Evaluating Gait Impairment in Parkinson’s Disease from Instrumented Insole and IMU Sensor Data
Parkinson’s disease (PD) is characterized by a variety of motor and non-motor symptoms, some of them pertaining to gait and balance. The use of sensors for the monitoring of patients’ mobility and the extraction of gait parameters, has emerged as an objective method for assessing the efficacy of the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/8/3902 |
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| author | Vassilis Tsakanikas Adamantios Ntanis George Rigas Christos Androutsos Dimitrios Boucharas Nikolaos Tachos Vasileios Skaramagkas Chariklia Chatzaki Zinovia Kefalopoulou Manolis Tsiknakis Dimitrios Fotiadis |
| author_facet | Vassilis Tsakanikas Adamantios Ntanis George Rigas Christos Androutsos Dimitrios Boucharas Nikolaos Tachos Vasileios Skaramagkas Chariklia Chatzaki Zinovia Kefalopoulou Manolis Tsiknakis Dimitrios Fotiadis |
| author_sort | Vassilis Tsakanikas |
| collection | DOAJ |
| description | Parkinson’s disease (PD) is characterized by a variety of motor and non-motor symptoms, some of them pertaining to gait and balance. The use of sensors for the monitoring of patients’ mobility and the extraction of gait parameters, has emerged as an objective method for assessing the efficacy of their treatment and the progression of the disease. To that end, two popular solutions are pressure insoles and body-worn IMU-based devices, which have been used for precise, continuous, remote, and passive gait assessment. In this work, insole and IMU-based solutions were evaluated for assessing gait impairment, and were subsequently compared, producing evidence to support the use of instrumentation in everyday clinical practice. The evaluation was conducted using two datasets, generated during a clinical study, in which patients with PD wore, simultaneously, a pair of instrumented insoles and a set of wearable IMU-based devices. The data from the study were used to extract and compare gait features, independently, from the two aforementioned systems. Subsequently, subsets comprised of the extracted features, were used by machine learning algorithms for gait impairment assessment. The results indicated that insole gait kinematic features were highly correlated with those extracted from IMU-based devices. Moreover, both had the capacity to train accurate machine learning models for the detection of PD gait impairment. |
| first_indexed | 2024-03-11T04:32:50Z |
| format | Article |
| id | doaj.art-3903b473cf69441fa220b8253d6b4598 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T04:32:50Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3903b473cf69441fa220b8253d6b45982023-11-17T21:16:15ZengMDPI AGSensors1424-82202023-04-01238390210.3390/s23083902Evaluating Gait Impairment in Parkinson’s Disease from Instrumented Insole and IMU Sensor DataVassilis Tsakanikas0Adamantios Ntanis1George Rigas2Christos Androutsos3Dimitrios Boucharas4Nikolaos Tachos5Vasileios Skaramagkas6Chariklia Chatzaki7Zinovia Kefalopoulou8Manolis Tsiknakis9Dimitrios Fotiadis10Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110 Ioannina, GreecePD Neurotechnology Ltd., GR 45500 Ioannina, GreecePD Neurotechnology Ltd., GR 45500 Ioannina, GreeceUnit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110 Ioannina, GreeceUnit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110 Ioannina, GreeceUnit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110 Ioannina, GreeceInstitute of Computer Science, Foundation for Research and Technology—Hellas, GR 70013 Heraklion, GreeceInstitute of Computer Science, Foundation for Research and Technology—Hellas, GR 70013 Heraklion, GreeceDepartment of Neurology, General University Hospital of Patras, GR 26504 Patras, GreeceInstitute of Computer Science, Foundation for Research and Technology—Hellas, GR 70013 Heraklion, GreeceUnit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110 Ioannina, GreeceParkinson’s disease (PD) is characterized by a variety of motor and non-motor symptoms, some of them pertaining to gait and balance. The use of sensors for the monitoring of patients’ mobility and the extraction of gait parameters, has emerged as an objective method for assessing the efficacy of their treatment and the progression of the disease. To that end, two popular solutions are pressure insoles and body-worn IMU-based devices, which have been used for precise, continuous, remote, and passive gait assessment. In this work, insole and IMU-based solutions were evaluated for assessing gait impairment, and were subsequently compared, producing evidence to support the use of instrumentation in everyday clinical practice. The evaluation was conducted using two datasets, generated during a clinical study, in which patients with PD wore, simultaneously, a pair of instrumented insoles and a set of wearable IMU-based devices. The data from the study were used to extract and compare gait features, independently, from the two aforementioned systems. Subsequently, subsets comprised of the extracted features, were used by machine learning algorithms for gait impairment assessment. The results indicated that insole gait kinematic features were highly correlated with those extracted from IMU-based devices. Moreover, both had the capacity to train accurate machine learning models for the detection of PD gait impairment.https://www.mdpi.com/1424-8220/23/8/3902Parkinson’s diseasegait analysisinstrumented insolesIMU sensorsdigital biomarkerssensor fusion |
| spellingShingle | Vassilis Tsakanikas Adamantios Ntanis George Rigas Christos Androutsos Dimitrios Boucharas Nikolaos Tachos Vasileios Skaramagkas Chariklia Chatzaki Zinovia Kefalopoulou Manolis Tsiknakis Dimitrios Fotiadis Evaluating Gait Impairment in Parkinson’s Disease from Instrumented Insole and IMU Sensor Data Sensors Parkinson’s disease gait analysis instrumented insoles IMU sensors digital biomarkers sensor fusion |
| title | Evaluating Gait Impairment in Parkinson’s Disease from Instrumented Insole and IMU Sensor Data |
| title_full | Evaluating Gait Impairment in Parkinson’s Disease from Instrumented Insole and IMU Sensor Data |
| title_fullStr | Evaluating Gait Impairment in Parkinson’s Disease from Instrumented Insole and IMU Sensor Data |
| title_full_unstemmed | Evaluating Gait Impairment in Parkinson’s Disease from Instrumented Insole and IMU Sensor Data |
| title_short | Evaluating Gait Impairment in Parkinson’s Disease from Instrumented Insole and IMU Sensor Data |
| title_sort | evaluating gait impairment in parkinson s disease from instrumented insole and imu sensor data |
| topic | Parkinson’s disease gait analysis instrumented insoles IMU sensors digital biomarkers sensor fusion |
| url | https://www.mdpi.com/1424-8220/23/8/3902 |
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