no code implementations • 21 Apr 2023 • Jake Stuchbury-Wass, Erika Bondareva, Kayla-Jade Butkow, Sanja Scepanovic, Zoran Radivojevic, Cecilia Mascolo
Our evaluation shows for the first time that we can successfully extract HR from audio collected from a wearable on the abdomen.
no code implementations • 30 Mar 2023 • Chi Ian Tang, Lorena Qendro, Dimitris Spathis, Fahim Kawsar, Cecilia Mascolo, Akhil Mathur
Kaizen is able to balance the trade-off between knowledge retention and learning from new data with an end-to-end model, paving the way for practical deployment of continual learning systems.
no code implementations • 13 Mar 2023 • Tong Xia, Jing Han, Abhirup Ghosh, Cecilia Mascolo
In this paper, we propose FedLoss, a novel cross-device FL framework for health diagnostics.
no code implementations • 20 Nov 2022 • Yu Wu, Dimitris Spathis, Hong Jia, Ignacio Perez-Pozuelo, Tomas I. Gonzales, Soren Brage, Nicholas Wareham, Cecilia Mascolo
Deep learning models have shown great promise in various healthcare applications.
1 code implementation • 6 May 2022 • Dimitris Spathis, Ignacio Perez-Pozuelo, Tomas I. Gonzales, Yu Wu, Soren Brage, Nicholas Wareham, Cecilia Mascolo
Cardiorespiratory fitness is an established predictor of metabolic disease and mortality.
no code implementations • 26 Apr 2022 • Dong Ma, Chi Ian Tang, Cecilia Mascolo
Many deep learning applications, like keyword spotting, require the incorporation of new concepts (classes) over time, referred to as Class Incremental Learning (CIL).
no code implementations • 8 Mar 2022 • Young D. Kwon, Jagmohan Chauhan, Cecilia Mascolo
In this paper, we propose YONO, a product quantization (PQ) based approach that compresses multiple heterogeneous models and enables in-memory model execution and switching for dissimilar multi-task learning on MCUs.
no code implementations • 21 Feb 2022 • Anish Das, Young D. Kwon, Jagmohan Chauhan, Cecilia Mascolo
Deep Learning (DL) has shown impressive performance in many mobile applications.
no code implementations • 17 Feb 2022 • Harry Coppock, Alican Akman, Christian Bergler, Maurice Gerczuk, Chloë Brown, Jagmohan Chauhan, Andreas Grammenos, Apinan Hasthanasombat, Dimitris Spathis, Tong Xia, Pietro Cicuta, Jing Han, Shahin Amiriparian, Alice Baird, Lukas Stappen, Sandra Ottl, Panagiotis Tzirakis, Anton Batliner, Cecilia Mascolo, Björn W. Schuller
The COVID-19 pandemic has caused massive humanitarian and economic damage.
no code implementations • 19 Jan 2022 • Zahra Tarkhani, Lorena Qendro, Malachy O'Connor Brown, Oscar Hill, Cecilia Mascolo, Anil Madhavapeddy
Consequently, they are susceptible to a multiplicity of attacks across the hardware, software, and networking stacks used that can leak users' brainwave data or at worst relinquish control of BCI-assisted devices to remote attackers.
no code implementations • 4 Jan 2022 • Ting Dang, Jing Han, Tong Xia, Dimitris Spathis, Erika Bondareva, Chloë Siegele-Brown, Jagmohan Chauhan, Andreas Grammenos, Apinan Hasthanasombat, Andres Floto, Pietro Cicuta, Cecilia Mascolo
Recent work has shown the potential of using audio data (eg, cough, breathing, and voice) in the screening for COVID-19.
no code implementations • 16 Dec 2021 • Tong Xia, Jing Han, Cecilia Mascolo
A biosignal is a signal that can be continuously measured from human bodies, such as respiratory sounds, heart activity (ECG), brain waves (EEG), etc, based on which, machine learning models have been developed with very promising performance for automatic disease detection and health status monitoring.
