Portable multipurpose bio-signal acquisition and wireless streaming device for wearables

Sarker, Victor Kathan; Jiang, Mingzhe; Gia, Tuan Nguyen; Anzanpour, Arman; Rahmani, Amir M.; Liljeberg, Pasi

doi:10.1109/sas.2017.7894053

Cited by 30 publications

(18 citation statements)

References 8 publications

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“…Thus for testing the method we use a real-world dataset containing one or several ECG contaminants, such as powerline interference, electromyographic noise, baseline wandering, or electrode motion artifact. The test dataset is part of a larger database 2 , where one channel ECG signal was recorded by a portable biopotential acquisition device [28] from postoperative patients during re-examination. In total 103 minutes of lead I ECG recordings sampled at 500 Hz from 7 patients are included in the test phase.…”

Section: Datasetsmentioning

confidence: 99%

Robust ECG R-peak detection using LSTM

Laitala

Jiang

Syrjälä

et al. 2020

Proceedings of the 35th Annual ACM Symposium on Applied Computing

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Detecting QRS complexes or R-peaks from the electrocardiogram (ECG) is the basis for heart rate determination and heart rate variability analysis. Over the years, multiple different methods have been proposed as solutions to this problem. Vast majority of the proposed methods are traditional rule based algorithms that are vulnerable to noise. We propose a new R-peak detection method that is based on the Long Short-Term Memory (LSTM) network. LSTM networks excel at temporal modelling tasks that include long-term dependencies, making it suitable for ECG analysis. Additionally, we propose data generator for creating noisy ECG data that is used to train the robust R-peak detector. Our initial testing shows that the proposed method outperforms traditional algorithms while the greatest competitive edge is achieved with the noisy ECG signals.

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Section: Datasetsmentioning

confidence: 99%

Robust ECG R-peak detection using LSTM

Laitala

Jiang

Syrjälä

et al. 2020

Proceedings of the 35th Annual ACM Symposium on Applied Computing

Self Cite

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“…At the cloud layer, we used a Linode [50] Cloud-based control and information display panel similar to [51] which runs parking information application and real-time notification service. The Cloud server aggregates all the data from different LoRa gateways spread around over a large geographic area.…”

Section: Lora Gateway and Cloudmentioning

confidence: 99%

Smart Parking System with Dynamic Pricing, Edge-Cloud Computing and LoRa

Sarker

Gia

Dhaou

et al. 2020

Sensors

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A rapidly growing number of vehicles in recent years cause long traffic jams and difficulty in the management of traffic in cities. One of the most significant reasons for increased traffic jams on the road is random parking in unauthorized and non-permitted places. In addition, managing of available parking places cannot achieve the expected reduction in traffic congestion related problems due to mismanagement, lack of real-time parking guidance to the drivers, and general ignorance. As the number of roads, highways and related resources has not increased significantly, a rising need for a smart, dynamic and effective parking solution is observed. Accordingly, with the use of multiple sensors, appropriate communication network and advanced processing capabilities of edge and cloud computing, a smart parking system can help manage parking effectively and make it easier for the vehicle owners. In this paper, we propose a multi-layer architecture for smart parking system consisting of multi-parametric parking slot sensor nodes, latest long-range low-power wireless communication technology and Edge-Cloud computation. The proposed system enables dynamic management of parking for large areas while providing useful information to the drivers about available parking locations and related services through near real-time monitoring of vehicles. Furthermore, we propose a dynamic pricing algorithm to yield maximum possible revenue for the parking authority and optimum parking slot availability for the drivers.

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“…For facial muscle activation recognition, the sEMG signal was captured with a multi-purpose biosignal acquisition device that was developed for health monitoring [ 29 ]. This device was designed and manufactured by the IoT4Health research group.…”

Section: Methodsmentioning

confidence: 99%

Developing a pain intensity prediction model using facial expression: A feasibility study with electromyography

et al. 2020

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The automatic detection of facial expressions of pain is needed to ensure accurate pain assessment of patients who are unable to self-report pain. To overcome the challenges of automatic systems for determining pain levels based on facial expressions in clinical patient monitoring, a surface electromyography method was tested for feasibility in healthy volunteers. In the current study, two types of experimental gradually increasing pain stimuli were induced in thirty-one healthy volunteers who attended the study. We used a surface electromyography method to measure the activity of five facial muscles to detect facial expressions during pain induction. Statistical tests were used to analyze the continuous electromyography data, and a supervised machine learning was applied for pain intensity prediction model. Muscle activation of corrugator supercilii was most strongly associated with selfreported pain, and the levator labii superioris and orbicularis oculi showed a statistically significant increase in muscle activation when the pain stimulus reached subjects' self-reported pain thresholds. The two strongest features associated with pain, the waveform length of the corrugator supercilii and levator labii superioris, were selected for a prediction model. The performance of the pain prediction model resulted in a c-index of 0.64. In the study results, the most detectable difference in muscle activity during the pain experience was connected to eyebrow lowering, nose wrinkling and upper lip raising. As the performance of the prediction model remains modest, yet with a statistically significant ordinal classification, we suggest testing with a larger sample size to further explore the variables that affect variation in expressiveness and subjective pain experience.

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Portable multipurpose bio-signal acquisition and wireless streaming device for wearables

Cited by 30 publications

References 8 publications

Robust ECG R-peak detection using LSTM

Robust ECG R-peak detection using LSTM

Smart Parking System with Dynamic Pricing, Edge-Cloud Computing and LoRa

Developing a pain intensity prediction model using facial expression: A feasibility study with electromyography

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