Gait-based Parkinson’s disease diagnosis and severity classification using force sensors and machine learning
Abstract A dual-stage model for classifying Parkinson’s disease severity, through a detailed analysis of Gait signals using force sensors and machine learning approaches, is proposed in this study. Parkinson’s disease is the primary neurodegenerative disorder that results in a gradual reduction in m...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-83357-9 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841559706340950016 |
|---|---|
| author | Navita Pooja Mittal Yogesh Kumar Sharma Anjani Kumar Rai Sarita Simaiya Umesh Kumar Lilhore Vimal Kumar |
| author_facet | Navita Pooja Mittal Yogesh Kumar Sharma Anjani Kumar Rai Sarita Simaiya Umesh Kumar Lilhore Vimal Kumar |
| author_sort | Navita |
| collection | DOAJ |
| description | Abstract A dual-stage model for classifying Parkinson’s disease severity, through a detailed analysis of Gait signals using force sensors and machine learning approaches, is proposed in this study. Parkinson’s disease is the primary neurodegenerative disorder that results in a gradual reduction in motor function. Early detection and monitoring of the disease progression is highly challenging due to the gradual progression of symptoms and the inadequacy of conventional methods in identifying subtle changes in mobility. The proposed dual-stage model utilized a hypertuned Random Forest Tree (RFT) to classify the subjects into PD and non-PD classes at Stage 1 and a hypertuned Ensemble Regressor (ER) to predict the severity of illness at Stage 2. Further, we have implemented the proposed model on the data signals gathered from both feet of 166 participants using Vertical Ground Reaction Force Sensors (VGRF). The dataset comprised 93 persons with Parkinson’s disease and 73 healthy controls. The dataset (imbalance) collected from both feet is passed to the preprocessing phase (for balancing data using the SMOTE method), followed by the feature extraction phase to extract features related to time, frequency, spatial, and temporal features domains that are highly effective for detecting and assigning severity levels of PD. A Recursive Feature Elimination method is also used to select the optimal set of features to improve the model performance. It is acknowledged that the early detection of Parkinson’s disease is contingent upon critical parameters, including stride length, stance duration, swing interval, double limb support, step time, and step length. The crucial evaluation metrics used for evaluating model performance include accuracy, mean absolute error, and root mean square error. The findings indicate that the suggested model significantly surpasses current methodologies. It attained an accuracy of 97.5 ± 2.1%, Sensitivity of 97% ± 2.5%, and average Specificity of 95% ± 2.2% in differentiating between PD and non-PD participants utilizing RFT and evaluated disease severity with an average accuracy of 96.4 ± 2.3%, an average mean absolute error of 0.065 ± 0.024, and a root mean square error of 0.080 ± 0.06. The results indicate that the proposed dual-stage model is exceptionally successful in the early detection and severity assessment of Parkinson’s disease and demonstrates better efficacy than alternative models. |
| format | Article |
| id | doaj-art-fc68727971374ea8bf6aede25f6194bb |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-fc68727971374ea8bf6aede25f6194bb2025-01-05T12:19:43ZengNature PortfolioScientific Reports2045-23222025-01-0115112310.1038/s41598-024-83357-9Gait-based Parkinson’s disease diagnosis and severity classification using force sensors and machine learningNavita0Pooja Mittal1Yogesh Kumar Sharma2Anjani Kumar Rai3Sarita Simaiya4Umesh Kumar Lilhore5Vimal Kumar6Department of Computer Science & Applications, Maharshi Dayanand UniversityDepartment of Computer Science & Applications, Maharshi Dayanand UniversityDepartment of Computer Science & Engineering, KoneruLakshmaiah Education FoundationDepartment of CEA, GLA UniversityDepartment of Computer Science & Engineering, Galgotias UniversityDepartment of Computer Science & Engineering, Galgotias UniversityDepartment of Computer Science & Engineering, Galgotias UniversityAbstract A dual-stage model for classifying Parkinson’s disease severity, through a detailed analysis of Gait signals using force sensors and machine learning approaches, is proposed in this study. Parkinson’s disease is the primary neurodegenerative disorder