on-module embedded system runs a Linux operating system that rules the interface between two software frameworks, which interact to satisfy the real-time constraints of signal acquisition as well as parallel recording, processing and wireless data transmission. A textile structure was developed to accommodate all components. Platform performance was evaluated in terms of hardware, software and signal quality. The electrodes were characterised through electrochemical impedance spectroscopy and the operating system performance running an epileptic discrimination algorithm was evaluated. Signal quality was thoroughly assessed in two different approaches: playback of EEG reference signals and benchmarking with a clinical-grade EEG system in alpha-wave replacement and steady-state visual evoked potential paradigms. The proposed platform seems to efficiently monitor epileptic patients in both inpatient and outpatient settings and paves the way to new ambulatory clinical regimens as well as non-clinical EEG applications.

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A Wearable Wireless General Purpose Bio-signal Acquisition Prototype System for Home Healthcare

Cited by 6 publications

References 6 publications

Wireless and wearable eeg acquisition platform for ambulatory monitoring

Wireless and wearable eeg acquisition platform for ambulatory monitoring

A novel wearable biometric capture system

myBrain: a novel EEG embedded system for epilepsy monitoring

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