publication . Other literature type . Conference object . 2019

Multiple-Instance Learning for In-The-Wild Parkinsonian Tremor Detection

Anastasios Delopoulos; Alexandros Papadopoulos; Ray K. Chaudhuri; Lisa Klingelhoefer; Konstantinos Kyritsis; Sevasti Bostanjopoulou;
Open Access
  • Published: 31 Jul 2019
  • Publisher: Zenodo
Abstract
Parkinson’s Disease (PD) is a neurodegenerative disorder that manifests through slowly progressing symptoms, such as tremor, voice degradation and bradykinesia. Automated detection of such symptoms has recently received much attention by the research community, owing to the clinical benefits associated with the early diagnosis of the disease. Unfortunately, most of the approaches proposed so far, operate under a strictly laboratory setting, thus limiting their potential applicability in real world conditions. In this work, we present a method for automatically detecting tremorous episodes related to PD, based on acceleration signals. We propose to address the pr...
Read more
Persistent Identifiers

doi: 10.5281/zenodo.3676524 , 10.5281/zenodo.3676525 , 10.1109/embc.2019.8856314

Subjects
free text keywords: Feature learning, Computer science, Accelerometer, Research community, Speech recognition, Limiting, Deep learning, Pooling, Artificial intelligence, business.industry, business
Related Organizations
Funded by
EC| i-PROGNOSIS
Project
i-PROGNOSIS
Intelligent Parkinson eaRly detectiOn Guiding NOvel Supportive InterventionS
  • Funder: European Commission (EC)
  • Project Code: 690494
  • Funding stream: H2020 | RIA
Download fromView all 5 versions
Zenodo
Other literature type . 2019
Provider: Datacite
ZENODO
Conference object . 2019
Provider: ZENODO
Zenodo
Other literature type . 2019
Provider: Datacite
https://zenodo.org/record/3676...
Conference object
Provider: UnpayWall
View more
Powered by OpenAIRE Research Graph
Any information missing or wrong?Report an Issue