Gait Variability to Phenotype Common Orthopedic Gait Impairments Using Wearable Sensors
Mobility impairments are a common symptom of age-related degenerative diseases. Gait features can discriminate those with mobility disorders from healthy individuals, yet phenotyping specific pathologies remains challenging. This study aims to identify if gait parameters derived from two foot-mounte...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-11-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/23/9301 |
| _version_ | 1797462152130330624 |
|---|---|
| author | Junichi Kushioka Ruopeng Sun Wei Zhang Amir Muaremi Heike Leutheuser Charles A. Odonkor Matthew Smuck |
| author_facet | Junichi Kushioka Ruopeng Sun Wei Zhang Amir Muaremi Heike Leutheuser Charles A. Odonkor Matthew Smuck |
| author_sort | Junichi Kushioka |
| collection | DOAJ |
| description | Mobility impairments are a common symptom of age-related degenerative diseases. Gait features can discriminate those with mobility disorders from healthy individuals, yet phenotyping specific pathologies remains challenging. This study aims to identify if gait parameters derived from two foot-mounted inertial measurement units (IMU) during the 6 min walk test (6MWT) can phenotype mobility impairment from different pathologies (Lumbar spinal stenosis (LSS)—neurogenic diseases, and knee osteoarthritis (KOA)—structural joint disease). Bilateral foot-mounted IMU data during the 6MWT were collected from patients with LSS and KOA and matched healthy controls (N = 30, 10 for each group). Eleven gait parameters representing four domains (pace, rhythm, asymmetry, variability) were derived for each minute of the 6MWT. In the entire 6MWT, gait parameters in all four domains distinguished between controls and both disease groups; however, the disease groups demonstrated no statistical differences, with a trend toward higher stride length variability in the LSS group (<i>p</i> = 0.057). Additional minute-by-minute comparisons identified stride length variability as a statistically significant marker between disease groups during the middle portion of 6WMT (3rd min: <i>p</i> ≤ 0.05; 4th min: <i>p =</i> 0.06). These findings demonstrate that gait variability measures are a potential biomarker to phenotype mobility impairment from different pathologies. Increased gait variability indicates loss of gait rhythmicity, a common feature in neurologic impairment of locomotor control, thus reflecting the underlying mechanism for the gait impairment in LSS. Findings from this work also identify the middle portion of the 6MWT as a potential window to detect subtle gait differences between individuals with different origins of gait impairment. |
| first_indexed | 2024-03-09T17:32:26Z |
| format | Article |
| id | doaj.art-f29c173f6e6148b79d688c3735e47de7 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T17:32:26Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f29c173f6e6148b79d688c3735e47de72023-11-24T12:11:55ZengMDPI AGSensors1424-82202022-11-012223930110.3390/s22239301Gait Variability to Phenotype Common Orthopedic Gait Impairments Using Wearable SensorsJunichi Kushioka0Ruopeng Sun1Wei Zhang2Amir Muaremi3Heike Leutheuser4Charles A. Odonkor5Matthew Smuck6Department of Orthopaedic Surgery, Stanford University, Stanford, CA 94305, USADepartment of Orthopaedic Surgery, Stanford University, Stanford, CA 94305, USALaboratory of Movement Analysis and Measurement, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, SwitzerlandNovartis Institutes for BioMedical Research, 4056 Basel, SwitzerlandMachine Learning and Data Analytics Lab (MaD Lab), Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91052 Erlangen, GermanyDepartment of Orthopedics and Rehabilitation, Division of Physiatry, Yale School of Medicine, New Haven, CT 06510, USADepartment of Orthopaedic Surgery, Stanford University, Stanford, CA 94305, USAMobility impairments are a common symptom of age-related degenerative diseases. Gait features can discriminate those with mobility disorders from healthy individuals, yet phenotyping specific pathologies remains challenging. This study aims to identify if gait parameters derived from two foot-mounted inertial measurement units (IMU) during the 6 min walk test (6MWT) can phenotype mobility impairment from different pathologies (Lumbar spinal stenosis (LSS)—neurogenic diseases, and knee osteoarthritis (KOA)—structural joint disease). Bilateral foot-mounted IMU data during the 6MWT were collected from patients with LSS and KOA and matched healthy controls (N = 30, 10 for each group). Eleven gait parameters representing four domains (pace, rhythm, asymmetry, variability) were derived for each minute of the 6MWT. In the entire 6MWT, gait parameters in all four domains distinguished between controls and both disease groups; however, the disease groups demonstrated no statistical differences, with a trend toward higher stride length variability in the LSS group (<i>p</i> = 0.057). Additional minute-by-minute comparisons identified stride length variability as a statistically significant marker between disease groups during the middle portion of 6WMT (3rd min: <i>p</i> ≤ 0.05; 4th min: <i>p =</i> 0.06). These findings demonstrate that gait variability measures are a potential biomarker to phenotype mobility impairment from different pathologies. Increased gait variability indicates loss of gait rhythmicity, a common feature in neurologic impairment of locomotor control, thus reflecting the underlying mechanism for the gait impairment in LSS. Findings from this work also identify the middle portion of the 6MWT as a potential window to detect subtle gait differences between individuals with different origins of gait impairment.https://www.mdpi.com/1424-8220/22/23/9301lumbar spinal stenosisknee osteoarthritiswearable IMU sensorgait variabilitygait impairment |
| spellingShingle | Junichi Kushioka Ruopeng Sun Wei Zhang Amir Muaremi Heike Leutheuser Charles A. Odonkor Matthew Smuck Gait Variability to Phenotype Common Orthopedic Gait Impairments Using Wearable Sensors Sensors lumbar spinal stenosis knee osteoarthritis wearable IMU sensor gait variability gait impairment |
| title | Gait Variability to Phenotype Common Orthopedic Gait Impairments Using Wearable Sensors |
| title_full | Gait Variability to Phenotype Common Orthopedic Gait Impairments Using Wearable Sensors |
| title_fullStr | Gait Variability to Phenotype Common Orthopedic Gait Impairments Using Wearable Sensors |
| title_full_unstemmed | Gait Variability to Phenotype Common Orthopedic Gait Impairments Using Wearable Sensors |
| title_short | Gait Variability to Phenotype Common Orthopedic Gait Impairments Using Wearable Sensors |
| title_sort | gait variability to phenotype common orthopedic gait impairments using wearable sensors |
| topic | lumbar spinal stenosis knee osteoarthritis wearable IMU sensor gait variability gait impairment |
| url | https://www.mdpi.com/1424-8220/22/23/9301 |
| work_keys_str_mv | AT junichikushioka gaitvariabilitytophenotypecommonorthopedicgaitimpairmentsusingwearablesensors AT ruopengsun gaitvariabilitytophenotypecommonorthopedicgaitimpairmentsusingwearablesensors AT weizhang gaitvariabilitytophenotypecommonorthopedicgaitimpairmentsusingwearablesensors AT amirmuaremi gaitvariabilitytophenotypecommonorthopedicgaitimpairmentsusingwearablesensors AT heikeleutheuser gaitvariabilitytophenotypecommonorthopedicgaitimpairmentsusingwearablesensors AT charlesaodonkor gaitvariabilitytophenotypecommonorthopedicgaitimpairmentsusingwearablesensors AT matthewsmuck gaitvariabilitytophenotypecommonorthopedicgaitimpairmentsusingwearablesensors |