Search Results for author:
Found 8 papers, 2 papers with code
Finding Order in Chaos: A Novel Data Augmentation Method for Time Series in Contrastive Learning
The success of contrastive learning is well known to be dependent on data augmentation.
BeliefPPG: Uncertainty-aware Heart Rate Estimation from PPG signals via Belief Propagation
We present a novel learning-based method that achieves state-of-the-art performance on several heart rate estimation benchmarks extracted from photoplethysmography signals (PPG).
Heart rate estimation
Photoplethysmography (PPG) heart rate estimation
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Neural Contextual Bandits Based Dynamic Sensor Selection for Low-Power Body-Area Networks
Providing health monitoring devices with machine intelligence is important for enabling automatic mobile healthcare applications.
Energy-Efficient Real-Time Heart Monitoring on Edge-Fog-Cloud Internet-of-Medical-Things
However, continuous monitoring of ECG signals is challenging in low-power wearable devices due to energy and memory constraints.
Energy-efficient Blood Pressure Monitoring based on Single-site Photoplethysmogram on Wearable Devices
The paper proposes accurate Blood Pressure Monitoring (BPM) based on a single-site Photoplethysmographic (PPG) sensor and provides an energy-efficient solution on edge cuffless wearable devices.
Single-Channel EEG Based Arousal Level Estimation Using Multitaper Spectrum Estimation at Low-Power Wearable Devices
This paper proposes a novel lightweight method using the multitaper power spectrum to estimate arousal levels at wearable devices.
LENS: Layer Distribution Enabled Neural Architecture Search in Edge-Cloud Hierarchies
Edge-Cloud hierarchical systems employing intelligence through Deep Neural Networks (DNNs) endure the dilemma of workload distribution within them.
AHAR: Adaptive CNN for Energy-efficient Human Activity Recognition in Low-power Edge Devices
Unlike traditional early exit architecture that makes the exit decision based on classification confidence, AHAR proposes a novel adaptive architecture that uses an output block predictor to select a portion of the baseline architecture to use during the inference phase.