that results in a gradual reduction in motor function. Early detection and monitoring of the disease progression is highly challenging due to the gradual progression of symptoms and the inadequacy of conventional methods in identifying subtle changes in mobility. The proposed dual-stage model utilized a hypertuned Random Forest Tree (RFT) to classify the subjects into PD and non-PD classes at Stage 1 and a hypertuned Ensemble Regressor (ER) to predict the severity of illness at Stage 2. Further, we have implemented the proposed model on the data signals gathered from both feet of 166 participants using Vertical Ground Reaction Force Sensors (VGRF). The dataset comprised 93 persons with Parkinson’s disease and 73 healthy controls. The dataset (imbalance) collected from both feet is passed to the preprocessing phase (for balancing data using the SMOTE method), followed by the feature extraction phase to extract features related to time, frequency, spatial, and temporal features domains that are highly effective for detecting and assigning severity levels of PD. A Recursive Feature Elimination method is also used to select the optimal set of features to improve the model performance. It is acknowledged that the early detection of Parkinson’s disease is contingent upon critical parameters, including stride length, stance duration, swing interval, double limb support, step time, and step length. The crucial evaluation metrics used for evaluating model performance include accuracy, mean absolute error, and root mean square error. The findings indicate that the suggested model significantly surpasses current methodologies. It attained an accuracy of 97.5 ± 2.1%, Sensitivity of 97% ± 2.5%, and average Specificity of 95% ± 2.2% in differentiating between PD and non-PD participants utilizing RFT and evaluated disease severity with an average accuracy of 96.4 ± 2.3%, an average mean absolute error of 0.065 ± 0.024, and a root mean square error of 0.080 ± 0.06. The results indicate that the proposed dual-stage model is exceptionally successful in the early detection and severity assessment of Parkinson’s disease and demonstrates better efficacy than alternative models.https://doi.org/10.1038/s41598-024-83357-9Machine learningParkinson’s diseaseRandom Forest treeRecursive feature eliminationSynthetic minority over-sampling technique |
| spellingShingle | Navita Pooja Mittal Yogesh Kumar Sharma Anjani Kumar Rai Sarita Simaiya Umesh Kumar Lilhore Vimal Kumar Gait-based Parkinson’s disease diagnosis and severity classification using force sensors and machine learning Scientific Reports Machine learning Parkinson’s disease Random Forest tree Recursive feature elimination Synthetic minority over-sampling technique |
| title | Gait-based Parkinson’s disease diagnosis and severity classification using force sensors and machine learning |
| title_full | Gait-based Parkinson’s disease diagnosis and severity classification using force sensors and machine learning |
| title_fullStr | Gait-based Parkinson’s disease diagnosis and severity classification using force sensors and machine learning |
| title_full_unstemmed | Gait-based Parkinson’s disease diagnosis and severity classification using force sensors and machine learning |
| title_short | Gait-based Parkinson’s disease diagnosis and severity classification using force sensors and machine learning |
| title_sort | gait based parkinson s disease diagnosis and severity classification using force sensors and machine learning |
| topic | Machine learning Parkinson’s disease Random Forest tree Recursive feature elimination Synthetic minority over-sampling technique |
| url | https://doi.org/10.1038/s41598-024-83357-9 |
| work_keys_str_mv | AT navita gaitbasedparkinsonsdiseasediagnosisandseverityclassificationusingforcesensorsandmachinelearning AT poojamittal gaitbasedparkinsonsdiseasediagnosisandseverityclassificationusingforcesensorsandmachinelearning AT yogeshkumarsharma gaitbasedparkinsonsdiseasediagnosisandseverityclassificationusingforcesensorsandmachinelearning AT anjanikumarrai gaitbasedparkinsonsdiseasediagnosisandseverityclassificationusingforcesensorsandmachinelearning AT saritasimaiya gaitbasedparkinsonsdiseasediagnosisandseverityclassificationusingforcesensorsandmachinelearning AT umeshkumarlilhore gaitbasedparkinsonsdiseasediagnosisandseverityclassificationusingforcesensorsandmachinelearning AT vimalkumar gaitbasedparkinsonsdiseasediagnosisandseverityclassificationusingforcesensorsandmachinelearning |