most citedAMSER: Adaptive Multi-modal Sensing for Energy Efficient and Resilient eHealth Systems

1 citations · 1 across the 2 of their papers we have counts for

collaborators

5 papers

cs.DC20241 cited

HiDP: Hierarchical DNN Partitioning for Distributed Inference on Heterogeneous Edge Platforms

Zain Taufique, Aman Vyas, Antonio Miele +2

Edge inference techniques partition and distribute Deep Neural Network (DNN) inference tasks among multiple edge nodes for low latency inference, without considering the core-level…

eess.SP2024

ECG Unveiled: Analysis of Client Re-identification Risks in Real-World ECG Datasets

Ziyu Wang, Anil Kanduri, Seyed Amir Hossein Aqajari +5

While ECG data is crucial for diagnosing and monitoring heart conditions, it also contains unique biometric information that poses significant privacy risks. Existing ECG re-identi…

eess.SP2024

Characterizing Accuracy Trade-offs of EEG Applications on Embedded HMPs

Zain Taufique, Muhammad Awais Bin Altaf, Antonio Miele +2

Electroencephalography (EEG) recordings are analyzed using battery-powered wearable devices to monitor brain activities and neurological disorders. These applications require long…

cs.LG2022

Edge-centric Optimization of Multi-modal ML-driven eHealth Applications

Anil Kanduri, Sina Shahhosseini, Emad Kasaeyan Naeini +4

Smart eHealth applications deliver personalized and preventive digital healthcare services to clients through remote sensing, continuous monitoring, and data analytics. Smart eHeal…

cs.LG20211 cited

AMSER: Adaptive Multi-modal Sensing for Energy Efficient and Resilient eHealth Systems

Emad Kasaeyan Naeini, Sina Shahhosseini, Anil Kanduri +3

eHealth systems deliver critical digital healthcare and wellness services for users by continuously monitoring physiological and contextual data. eHealth applications use multi-mod…