Vision-based Assessment of Balance Control in Elderly People
| Autor: | Nicola Lorusso, Laura Romeo, Maria Teresa Angelillo, Roberto Marani, Grazia Cicirelli |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Information management
030506 rehabilitation Computer science business.industry Feature extraction Control (management) Decision tree Image processing Machine learning computer.software_genre Pipeline (software) 03 medical and health sciences OpenPose 0302 clinical medicine Classifier (linguistics) Fall risk assessment low-cost cameras neurodegenerative diseases Artificial intelligence 0305 other medical science business computer 030217 neurology & neurosurgery Balance (ability) |
| Zdroj: | The 15th Edition of IEEE International Symposium on Medical Measurements and Applications, Virtual, 01/06/2020-01/07/2020 info:cnr-pdr/source/autori:Laura Romeo, Roberto Marani, Nicola Lorusso, Maria Teresa Angelillo, Grazia Cicirelli/congresso_nome:The 15th Edition of IEEE International Symposium on Medical Measurements and Applications/congresso_luogo:Virtual/congresso_data:01%2F06%2F2020-01%2F07%2F2020/anno:2020/pagina_da:/pagina_a:/intervallo_pagine MeMeA |
| Popis: | Falls represent one of the most serious clinical problems in the elderly population. This risk is even more important in people suffering from neurodegenerative problems. This work aims to instrumentally assess the balance performance of elderly people and specifically those suffering from neurodegenerative diseases, to obtain an objective evaluation of their risk of falls. This paper presents a vision-based system made of three low-cost cameras, able to automatically infer important mobility parameters by observing the execution of well-established tests for stability assessment. This result is achieved by a dedicated image processing pipeline, which processes videos to get dynamic user skeletons, and the following strategy for information management, which targets to feature extraction. This information finally feeds a classifier, namely a decision tree, trained to predict the risk of fall of patients within 5 classes of interest. Actual experiments performed on actual video recordings prove a good agreement of results with those expected, labeled by expert therapists, with final prediction accuracy of 79.1%. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|