Juan Camilo Vásquez Correa
Latest Projects
Training Network on Automatic Processing of PAthological Speech
TAPAS
Modelling the progression of neurological diseases
Develop speech technology that can allow unobtrusive monitoring of many kinds of neurological diseases. The state of a patient can degrade slowly between medical check-ups. We want to track the state of a patient unobtrusively without the feeling of constant supervision. At the same time the privacy of the patient has to be respected. We will concentrate on PD and thus on acoustic cues of changes. The algorithms should run on a smartphone, track acoustic changes during regular phone conversations over time and thus have to be low-resource. No speech recognition will be used and only some analysis parameters of the conversation are stored on the phone and transferred to the server.
Research and innovation programme
Marie Sklodowska-Curie Grant Agreement No. 766287.
Conexión salud
Study to measure the level of Communication and information technologies (TICs) in the public Health Services institutions of Colombia
Role in Project: software developer
Asynchronous Non-Intrusive Multi-Modal Analysis of Bio-Signals for the Automatic evaluation of the Neurological State of People With Parkinson's Disease. (2017-2019)
The main aim of this project is to develop a method to evaluate the motor impairments and the neurological state of patients with Parkinson's Disease using three bio-signals: speech, handwriting and gait.
This research work is suitable for the development of computer aided tools to evaluate the motor impairments of patients with different neurodegenerative disorders considering information from different bio--signals to take accurate decisions about the treatment of the patients.
CODI
University of Antioquia
2017-2019
Remote Monitoring of Neurodegeneration through Speech (2016)
Alzheimer’s disease (AD) is the most common neurodegenerative disorder. It generally deteriorates memory function, then language, then executive function to the point where simple activities of daily living (ADLs) become difficult. Parkinson’s disease (PD) is the second most common neurodegenerative disease, also primarily affecting individuals of advanced age. Its cardinal symptoms include akinesia, tremor, rigidity, and postural imbalance. Together, AD and PD afflict approximately 55 million people, and there is no cure.
Currently, professional or informal caregivers look after these individuals, either at home or in long-term care facilities. Caregiving is already a great, expensive burden on the system, but things will soon become far worse. Populations of many nations are aging rapidly and, with over 12% of people above the age of 65 having either AD or PD, incidence rates are set to triple over the next few decades.
Monitoring and assessment are vital, but current models are unsustainable. Patients need to be monitored regularly (e.g. to check if medication needs to be updated), which is expensive, time-consuming, and especially difficult when travelling to the closest neurologist is unrealistic. Monitoring patients using non-intrusive sensors to collect data during ADLs from speech, gait, and handwriting, can help to reduce the burden.
Automatic recognition of emotion from speech signals in non-controlled environments. (2014)
Human emotions detection considering speech signals is a field that has attracted the attention of the research community since the last years. Several situations where the human integrity and security is at risk have been addressed; particularly the analysis of speech in emergency calls or in call-centers, are an interesting scenario.
In this project is proposed the development of a methodology to classify different types of emotions such as anger, anxiety, disgust, and desperation, in scenarios where the speech signal is contaminated with noise or is coded by telephone channels.
Young researches and innovators COLCIENCIAS
2014-2015
Analysis of the discriminant capacity of phonation, articulation and prosody features from patients with Parkinson's disease on preclinic and advanced stages for the development of computer aided tools for supporting the diagnosis and monitoring of the patients. (2013-2016)
Parkinson's Disease (PD) is the second neurological condition more prevalent after Alheimer. It is fundamental identify early markers of the disease. 90% of people with PD present speech disorders, but only from 3% to 4% recieve speech treatment. In this project will be developed methodologies based on signal processing to establish if the speech signals represent an early marker of PD. Also will be applied machine learning techniques to develop methodologies that allow make an objective following about of speech quality in patients with PD.
COLCIENCIAS
2013-2016
