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Amit Acharyya
Researcher at Indian Institute of Technology, Hyderabad
Publications - 195
Citations - 2147
Amit Acharyya is an academic researcher from Indian Institute of Technology, Hyderabad. The author has contributed to research in topics: Computer science & FastICA. The author has an hindex of 20, co-authored 164 publications receiving 1494 citations. Previous affiliations of Amit Acharyya include Newcastle University & Indian Institutes of Technology.
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Journal ArticleDOI
A Low-Complexity ECG Feature Extraction Algorithm for Mobile Healthcare Applications
Evangelos B. Mazomenos,Dwaipayan Biswas,Amit Acharyya,Taihai Chen,Koushik Maharatna,James A. Rosengarten,J.M. Morgan,Nick Curzen +7 more
TL;DR: A low-complexity algorithm for the extraction of the fiducial points from the electrocardiogram, based on the discrete wavelet transform with the Haar function being the mother wavelet, which achieves an ideal tradeoff between computational complexity and performance, a key requirement in remote cardiovascular disease monitoring systems.
Journal ArticleDOI
CorNET: Deep Learning Framework for PPG-Based Heart Rate Estimation and Biometric Identification in Ambulant Environment
Dwaipayan Biswas,Luke R. Everson,Muqing Liu,Madhuri Panwar,Bram-Ernst Verhoef,Shrishail Patki,Chris H. Kim,Amit Acharyya,Chris Van Hoof,Mario Konijnenburg,Nick Van Helleputte +10 more
TL;DR: A novel deep learning framework (CorNET) to efficiently estimate heart rate (HR) information and perform biometric identification (BId) using only a wrist-worn, single-channel PPG signal collected in ambulant environment is presented.
Proceedings ArticleDOI
CNN based approach for activity recognition using a wrist-worn accelerometer
Madhuri Panwar,S. Ram Dyuthi,K. Chandra Prakash,Dwaipayan Biswas,Amit Acharyya,Koushik Maharatna,Arvind Gautam,Ganesh R. Naik +7 more
TL;DR: This work has attempted to design a generalized model for recognition of three fundamental movements of the human forearm performed in daily life where data is collected from four different subjects using a single wrist worn accelerometer sensor.
Journal ArticleDOI
PP-Net: A Deep Learning Framework for PPG-Based Blood Pressure and Heart Rate Estimation
TL;DR: The accurate evaluation on a huge population with CVD complications, validates the robustness of the proposed framework in pervasive healthcare monitoring especially cardiac and stroke rehabilitation monitoring.
Journal ArticleDOI
Rehab-Net: Deep Learning Framework for Arm Movement Classification Using Wearable Sensors for Stroke Rehabilitation
Madhuri Panwar,Dwaipayan Biswas,Harsh Bajaj,Michael Jobges,Ruth Turk,Koushik Maharatna,Amit Acharyya +6 more
TL;DR: The proposed Rehab-Net framework was validated on sensor data collected in two situations: semi-naturalistic environment involving an archetypal activity of “making-tea” with four stroke survivors and natural environment where ten stroke survivors were free to perform any desired arm movement for the duration of 120 min.