Real Time ECG Acquisition and FPGA Based QRS Detection

Bhosale, Madhura M.; Chitre, Abhijit V.

doi:10.1109/iccubea.2018.8697710

Cited by 3 publications

(1 citation statement)

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“…There are several case studies that have mapped real-time, complex hardware implementation of machine learning classifiers for wearable healthcare applications. The Field Programmable Gate Array (FPGA) based systems were proposed for heartbeat monitoring [9], [10], seizure episodes in epilepsy disease [11]- [13] and activities of daily living [14], [15]. In light of these advances in healthcare sector, this article presents a non-invasive, low-cost and easily deployable FOG detector based on the data fusion of scalogram obtained using Wi-Fi sensing and spectrograms acquired using radar sensor.…”

Section: Table I Radar Sensor Detailmentioning

confidence: 99%

Sensor Fusion for Identification of Freezing of Gait Episodes Using Wi-Fi and Radar Imaging

et al. 2020

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Parkinson's disease (PD) is a progressive and neurodegenerative condition causing motor impairments. One of the major motor related impairments that present biggest challenge is freezing of gait (FOG) in Parkinson's patients. In FOG episode, the patient is unable to initiate, control or sustain a gait that consequently affects the Activities of Daily Livings (ADLs) and increases the occurrence of critical events such as falls. This paper presents continuous monitoring ADLs and classification freezing of gait episodes using Wi-Fi and radar imaging. The idea is to exploit the multiresolution scalograms generated by channel state information (CSI) imprint and micro-Doppler signatures produced by reflected radar signal. A total of 120 volunteers took part in experimental campaign and were asked to perform different activities including walking fast, walking slow, voluntary stop, sitting down & stand up and freezing of gait. Two neural networks namely Autoencoder and a proposed enhanced Autoencoder were used classify ADLs and FOG episodes using data fusion process by combining the images acquired from both sensing techniques. The Autoencoder provided overall classification accuracy of ∼87% for combined datasets. The proposed algorithm provided significantly better results by presenting an overall accuracy of ∼98% using data fusion.

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