Artificial Neural Network Detects Hip Muscle Forces as Determinant for Harmonic Walking in People after Stroke
Many recent studies have highlighted that the harmony of physiological walking is based on a specific proportion between the durations of the phases of the gait cycle. When this proportion is close to the so-called golden ratio (about 1.618), the gait cycle assumes an autosimilar fractal structure....
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-02-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/4/1374 |
| _version_ | 1827652856330584064 |
|---|---|
| author | Marco Iosa Maria Grazia Benedetti Gabriella Antonucci Stefano Paolucci Giovanni Morone |
| author_facet | Marco Iosa Maria Grazia Benedetti Gabriella Antonucci Stefano Paolucci Giovanni Morone |
| author_sort | Marco Iosa |
| collection | DOAJ |
| description | Many recent studies have highlighted that the harmony of physiological walking is based on a specific proportion between the durations of the phases of the gait cycle. When this proportion is close to the so-called golden ratio (about 1.618), the gait cycle assumes an autosimilar fractal structure. In stroke patients this harmony is altered, but it is unclear which factor is associated with the ratios between gait phases because these relationships are probably not linear. We used an artificial neural network to determine the weights associable to each factor for determining the ratio between gait phases and hence the harmony of walking. As expected, the gait ratio obtained as the ratio between stride duration and stance duration was found to be associated with walking speed and stride length, but also with hip muscle forces. These muscles could be important for exploiting the recovery of energy typical of the pendular mechanism of walking. Our study also highlighted that the results of an artificial neural network should be associated with a reliability analysis, being a non-deterministic approach. A good level of reliability was found for the findings of our study. |
| first_indexed | 2024-03-09T21:07:01Z |
| format | Article |
| id | doaj.art-466f3c420007411ebf128af9769133c3 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T21:07:01Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-466f3c420007411ebf128af9769133c32023-11-23T21:58:30ZengMDPI AGSensors1424-82202022-02-01224137410.3390/s22041374Artificial Neural Network Detects Hip Muscle Forces as Determinant for Harmonic Walking in People after StrokeMarco Iosa0Maria Grazia Benedetti1Gabriella Antonucci2Stefano Paolucci3Giovanni Morone4Department of Psychology, Sapienza University of Rome, 00185 Rome, ItalyPhysical Medicine and Rehabilitation Unit, IRCCS-Istituto Ortopedico Rizzoli, 40136 Bologna, ItalyDepartment of Psychology, Sapienza University of Rome, 00185 Rome, ItalyIRCCS Fondazione Santa Lucia, 00179 Rome, ItalyIRCCS Fondazione Santa Lucia, 00179 Rome, ItalyMany recent studies have highlighted that the harmony of physiological walking is based on a specific proportion between the durations of the phases of the gait cycle. When this proportion is close to the so-called golden ratio (about 1.618), the gait cycle assumes an autosimilar fractal structure. In stroke patients this harmony is altered, but it is unclear which factor is associated with the ratios between gait phases because these relationships are probably not linear. We used an artificial neural network to determine the weights associable to each factor for determining the ratio between gait phases and hence the harmony of walking. As expected, the gait ratio obtained as the ratio between stride duration and stance duration was found to be associated with walking speed and stride length, but also with hip muscle forces. These muscles could be important for exploiting the recovery of energy typical of the pendular mechanism of walking. Our study also highlighted that the results of an artificial neural network should be associated with a reliability analysis, being a non-deterministic approach. A good level of reliability was found for the findings of our study.https://www.mdpi.com/1424-8220/22/4/1374artificial intelligencegait analysisgaitgolden ratioquadricepsiliopsoas |
| spellingShingle | Marco Iosa Maria Grazia Benedetti Gabriella Antonucci Stefano Paolucci Giovanni Morone Artificial Neural Network Detects Hip Muscle Forces as Determinant for Harmonic Walking in People after Stroke Sensors artificial intelligence gait analysis gait golden ratio quadriceps iliopsoas |
| title | Artificial Neural Network Detects Hip Muscle Forces as Determinant for Harmonic Walking in People after Stroke |
| title_full | Artificial Neural Network Detects Hip Muscle Forces as Determinant for Harmonic Walking in People after Stroke |
| title_fullStr | Artificial Neural Network Detects Hip Muscle Forces as Determinant for Harmonic Walking in People after Stroke |
| title_full_unstemmed | Artificial Neural Network Detects Hip Muscle Forces as Determinant for Harmonic Walking in People after Stroke |
| title_short | Artificial Neural Network Detects Hip Muscle Forces as Determinant for Harmonic Walking in People after Stroke |
| title_sort | artificial neural network detects hip muscle forces as determinant for harmonic walking in people after stroke |
| topic | artificial intelligence gait analysis gait golden ratio quadriceps iliopsoas |
| url | https://www.mdpi.com/1424-8220/22/4/1374 |
| work_keys_str_mv | AT marcoiosa artificialneuralnetworkdetectshipmuscleforcesasdeterminantforharmonicwalkinginpeopleafterstroke AT mariagraziabenedetti artificialneuralnetworkdetectshipmuscleforcesasdeterminantforharmonicwalkinginpeopleafterstroke AT gabriellaantonucci artificialneuralnetworkdetectshipmuscleforcesasdeterminantforharmonicwalkinginpeopleafterstroke AT stefanopaolucci artificialneuralnetworkdetectshipmuscleforcesasdeterminantforharmonicwalkinginpeopleafterstroke AT giovannimorone artificialneuralnetworkdetectshipmuscleforcesasdeterminantforharmonicwalkinginpeopleafterstroke |