Gait Monitoring and Analysis: A Mathematical Approach
Gait abnormalities are common in the elderly and individuals diagnosed with Parkinson’s, often leading to reduced mobility and increased fall risk. Monitoring and assessing gait patterns in these populations play a crucial role in understanding disease progression, early detection of motor impairmen...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/18/7743 |
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| author | Massimo Canonico Francesco Desimoni Alberto Ferrero Pietro Antonio Grassi Christopher Irwin Daiana Campani Alberto Dal Molin Massimiliano Panella Luca Magistrelli |
| author_facet | Massimo Canonico Francesco Desimoni Alberto Ferrero Pietro Antonio Grassi Christopher Irwin Daiana Campani Alberto Dal Molin Massimiliano Panella Luca Magistrelli |
| author_sort | Massimo Canonico |
| collection | DOAJ |
| description | Gait abnormalities are common in the elderly and individuals diagnosed with Parkinson’s, often leading to reduced mobility and increased fall risk. Monitoring and assessing gait patterns in these populations play a crucial role in understanding disease progression, early detection of motor impairments, and developing personalized rehabilitation strategies. In particular, by identifying gait irregularities at an early stage, healthcare professionals can implement timely interventions and personalized therapeutic approaches, potentially delaying the onset of severe motor symptoms and improving overall patient outcomes. In this paper, we studied older adults affected by chronic diseases and/or Parkinson’s disease by monitoring their gait due to wearable devices that can accurately detect a person’s movements. In our study, about 50 people were involved in the trial (20 with Parkinson’s disease and 30 people with chronic diseases) who have worn our device for at least 6 months. During the experimentation, each device collected 25 samples from the accelerometer sensor for each second. By analyzing those data, we propose a metric for the “gait quality” based on the measure of entropy obtained by applying the Fourier transform. |
| first_indexed | 2024-03-10T22:03:15Z |
| format | Article |
| id | doaj.art-5178f67f3ac34d559cc18fddcd45d7d9 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T22:03:15Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5178f67f3ac34d559cc18fddcd45d7d92023-11-19T12:53:36ZengMDPI AGSensors1424-82202023-09-012318774310.3390/s23187743Gait Monitoring and Analysis: A Mathematical ApproachMassimo Canonico0Francesco Desimoni1Alberto Ferrero2Pietro Antonio Grassi3Christopher Irwin4Daiana Campani5Alberto Dal Molin6Massimiliano Panella7Luca Magistrelli8Department of Sciences and Technological Innovation, University of Piemonte Orientale, 15121 Alessandria, ItalyDepartment of Sciences and Technological Innovation, University of Piemonte Orientale, 15121 Alessandria, ItalyDepartment of Sciences and Technological Innovation, University of Piemonte Orientale, 15121 Alessandria, ItalyDepartment of Sciences and Technological Innovation, University of Piemonte Orientale, 15121 Alessandria, ItalyDepartment of Sciences and Technological Innovation, University of Piemonte Orientale, 15121 Alessandria, ItalyDepartment of Translational Medicine, Università del Piemonte Orientale, 28100 Novara, ItalyDepartment of Translational Medicine, Università del Piemonte Orientale, 28100 Novara, ItalyDepartment of Translational Medicine, Università del Piemonte Orientale, 28100 Novara, ItalyDepartment of Translational Medicine, Università del Piemonte Orientale, 28100 Novara, ItalyGait abnormalities are common in the elderly and individuals diagnosed with Parkinson’s, often leading to reduced mobility and increased fall risk. Monitoring and assessing gait patterns in these populations play a crucial role in understanding disease progression, early detection of motor impairments, and developing personalized rehabilitation strategies. In particular, by identifying gait irregularities at an early stage, healthcare professionals can implement timely interventions and personalized therapeutic approaches, potentially delaying the onset of severe motor symptoms and improving overall patient outcomes. In this paper, we studied older adults affected by chronic diseases and/or Parkinson’s disease by monitoring their gait due to wearable devices that can accurately detect a person’s movements. In our study, about 50 people were involved in the trial (20 with Parkinson’s disease and 30 people with chronic diseases) who have worn our device for at least 6 months. During the experimentation, each device collected 25 samples from the accelerometer sensor for each second. By analyzing those data, we propose a metric for the “gait quality” based on the measure of entropy obtained by applying the Fourier transform.https://www.mdpi.com/1424-8220/23/18/7743cloud computingwearable devicesgait monitoringtelemedicine |
| spellingShingle | Massimo Canonico Francesco Desimoni Alberto Ferrero Pietro Antonio Grassi Christopher Irwin Daiana Campani Alberto Dal Molin Massimiliano Panella Luca Magistrelli Gait Monitoring and Analysis: A Mathematical Approach Sensors cloud computing wearable devices gait monitoring telemedicine |
| title | Gait Monitoring and Analysis: A Mathematical Approach |
| title_full | Gait Monitoring and Analysis: A Mathematical Approach |
| title_fullStr | Gait Monitoring and Analysis: A Mathematical Approach |
| title_full_unstemmed | Gait Monitoring and Analysis: A Mathematical Approach |
| title_short | Gait Monitoring and Analysis: A Mathematical Approach |
| title_sort | gait monitoring and analysis a mathematical approach |
| topic | cloud computing wearable devices gait monitoring telemedicine |
| url | https://www.mdpi.com/1424-8220/23/18/7743 |
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