SHIMMER&amp;#x2122;: An extensible platform for physiological signal capture

Burns, Adrian; Doheny, Emer P.; Greene, Barry R.; Foran, Tim; Leahy, Daniel; O’Donovan, Karol; McGrath, Michael J.

doi:10.1109/iembs.2010.5627535

Cited by 51 publications

(35 citation statements)

References 3 publications

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“…Furthermore in this paper, a study related to some available wireless biosensor platforms which are used in different healthcare applications, the technologies and the sensor types used in these platforms is presented. Also in this paper we investigate and discuss the ECG and surface EMG based work has been done so far using SHIMMER TM sensor platform [4]. The reason behind the selection of SHIMMER TM sensor platform is its user friendly hardware and software development architecture, which suited for our future and current works in healthcare.…”

Section: Figure 3: [The Limb Leads]mentioning

confidence: 99%

“…Shimmer also allows software development in different tools such as C#, MATLAB, Android and LabVIEW so it can be easily integrated with Shimmer. Figure. 8: SHIMMER Mote [4] …”

Section: Figure 7 Bts Freeemg[15]mentioning

confidence: 99%

“…Sensing Health with Intelligence, Modularity, Mobility and Experimental Reusability (SHIMMER) [4] is a well reputed wearable sensor platform designed for research and commercial use and well suited for wearable applications. A system developed with Shimmer wearable sensors enables the real time processing, transmission and display of the sensed data with the simple and effective capturing from the body.…”

Section: Figure 7 Bts Freeemg[15]mentioning

confidence: 99%

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A Study of Wearable Bio-Sensor Technologies and Applications in Healthcare

Mehmood¹,

Nadeem²,

Rizwan³

et al. 2017

SJCMS

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Abstract:In today's world the rapid advancements in Micro-Electromechanical Systems (MEMS) and Nano technology have improved almost all the aspects of daily life routine with the help of different smart devices such as smart phones, compact electronic devices etc. The prime example of these emerging developments is the development of wireless sensors for healthcare procedures. One kind of these sensors is wearable bio-sensors. In this paper, the technologies of two types of bio-sensors (ECG, EMG) are investigated and also compared with traditional ECG, EMG equipment. We have taken SHIMMERTM wireless sensor platform as an example of wearable biosensors technology. We have investigated the systems developed for

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Section: Figure 3: [The Limb Leads]mentioning

confidence: 99%

“…Shimmer also allows software development in different tools such as C#, MATLAB, Android and LabVIEW so it can be easily integrated with Shimmer. Figure. 8: SHIMMER Mote [4] …”

Section: Figure 7 Bts Freeemg[15]mentioning

confidence: 99%

Section: Figure 7 Bts Freeemg[15]mentioning

confidence: 99%

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A Study of Wearable Bio-Sensor Technologies and Applications in Healthcare

Mehmood¹,

Nadeem²,

Rizwan³

et al. 2017

SJCMS

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“…The outer room had a computer that was used for managing playback of the film clips and a screen connected to the surveillance cameras. The monitoring of activations in the ANS of the participants was performed with wireless sensors manufactured by Shimmer (Burns et al 2010). These portable sensors enabled us to measure physiological signals of the subjects' bodies in a real-time manner.…”

Section: Experimental Designmentioning

confidence: 99%

Beyond cognition and affect: sensing the unconscious

Ivonin

Chang

Díaz

et al. 2014

Behaviour & Information Technology

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In the past decade, research on human-computer interaction has embraced psychophysiological user interfaces that enhance awareness of computers about conscious cognitive and affective states of users and increase their adaptive capabilities. Still, human experience is not limited to the levels of cognition and affect but extends further into the realm of universal instincts and innate behaviors that form the collective unconscious. Patterns of instinctual traits shape archetypes that represent images of the unconscious. This study investigated Further analysis suggested that classification performance could be improved up to 70.3 percent in the case of 7 archetypes by using within-subject models.

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“…With the increase in demand for technologically driven medical devices and the advancements in neuroscience, cognitive psychology, and artificial intelligence research, biopotential signals are being increasingly applied to real-time health monitoring, disease diagnoses, control rehabilitation devices, and brain-computer interfaces among others [2,3]. Generally, biopotential signals such as electrocardiogram (ECG), electromyography (EMG), electroencephalography (EEG), and electro-oculogram (EOG) are characterized by high impedance, low frequency, low amplitude, and strong background noise [4,5].…”

Section: Introductionmentioning

confidence: 99%

Effective Biopotential Signal Acquisition: Comparison of Different Shielded Drive Technologies

et al. 2018

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Biopotential signals are mainly characterized by low amplitude and thus often distorted by extraneous interferences, such as power line interference in the recording environment and movement artifacts during the acquisition process. With the presence of such large-amplitude interferences, subsequent processing and analysis of the acquired signals becomes quite a challenging task that has been reported by many previous studies. A number of software-based filtering techniques have been proposed, with most of them being able to minimize the interferences but at the expense of distorting the useful components of the target signal. Therefore, this study proposes a hardware-based method that utilizes a shielded drive circuit to eliminate extraneous interferences on biopotential signal recordings, while also preserving all useful components of the target signal. The performance of the proposed method was evaluated by comparing the results with conventional hardware and software filtering methods in three different biopotential signal recording experiments (electrocardiogram (ECG), electro-oculogram (EOG), and electromyography (EMG)) on an ADS1299EEG-FE platform. The results showed that the proposed method could effectively suppress power line interference as well as its harmonic components, and it could also significantly eliminate the influence of unwanted electrode lead jitter interference. Findings from this study suggest that the proposed method may provide potential insight into high quality acquisition of different biopotential signals to greatly ease subsequent processing in various biomedical applications.

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SHIMMER™: An extensible platform for physiological signal capture

Cited by 51 publications

References 3 publications

A Study of Wearable Bio-Sensor Technologies and Applications in Healthcare

A Study of Wearable Bio-Sensor Technologies and Applications in Healthcare

Beyond cognition and affect: sensing the unconscious

Effective Biopotential Signal Acquisition: Comparison of Different Shielded Drive Technologies

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