Machine learning for time series obtained in mHealth applications

• Grant number: 22/03176-1
• Support Opportunities: Research Grants - Initial Project
• Start date: February 01, 2023
• End date: January 31, 2028
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Diego Furtado Silva
• Grantee: Diego Furtado Silva
• Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
• City of the host institution: São Carlos
• Associated researchers: Audrey Borghi Silva ; Eamonn John Keogh ; Germain Forestier ; Gustavo Enrique de Almeida Prado Alves Batista ; Ricardo Cerri ; Ricardo Marcondes Marcacini ; Thiago Mazzu Do Nascimento
• Associated research grant(s): 25/19417-6 - Evaluation of Foundation Models for Physiological Signals, AP.R; 24/09747-6 - Resource-constrained neural models for time series classification and extrinsic regression, AP.R SPRINT
• Associated scholarship(s): 26/03741-1 - Multimodal Learning for Streaming Physiological Signals, BP.MS; 25/13341-8 - Machine Learning for Time Series in mHealth Applications - Semi-supervised Learning Algorithms, BP.DD; 25/04971-8 - Off-the-shelf algorithms for time series classification and extrinsic regression, BP.MS; + associated scholarships 24/14856-9 - Knowledge Distillation for Time Series Models, BP.MS; 24/07016-4 - Time Series Self-Supervised Representation Learning, BP.DR; 24/07047-7 - Off-the-shelf Algorithms for Classification and Extrinsic Regression, BP.IC; 23/05041-9 - Adapting Time Series Classification Algorithms to Regression, BP.IC; 23/05171-0 - Sumi-supervised learning algorithms in mHealth domain, BP.DD; 23/03069-3 - Time Series in mHealth Applications: Task Definition and Data Collection, BP.IC; 23/02680-0 - Transfer of Learning to Deal with Devices Heterogeneity, BP.MS - associated scholarships

Abstract

Monitoring physiological signs, vital signs, and other parameters that can be collected over time from individuals are essential in several tasks in healthcare, such as heart rate estimation and the identification of abnormal heartbeats. However, these time series are obtained by very expensive and usually not portable equipment. On the other hand, with the improvement and miniaturization of sensors capable of transmitting various data types, mobile and wearable devices have increasingly shown themselves as options to support medical decisions. Smartphones and smartwatches have increasingly accurate and diverse sensors, making the World Health Organization consider that mobile health (mHealth) may revolutionize how populations interact with public health systems. However, several scientific and technological challenges need to be overcome for mHealth applications to be viable in practice. Among these challenges are the need for low-cost methods, the heterogeneity and multimodality of the data, and the difficulty in obtaining annotated data. In this scenario, this project proposes investigating the use of Machine Learning for time series in mHealth applications. At the end of this research, we intend to have advanced the state-of-the-art for these applications and still make available the models generated for this, along with all other resources necessary for the advancement of research in the same domain. (AU)

Scientific publications

(6)

(The scientific publications listed on this page originate from the Web of Science or SciELO databases. Their authors have cited FAPESP grant or fellowship project numbers awarded to Principal Investigators or Fellowship Recipients, whether or not they are among the authors. This information is collected automatically and retrieved directly from those bibliometric databases.)

BARBOSA DE MEDEIROS JUNIOR, JOSE GILBERTO; DE MITRI, ANDRE GUARNIER; SILVA, DIEGO FURTADO. Semi-periodic Activation for Time Series Classification. INTELLIGENT SYSTEMS, BRACIS 2024, PT IV, v. 15415, p. 15-pg., 2025-01-01. (23/11775-5, 23/02680-0, 22/03176-1, 23/11745-9, 23/05041-9)

SILVA, RAFAEL DA COSTA; SILVA, DIEGO FURTADO. Tackling Low-Resource ECG Classification with Self-supervised Learning. INTELLIGENT SYSTEMS, BRACIS 2024, PT IV, v. 15415, p. 15-pg., 2025-01-01. (24/07016-4, 22/03176-1)

DONYAVI, ZAHRA; LI, FEIYU; ZHANG, YUNRUI; SILVA, DIEGO F.; BATISTA, GUSTAVO E.. Match: A Maximum-Likelihood Approach for Classification under Label Shift. PROCEEDINGS OF THE 31ST ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING V.2, KDD 2025, v. N/A, p. 10-pg., 2025-01-01. (22/03176-1)

SILVA, DIEGO F.; JUNIOR, JOSE G. B. DE M.; DOMINGUES, LUCAS V.; MAZRU-NASCIMENTO, THIAGO; ALMEIDA, JR; SPILIOPOULOU, M; ANDRADES, JAB; PLACIDI, G; GONZALEZ, AR; SICILIA, R; et al. Hemoglobin Estimation from Smartphone-Based Photoplethysmography with Small Data. 2023 IEEE 36TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS, CBMS, v. N/A, p. 4-pg., 2023-01-01. (23/02680-0, 22/03176-1)

GUIJO-RUBIO, DAVID; MIDDLEHURST, MATTHEW; ARCENCIO, GUILHERME; SILVA, DIEGO FURTADO; BAGNALL, ANTHONY. Unsupervised feature based algorithms for time series extrinsic regression. DATA MINING AND KNOWLEDGE DISCOVERY, v. 38, n. 4, p. 45-pg., 2024-05-19. (22/12486-4, 22/00305-5, 22/03176-1)

VANZIN, VINICIUS JOAO DE BARROS; MOREIRA, DILVAN DE ABREU; MARCACINI, RICARDO MARCONDES. LLM-based approaches for automated vocabulary mapping between SIGTAP and OMOP CDM concepts. ARTIFICIAL INTELLIGENCE IN MEDICINE, v. 168, p. 11-pg., 2025-07-23. (23/00488-5, 19/07665-4, 13/07375-0, 23/10100-4, 22/03176-1, 24/08485-8)