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KID-PPG: Knowledge Informed Deep Learning for Extracting Heart Rate from a Smartwatch
Related publications (38)
Predicting the long-term collective behaviour of fish pairs with deep learning
Francesco Mondada, Alexandre Massoud Alahi, Vaios Papaspyros
Modern computing has enhanced our understanding of how social interactions shape collective behaviour in animal societies. Although analytical models dominate in studying collective behaviour, this study introduces a deep learning model to assess social in ...
2024Glenohumeral joint force prediction with deep learning
Dominique Pioletti, Alexandre Terrier, Patrick Goetti, Philippe Büchler
Deep learning models (DLM) are efficient replacements for computationally intensive optimization techniques. Musculoskeletal models (MSM) typically involve resource-intensive optimization processes for determining joint and muscle forces. Consequently, DLM ...
London2024DELMEP: a deep learning algorithm for automated annotation of motor evoked potential latencies
Friedhelm Christoph Hummel, Claudia Bigoni, Nima Taherinejad
The analysis of motor evoked potentials (MEPs) generated by transcranial magnetic stimulation (TMS) is crucial in research and clinical medical practice. MEPs are characterized by their latency and the treatment of a single patient may require the characte ...
NATURE PORTFOLIO2023Multienzyme deep learning models improve peptide de novo sequencing by mass spectrometry proteomics
Author summaryIn recent years, the application of deep learning represented a breakthrough in the mass spectrometry (MS) field by improving the assignment of the correct sequence of amino acids from observable MS spectra without prior knowledge, also known ...
PUBLIC LIBRARY SCIENCE2023Breaking the Curse of Dimensionality in Deep Neural Networks by Learning Invariant Representations
Artificial intelligence, particularly the subfield of machine learning, has seen a paradigm shift towards data-driven models that learn from and adapt to data. This has resulted in unprecedented advancements in various domains such as natural language proc ...
EPFL2023Bridging the gap between model-driven and data-driven methods in the era of Big Data
Data-driven and model-driven methodologies can be regarded as competitive fields since they tackle similar problems such as prediction. However, these two fields can learn from each other to improve themselves. Indeed, data-driven methodologies have been d ...
EPFL2022Mechanical intelligence for learning embodied sensor-object relationships
Intelligence involves processing sensory experiences into representations useful for prediction. Understanding sensory experiences and building these contextual representations without prior knowledge of sensor models and environment is a challenging unsup ...
NATURE PORTFOLIO2022Reinforcement Learning for the occupant-centric operation of building energy systems: Theoretical and experimental investigations
Occupant behavior, defined as the presence and energy-related actions of occupants, is today known as a key driver of building energy use. Closing the gap between what is provided by building energy systems and what is actually needed by occupants requires ...
EPFL2022Word-level Embeddings for Cross-Task Transfer Learning in Speech Processing
Milos Cernak, Pierre Anton Beckmann
Recent breakthroughs in deep learning often rely on representation learning and knowledge transfer. In recent years, unsupervised and self-supervised techniques for learning speech representation were developed to foster automatic speech recognition. Up to ...
EUROPEAN ASSOC SIGNAL SPEECH & IMAGE PROCESSING-EURASIP2021Deep Learning Approaches for Auditory Perception in Robotics
Auditory perception is an essential part of a robotic system in Human-Robot Interaction (HRI), and creating an artificial auditory perception system that is on par with human has been a long-standing goal for researchers. In fact, this is a challenging res ...
EPFL2021