8 citations · 13 across the 19 of their papers we have counts for
20 papers
MiBOT: A head-worn robot that modulates cardiovascular responses through human-like soft massage
Alice Mylaeus, Stephanie Vogt, Berken Utku Demirel +4
Massage therapy is helpful for the rehabilitation of various diseases, such as headaches caused by migraines and stress. Existing robotic systems have focused on massage therapy on…
EgoHRV: Continuous Heart Rate Variability Estimation from Egocentric Systems for Autonomic Response and Skill Assessment
Berken Utku Demirel, Christian Holz
Egocentric vision systems capture human behavior from visible cues, but overlook physiological indicators of autonomic states such as stress, engagement, and attention. Heart rate…
FW-NKF: Frequency-Weighted Neural Kalman Filters
Adnan Harun Dogan, Berken Utku Demirel, Christian Holz
Robust state estimation is central to robotic autonomy, yet classical Kalman filters struggle with frequency-dependent disturbances and model mismatch such as sensor vibrations, el…
Temporal Cardiovascular Dynamics for Improved PPG-Based Heart Rate Estimation
Berken Utku Demirel, Christian Holz
The oscillations of the human heart rate are inherently complex and non-linear -- they are best described by mathematical chaos, and they present a challenge when applied to the pr…
Learning Without Augmenting: Unsupervised Time Series Representation Learning via Frame Projections
Berken Utku Demirel, Christian Holz
Self-supervised learning (SSL) has emerged as a powerful paradigm for learning representations without labeled data. Most SSL approaches rely on strong, well-established, handcraft…
Beyond Subjectivity: Continuous Cybersickness Detection Using EEG-based Multitaper Spectrum Estimation
Berken Utku Demirel, Adnan Harun Dogan, Juliete Rossie +2
Virtual reality (VR) presents immersive opportunities across many applications, yet the inherent risk of developing cybersickness during interaction can severely reduce enjoyment a…
Shifting the Paradigm: A Diffeomorphism Between Time Series Data Manifolds for Achieving Shift-Invariancy in Deep Learning
Berken Utku Demirel, Christian Holz
Deep learning models lack shift invariance, making them sensitive to input shifts that cause changes in output. While recent techniques seek to address this for images, our finding…
Tri-Spectral PPG: Robust Reflective Photoplethysmography by Fusing Multiple Wavelengths for Cardiac Monitoring
Manuel Meier, Berken Utku Demirel, Christian Holz
Multi-channel photoplethysmography (PPG) sensors have found widespread adoption in wearable devices for monitoring cardiac health. Channels thereby serve different functions -- whe…
WildPPG: A Real-World PPG Dataset of Long Continuous Recordings
Manuel Meier, Berken Utku Demirel, Christian Holz
Reflective photoplethysmography (PPG) has become the default sensing technique in wearable devices to monitor cardiac activity via a person's heart rate (HR). However, PPG-based HR…
Nightbeat: Heart Rate Estimation From a Wrist-Worn Accelerometer During Sleep
Max Moebus, Lars Hauptmann, Nicolas Kopp +3
Today's fitness bands and smartwatches typically track heart rates (HR) using optical sensors. Large behavioral studies such as the UK Biobank use activity trackers without such op…
An Unsupervised Approach for Periodic Source Detection in Time Series
Berken Utku Demirel, Christian Holz
Detection of periodic patterns of interest within noisy time series data plays a critical role in various tasks, spanning from health monitoring to behavior analysis. Existing lear…
Finding Order in Chaos: A Novel Data Augmentation Method for Time Series in Contrastive Learning
Berken Utku Demirel, Christian Holz
The success of contrastive learning is well known to be dependent on data augmentation. Although the degree of data augmentations has been well controlled by utilizing pre-defined…
BeliefPPG: Uncertainty-aware Heart Rate Estimation from PPG signals via Belief Propagation
Valentin Bieri, Paul Streli, Berken Utku Demirel +1
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). W…
Neural Contextual Bandits Based Dynamic Sensor Selection for Low-Power Body-Area Networks
Berken Utku Demirel, Luke Chen, Mohammad Abdullah Al Faruque
Providing health monitoring devices with machine intelligence is important for enabling automatic mobile healthcare applications. However, this brings additional challenges due to…
Energy-Efficient Real-Time Heart Monitoring on Edge-Fog-Cloud Internet-of-Medical-Things
Berken Utku Demirel, Islam Abdelsalam Bayoumy, Mohammad Abdullah Al Faruque
The recent developments in wearable devices and the Internet of Medical Things (IoMT) allow real-time monitoring and recording of electrocardiogram (ECG) signals. However, continuo…
Future of Smart Classroom in the Era of Wearable Neurotechnology
Mojtaba Taherisadr, Berken Utku Demirel, Mohammad Abdullah Al Faruque +1
Interdisciplinary research among engineering, computer science, and neuroscience to understand and utilize the human brain signals resulted in advances and widespread applicability…
Energy-efficient Blood Pressure Monitoring based on Single-site Photoplethysmogram on Wearable Devices
Wenrui Lin, Berken Utku Demirel, Mohammad Abdullah Al Faruque +1
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 wea…
Single-Channel EEG Based Arousal Level Estimation Using Multitaper Spectrum Estimation at Low-Power Wearable Devices
Berken Utku Demirel, Ivan Skelin, Haoxin Zhang +2
This paper proposes a novel lightweight method using the multitaper power spectrum to estimate arousal levels at wearable devices. We show that the spectral slope (1/f) of the elec…
LENS: Layer Distribution Enabled Neural Architecture Search in Edge-Cloud Hierarchies
Mohanad Odema, Nafiul Rashid, Berken Utku Demirel +1
Edge-Cloud hierarchical systems employing intelligence through Deep Neural Networks (DNNs) endure the dilemma of workload distribution within them. Previous solutions proposed to d…
AHAR: Adaptive CNN for Energy-efficient Human Activity Recognition in Low-power Edge Devices
Nafiul Rashid, Berken Utku Demirel, Mohammad Abdullah Al Faruque
Human Activity Recognition (HAR) is one of the key applications of health monitoring that requires continuous use of wearable devices to track daily activities. This paper proposes…