Publicação
A convolutional neural network based model for daily motor activities recognition: a contribution for Parkinson’s disease
| Resumo: | Daily motor activities are affected by motor disabilities caused by Parkinson's disease (PD). Monitoring motor disabilities frequently observed in PD is difficult for physicians, as they are limited to the information observed or self-reported during routine consultations, resulting in subjectivity and limited assessment. Thus, it is necessary to evaluate more frequently and objectively, ideally in a continuous manner including daily life tasks. While wearable sensory devices, such as inertial sensors, and their applications are steadily growing, the advances in artificial intelligence studies have revolutionized their ability to extract deeply hidden information for accurate detection and interpretation of motor activities. However, further studies are required, mainly focused on PD. This study aimed to implement a deep learning (DL) based model for recognizing daily motor activities based on inertial data to contribute to PD. The model relied on a convolutional neural network (CNN) architecture, trained and tested on a created custom dataset. We further benchmarked our model against other popular DL frameworks. The dataset included inertial data captured from 18 patients while performing trivial quotidian tasks, such as walking, turning, sitting, and lying. We hypothesized that a DL model based on a CNN architecture could be an appropriate solution for modeling daily motor non-steady and steady-state tasks from a single inertial sensor. We measured an F1 score of 0.906 and an accuracy of 91.1 % on final testing with our optimized CNN model, being the tasks of standing and walking the most accurately recognized by the model. Future challenges should cover exploring attention-based models and increasing the dataset. |
|---|---|
| Autores principais: | Gonçalves, Helena R. |
| Outros Autores: | Martins, Luís Barreiros; Ribeiro, Nuno Miguel Ferrete; Abreu, Luís Filipe Simões; Rodrigues, Ana Margarida Fernandes Marques; Santos, Cristina |
| Assunto: | Activities recognition Convolutional neural network Deep learning Motor monitoring Parkinson's disease |
| Ano: | 2025 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| Resumo: | Daily motor activities are affected by motor disabilities caused by Parkinson's disease (PD). Monitoring motor disabilities frequently observed in PD is difficult for physicians, as they are limited to the information observed or self-reported during routine consultations, resulting in subjectivity and limited assessment. Thus, it is necessary to evaluate more frequently and objectively, ideally in a continuous manner including daily life tasks. While wearable sensory devices, such as inertial sensors, and their applications are steadily growing, the advances in artificial intelligence studies have revolutionized their ability to extract deeply hidden information for accurate detection and interpretation of motor activities. However, further studies are required, mainly focused on PD. This study aimed to implement a deep learning (DL) based model for recognizing daily motor activities based on inertial data to contribute to PD. The model relied on a convolutional neural network (CNN) architecture, trained and tested on a created custom dataset. We further benchmarked our model against other popular DL frameworks. The dataset included inertial data captured from 18 patients while performing trivial quotidian tasks, such as walking, turning, sitting, and lying. We hypothesized that a DL model based on a CNN architecture could be an appropriate solution for modeling daily motor non-steady and steady-state tasks from a single inertial sensor. We measured an F1 score of 0.906 and an accuracy of 91.1 % on final testing with our optimized CNN model, being the tasks of standing and walking the most accurately recognized by the model. Future challenges should cover exploring attention-based models and increasing the dataset. |
|---|