no code implementations • 13 Nov 2021 • Kevalee Shah, Dimitris Spathis, Chi Ian Tang, Cecilia Mascolo
Vast quantities of person-generated health data (wearables) are collected but the process of annotating to feed to machine learning models is impractical.
no code implementations • 25 Oct 2021 • Young D. Kwon, Jagmohan Chauhan, Abhishek Kumar, Pan Hui, Cecilia Mascolo
Our findings suggest that replay with exemplars-based schemes such as iCaRL has the best performance trade-offs, even in complex scenarios, at the expense of some storage space (few MBs) for training examples (1% to 5%).
no code implementations • 9 Aug 2021 • Erika Bondareva, Jing Han, William Bradlow, Cecilia Mascolo
Cardiovascular (CV) diseases are the leading cause of death in the world, and auscultation is typically an essential part of a cardiovascular examination.
no code implementations • 9 Aug 2021 • Erika Bondareva, Elín Rós Hauksdóttir, Cecilia Mascolo
Bruxism is a disorder characterised by teeth grinding and clenching, and many bruxism sufferers are not aware of this disorder until their dental health professional notices permanent teeth wear.
no code implementations • 21 Jul 2021 • Lorena Qendro, Alexander Campbell, Pietro Liò, Cecilia Mascolo
Moreover, these pipelines are deterministic in nature, making them unable to capture predictive uncertainty.
Electroencephalogram (EEG) Vocal Bursts Intensity Prediction
no code implementations • 29 Jun 2021 • Jing Han, Tong Xia, Dimitris Spathis, Erika Bondareva, Chloë Brown, Jagmohan Chauhan, Ting Dang, Andreas Grammenos, Apinan Hasthanasombat, Andres Floto, Pietro Cicuta, Cecilia Mascolo
In this paper, we explore the realistic performance of audio-based digital testing of COVID-19.
1 code implementation • 21 Jun 2021 • Benjamin Lucas Searle, Dimitris Spathis, Marios Constantinides, Daniele Quercia, Cecilia Mascolo
Body-focused repetitive behaviors (BFRBs), like face-touching or skin-picking, are hand-driven behaviors which can damage one's appearance, if not identified early and treated.
no code implementations • 14 Jun 2021 • Young D. Kwon, Jagmohan Chauhan, Cecilia Mascolo
Various incremental learning (IL) approaches have been proposed to help deep learning models learn new tasks/classes continuously without forgetting what was learned previously (i. e., avoid catastrophic forgetting).
no code implementations • 6 Jun 2021 • Sandra Servia-Rodriguez, Cecilia Mascolo, Young D. Kwon
Our experiments prove the robustness and reliability of the learned models to adapt to the changing sensing environment, and show the suitability of using uncertainty of the predictions to assess their reliability.
no code implementations • 29 Apr 2021 • Krittika D'Silva, Jordan Cambe, Anastasios Noulas, Cecilia Mascolo, Adam Waksman
Relative to state-of-the-art deep learning models, our model reduces the RSME by ~ 28% in London and ~ 13% in Paris.
no code implementations • 28 Apr 2021 • Jordan Cambe, Krittika D'Silva, Anastasios Noulas, Cecilia Mascolo, Adam Waksman
Lastly, we build a supervised machine learning model to predict the impact of a given new venue on its local retail ecosystem.
no code implementations • 5 Apr 2021 • Tong Xia, Jing Han, Lorena Qendro, Ting Dang, Cecilia Mascolo
To handle these issues, we propose an ensemble framework where multiple deep learning models for sound-based COVID-19 detection are developed from different but balanced subsets from original data.
