EarGait: Estimation of Temporal Gait Parameters from Hearing Aid Integrated Inertial Sensors
Wearable sensors are able to monitor physical health in a home environment and detect changes in gait patterns over time. To ensure long-term user engagement, wearable sensors need to be seamlessly integrated into the user’s daily life, such as hearing aids or earbuds. Therefore, we present EarGait,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/14/6565 |
| _version_ | 1797587507257278464 |
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| author | Ann-Kristin Seifer Eva Dorschky Arne Küderle Hamid Moradi Ronny Hannemann Björn M. Eskofier |
| author_facet | Ann-Kristin Seifer Eva Dorschky Arne Küderle Hamid Moradi Ronny Hannemann Björn M. Eskofier |
| author_sort | Ann-Kristin Seifer |
| collection | DOAJ |
| description | Wearable sensors are able to monitor physical health in a home environment and detect changes in gait patterns over time. To ensure long-term user engagement, wearable sensors need to be seamlessly integrated into the user’s daily life, such as hearing aids or earbuds. Therefore, we present EarGait, an open-source Python toolbox for gait analysis using inertial sensors integrated into hearing aids. This work contributes a validation for gait event detection algorithms and the estimation of temporal parameters using ear-worn sensors. We perform a comparative analysis of two algorithms based on acceleration data and propose a modified version of one of the algorithms. We conducted a study with healthy young and elderly participants to record walking data using the hearing aid’s integrated sensors and an optical motion capture system as a reference. All algorithms were able to detect gait events (initial and terminal contacts), and the improved algorithm performed best, detecting 99.8% of initial contacts and obtaining a mean stride time error of 12 ± 32 ms. The existing algorithms faced challenges in determining the laterality of gait events. To address this limitation, we propose modifications that enhance the determination of the step laterality (ipsi- or contralateral), resulting in a 50% reduction in stride time error. Moreover, the improved version is shown to be robust to different study populations and sampling frequencies but is sensitive to walking speed. This work establishes a solid foundation for a comprehensive gait analysis system integrated into hearing aids that will facilitate continuous and long-term home monitoring. |
| first_indexed | 2024-03-11T00:39:56Z |
| format | Article |
| id | doaj.art-45d76dbaa87f46c6b881c3c6c27d52ac |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T00:39:56Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-45d76dbaa87f46c6b881c3c6c27d52ac2023-11-18T21:19:29ZengMDPI AGSensors1424-82202023-07-012314656510.3390/s23146565EarGait: Estimation of Temporal Gait Parameters from Hearing Aid Integrated Inertial SensorsAnn-Kristin Seifer0Eva Dorschky1Arne Küderle2Hamid Moradi3Ronny Hannemann4Björn M. Eskofier5Machine Learning and Data Analytics Lab (MaD Lab), Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91052 Erlangen, GermanyMachine Learning and Data Analytics Lab (MaD Lab), Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91052 Erlangen, GermanyMachine Learning and Data Analytics Lab (MaD Lab), Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91052 Erlangen, GermanyMachine Learning and Data Analytics Lab (MaD Lab), Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91052 Erlangen, GermanyWS Audiology, 91058 Erlangen, GermanyMachine Learning and Data Analytics Lab (MaD Lab), Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91052 Erlangen, GermanyWearable sensors are able to monitor physical health in a home environment and detect changes in gait patterns over time. To ensure long-term user engagement, wearable sensors need to be seamlessly integrated into the user’s daily life, such as hearing aids or earbuds. Therefore, we present EarGait, an open-source Python toolbox for gait analysis using inertial sensors integrated into hearing aids. This work contributes a validation for gait event detection algorithms and the estimation of temporal parameters using ear-worn sensors. We perform a comparative analysis of two algorithms based on acceleration data and propose a modified version of one of the algorithms. We conducted a study with healthy young and elderly participants to record walking data using the hearing aid’s integrated sensors and an optical motion capture system as a reference. All algorithms were able to detect gait events (initial and terminal contacts), and the improved algorithm performed best, detecting 99.8% of initial contacts and obtaining a mean stride time error of 12 ± 32 ms. The existing algorithms faced challenges in determining the laterality of gait events. To address this limitation, we propose modifications that enhance the determination of the step laterality (ipsi- or contralateral), resulting in a 50% reduction in stride time error. Moreover, the improved version is shown to be robust to different study populations and sampling frequencies but is sensitive to walking speed. This work establishes a solid foundation for a comprehensive gait analysis system integrated into hearing aids that will facilitate continuous and long-term home monitoring.https://www.mdpi.com/1424-8220/23/14/6565earablesgait analysisgait event detectioninertial sensorSSAwearables |
| spellingShingle | Ann-Kristin Seifer Eva Dorschky Arne Küderle Hamid Moradi Ronny Hannemann Björn M. Eskofier EarGait: Estimation of Temporal Gait Parameters from Hearing Aid Integrated Inertial Sensors Sensors earables gait analysis gait event detection inertial sensor SSA wearables |
| title | EarGait: Estimation of Temporal Gait Parameters from Hearing Aid Integrated Inertial Sensors |
| title_full | EarGait: Estimation of Temporal Gait Parameters from Hearing Aid Integrated Inertial Sensors |
| title_fullStr | EarGait: Estimation of Temporal Gait Parameters from Hearing Aid Integrated Inertial Sensors |
| title_full_unstemmed | EarGait: Estimation of Temporal Gait Parameters from Hearing Aid Integrated Inertial Sensors |
| title_short | EarGait: Estimation of Temporal Gait Parameters from Hearing Aid Integrated Inertial Sensors |
| title_sort | eargait estimation of temporal gait parameters from hearing aid integrated inertial sensors |
| topic | earables gait analysis gait event detection inertial sensor SSA wearables |
| url | https://www.mdpi.com/1424-8220/23/14/6565 |
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