Machine learning based estimation of dynamic balance and gait adaptability in persons with neurological diseases using inertial sensors
Abstract Poor dynamic balance and impaired gait adaptation to different contexts are hallmarks of people with neurological disorders (PwND), leading to difficulties in daily life and increased fall risk. Frequent assessment of dynamic balance and gait adaptability is therefore essential for monitori...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-05-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-35744-x |
| _version_ | 1827939945414656000 |
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| author | Piergiuseppe Liuzzi Ilaria Carpinella Denise Anastasi Elisa Gervasoni Tiziana Lencioni Rita Bertoni Maria Chiara Carrozza Davide Cattaneo Maurizio Ferrarin Andrea Mannini |
| author_facet | Piergiuseppe Liuzzi Ilaria Carpinella Denise Anastasi Elisa Gervasoni Tiziana Lencioni Rita Bertoni Maria Chiara Carrozza Davide Cattaneo Maurizio Ferrarin Andrea Mannini |
| author_sort | Piergiuseppe Liuzzi |
| collection | DOAJ |
| description | Abstract Poor dynamic balance and impaired gait adaptation to different contexts are hallmarks of people with neurological disorders (PwND), leading to difficulties in daily life and increased fall risk. Frequent assessment of dynamic balance and gait adaptability is therefore essential for monitoring the evolution of these impairments and/or the long-term effects of rehabilitation. The modified dynamic gait index (mDGI) is a validated clinical test specifically devoted to evaluating gait facets in clinical settings under a physiotherapist’s supervision. The need of a clinical environment, consequently, limits the number of assessments. Wearable sensors are increasingly used to measure balance and locomotion in real-world contexts and may permit an increase in monitoring frequency. This study aims to provide a preliminary test of this opportunity by using nested cross-validated machine learning regressors to predict the mDGI scores of 95 PwND via inertial signals collected from short steady-state walking bouts derived from the 6-minute walk test. Four different models were compared, one for each pathology (multiple sclerosis, Parkinson’s disease, and stroke) and one for the pooled multipathological cohort. Model explanations were computed on the best-performing solution; the model trained on the multipathological cohort yielded a median (interquartile range) absolute test error of 3.58 (5.38) points. In total, 76% of the predictions were within the mDGI’s minimal detectable change of 5 points. These results confirm that steady-state walking measurements provide information about dynamic balance and gait adaptability and can help clinicians identify important features to improve upon during rehabilitation. Future developments will include training of the method using short steady-state walking bouts in real-world settings, analysing the feasibility of this solution to intensify performance monitoring, providing prompt detection of worsening/improvements, and complementing clinical assessments. |
| first_indexed | 2024-03-13T09:03:12Z |
| format | Article |
| id | doaj.art-f92456a799414b66a70a651a9989694b |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-13T09:03:12Z |
| publishDate | 2023-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-f92456a799414b66a70a651a9989694b2023-05-28T11:15:40ZengNature PortfolioScientific Reports2045-23222023-05-0113111510.1038/s41598-023-35744-xMachine learning based estimation of dynamic balance and gait adaptability in persons with neurological diseases using inertial sensorsPiergiuseppe Liuzzi0Ilaria Carpinella1Denise Anastasi2Elisa Gervasoni3Tiziana Lencioni4Rita Bertoni5Maria Chiara Carrozza6Davide Cattaneo7Maurizio Ferrarin8Andrea Mannini9AIRLab, IRCCS Fondazione Don Carlo Gnocchi ONLUSLAMoBIR and LaRiCE, IRCCS Fondazione Don Carlo Gnocchi ONLUSLAMoBIR and LaRiCE, IRCCS Fondazione Don Carlo Gnocchi ONLUSLAMoBIR and LaRiCE, IRCCS Fondazione Don Carlo Gnocchi ONLUSLAMoBIR and LaRiCE, IRCCS Fondazione Don Carlo Gnocchi ONLUSLAMoBIR and LaRiCE, IRCCS Fondazione Don Carlo Gnocchi ONLUSScuola Superiore Sant’Anna, Istituto di BioRoboticaLAMoBIR and LaRiCE, IRCCS Fondazione Don Carlo Gnocchi ONLUSLAMoBIR and LaRiCE, IRCCS Fondazione Don Carlo Gnocchi ONLUSAIRLab, IRCCS Fondazione Don Carlo Gnocchi ONLUSAbstract Poor dynamic balance and impaired gait adaptation to different contexts are hallmarks of people with neurological disorders (PwND), leading to difficulties in daily life and increased fall risk. Frequent assessment of dynamic balance and gait adaptability is therefore essential for monitoring the evolution of these impairments and/or the long-term effects of rehabilitation. The modified dynamic gait index (mDGI) is a validated clinical test specifically devoted to evaluating gait facets in clinical settings under a physiotherapist’s supervision. The need of a clinical environment, consequently, limits the number of assessments. Wearable sensors are increasingly used to measure balance and locomotion in real-world contexts and may permit an increase in monitoring frequency. This study aims to provide a preliminary test of this opportunity by using nested cross-validated machine learning regressors to predict the mDGI scores of 95 PwND via inertial signals collected from short steady-state walking bouts derived from the 6-minute walk test. Four different models were compared, one for each pathology (multiple sclerosis, Parkinson’s disease, and stroke) and one for the pooled multipathological cohort. Model explanations were computed on the best-performing solution; the model trained on the multipathological cohort yielded a median (interquartile range) absolute test error of 3.58 (5.38) points. In total, 76% of the predictions were within the mDGI’s minimal detectable change of 5 points. These results confirm that steady-state walking measurements provide information about dynamic balance and gait adaptability and can help clinicians identify important features to improve upon during rehabilitation. Future developments will include training of the method using short steady-state walking bouts in real-world settings, analysing the feasibility of this solution to intensify performance monitoring, providing prompt detection of worsening/improvements, and complementing clinical assessments.https://doi.org/10.1038/s41598-023-35744-x |
| spellingShingle | Piergiuseppe Liuzzi Ilaria Carpinella Denise Anastasi Elisa Gervasoni Tiziana Lencioni Rita Bertoni Maria Chiara Carrozza Davide Cattaneo Maurizio Ferrarin Andrea Mannini Machine learning based estimation of dynamic balance and gait adaptability in persons with neurological diseases using inertial sensors Scientific Reports |
| title | Machine learning based estimation of dynamic balance and gait adaptability in persons with neurological diseases using inertial sensors |
| title_full | Machine learning based estimation of dynamic balance and gait adaptability in persons with neurological diseases using inertial sensors |
| title_fullStr | Machine learning based estimation of dynamic balance and gait adaptability in persons with neurological diseases using inertial sensors |
| title_full_unstemmed | Machine learning based estimation of dynamic balance and gait adaptability in persons with neurological diseases using inertial sensors |
| title_short | Machine learning based estimation of dynamic balance and gait adaptability in persons with neurological diseases using inertial sensors |
| title_sort | machine learning based estimation of dynamic balance and gait adaptability in persons with neurological diseases using inertial sensors |
| url | https://doi.org/10.1038/s41598-023-35744-x |
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