no code implementations • 24 Feb 2021 • Björn W. Schuller, Anton Batliner, Christian Bergler, Cecilia Mascolo, Jing Han, Iulia Lefter, Heysem Kaya, Shahin Amiriparian, Alice Baird, Lukas Stappen, Sandra Ottl, Maurice Gerczuk, Panagiotis Tzirakis, Chloë Brown, Jagmohan Chauhan, Andreas Grammenos, Apinan Hasthanasombat, Dimitris Spathis, Tong Xia, Pietro Cicuta, Leon J. M. Rothkrantz, Joeri Zwerts, Jelle Treep, Casper Kaandorp
The INTERSPEECH 2021 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the COVID-19 Cough and COVID-19 Speech Sub-Challenges, a binary classification on COVID-19 infection has to be made based on coughing sounds and speech; in the Escalation SubChallenge, a three-way assessment of the level of escalation in a dialogue is featured; and in the Primates Sub-Challenge, four species vs background need to be classified.
no code implementations • 11 Feb 2021 • Lorena Qendro, Jagmohan Chauhan, Alberto Gil C. P. Ramos, Cecilia Mascolo
To meet the energy and latency requirements of these embedded platforms the framework is built from the ground up to provide predictive uncertainty based only on one forward pass and a negligible amount of additional matrix multiplications with theoretically proven correctness.
1 code implementation • 11 Feb 2021 • Chi Ian Tang, Ignacio Perez-Pozuelo, Dimitris Spathis, Soren Brage, Nick Wareham, Cecilia Mascolo
Machine learning and deep learning have shown great promise in mobile sensing applications, including Human Activity Recognition.
1 code implementation • NeurIPS 2020 • Dionysis Manousakas, Zuheng Xu, Cecilia Mascolo, Trevor Campbell
Standard Bayesian inference algorithms are prohibitively expensive in the regime of modern large-scale data.
1 code implementation • 23 Nov 2020 • Chi Ian Tang, Ignacio Perez-Pozuelo, Dimitris Spathis, Cecilia Mascolo
Human Activity Recognition (HAR) constitutes one of the most important tasks for wearable and mobile sensing given its implications in human well-being and health monitoring.
1 code implementation • 18 Nov 2020 • Dimitris Spathis, Ignacio Perez-Pozuelo, Soren Brage, Nicholas J. Wareham, Cecilia Mascolo
Our contributions are two-fold: i) the pre-training task creates a model that can accurately forecast HR based only on cheap activity sensors, and ii) we leverage the information captured through this task by proposing a simple method to aggregate the learnt latent representations (embeddings) from the window-level to user-level.
2 code implementations • 9 Nov 2020 • Dimitris Spathis, Ignacio Perez-Pozuelo, Soren Brage, Nicholas J. Wareham, Cecilia Mascolo
To date, research on sensor-equipped mobile devices has primarily focused on the purely supervised task of human activity recognition (walking, running, etc), demonstrating limited success in inferring high-level health outcomes from low-level signals, such as acceleration.
1 code implementation • 31 Aug 2020 • Dionysis Manousakas, Cecilia Mascolo
Modern machine learning applications should be able to address the intrinsic challenges arising over inference on massive real-world datasets, including scalability and robustness to outliers.
4 code implementations • 10 Jun 2020 • Chloë Brown, Jagmohan Chauhan, Andreas Grammenos, Jing Han, Apinan Hasthanasombat, Dimitris Spathis, Tong Xia, Pietro Cicuta, Cecilia Mascolo
This work opens the door to further investigation of how automatically analysed respiratory patterns could be used as pre-screening signals to aid COVID-19 diagnosis.
1 code implementation • NeurIPS 2020 • Andreas Grammenos, Rodrigo Mendoza-Smith, Jon Crowcroft, Cecilia Mascolo
We present a federated, asynchronous, and $(\varepsilon, \delta)$-differentially private algorithm for PCA in the memory-limited setting.
2 code implementations • 19 May 2019 • Xiao Zhou, Cecilia Mascolo, Zhongxiang Zhao
Point-of-Interest (POI) recommender systems play a vital role in people's lives by recommending unexplored POIs to users and have drawn extensive attention from both academia and industry.
no code implementations • 26 Jun 2014 • Chloë Brown, Christos Efstratiou, Ilias Leontiadis, Daniele Quercia, Cecilia Mascolo, James Scott, Peter Key
The layouts of the buildings we live in shape our everyday lives.
Computers and Society Human-Computer Interaction Social and Information Networks