Abnormal Gait Detection Using Wearable Hall-Effect Sensors
Abnormalities and irregularities in walking (gait) are predictors and indicators of both disease and injury. Gait has traditionally been monitored and analyzed in clinical settings using complex video (camera-based) systems, pressure mats, or a combination thereof. Wearable gait sensors offer the op...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/4/1206 |
| _version_ | 1827601599850086400 |
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| author | Courtney Chheng Denise Wilson |
| author_facet | Courtney Chheng Denise Wilson |
| author_sort | Courtney Chheng |
| collection | DOAJ |
| description | Abnormalities and irregularities in walking (gait) are predictors and indicators of both disease and injury. Gait has traditionally been monitored and analyzed in clinical settings using complex video (camera-based) systems, pressure mats, or a combination thereof. Wearable gait sensors offer the opportunity to collect data in natural settings and to complement data collected in clinical settings, thereby offering the potential to improve quality of care and diagnosis for those whose gait varies from healthy patterns of movement. This paper presents a gait monitoring system designed to be worn on the inner knee or upper thigh. It consists of low-power Hall-effect sensors positioned on one leg and a compact magnet positioned on the opposite leg. Wireless data collected from the sensor system were used to analyze stride width, stride width variability, cadence, and cadence variability for four different individuals engaged in normal gait, two types of abnormal gait, and two types of irregular gait. Using leg gap variability as a proxy for stride width variability, 81% of abnormal or irregular strides were accurately identified as different from normal stride. Cadence was surprisingly 100% accurate in identifying strides which strayed from normal, but variability in cadence provided no useful information. This highly sensitive, non-contact Hall-effect sensing method for gait monitoring offers the possibility for detecting visually imperceptible gait variability in natural settings. These nuanced changes in gait are valuable for predicting early stages of disease and also for indicating progress in recovering from injury. |
| first_indexed | 2024-03-09T04:59:47Z |
| format | Article |
| id | doaj.art-2cbe49db68104be886fb8fdb03419b37 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T04:59:47Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-2cbe49db68104be886fb8fdb03419b372023-12-03T13:00:39ZengMDPI AGSensors1424-82202021-02-01214120610.3390/s21041206Abnormal Gait Detection Using Wearable Hall-Effect SensorsCourtney Chheng0Denise Wilson1Department of Electrical Engineering, University of Washington Bothell, Bothell, WA 98011, USADepartment of Electrical and Computer Engineering, University of Washington Seattle, Seattle, WA 98195, USAAbnormalities and irregularities in walking (gait) are predictors and indicators of both disease and injury. Gait has traditionally been monitored and analyzed in clinical settings using complex video (camera-based) systems, pressure mats, or a combination thereof. Wearable gait sensors offer the opportunity to collect data in natural settings and to complement data collected in clinical settings, thereby offering the potential to improve quality of care and diagnosis for those whose gait varies from healthy patterns of movement. This paper presents a gait monitoring system designed to be worn on the inner knee or upper thigh. It consists of low-power Hall-effect sensors positioned on one leg and a compact magnet positioned on the opposite leg. Wireless data collected from the sensor system were used to analyze stride width, stride width variability, cadence, and cadence variability for four different individuals engaged in normal gait, two types of abnormal gait, and two types of irregular gait. Using leg gap variability as a proxy for stride width variability, 81% of abnormal or irregular strides were accurately identified as different from normal stride. Cadence was surprisingly 100% accurate in identifying strides which strayed from normal, but variability in cadence provided no useful information. This highly sensitive, non-contact Hall-effect sensing method for gait monitoring offers the possibility for detecting visually imperceptible gait variability in natural settings. These nuanced changes in gait are valuable for predicting early stages of disease and also for indicating progress in recovering from injury.https://www.mdpi.com/1424-8220/21/4/1206gait monitoringHall-effect sensorsmagnetic sensorswearable sensorsgait irregularitiesstride |
| spellingShingle | Courtney Chheng Denise Wilson Abnormal Gait Detection Using Wearable Hall-Effect Sensors Sensors gait monitoring Hall-effect sensors magnetic sensors wearable sensors gait irregularities stride |
| title | Abnormal Gait Detection Using Wearable Hall-Effect Sensors |
| title_full | Abnormal Gait Detection Using Wearable Hall-Effect Sensors |
| title_fullStr | Abnormal Gait Detection Using Wearable Hall-Effect Sensors |
| title_full_unstemmed | Abnormal Gait Detection Using Wearable Hall-Effect Sensors |
| title_short | Abnormal Gait Detection Using Wearable Hall-Effect Sensors |
| title_sort | abnormal gait detection using wearable hall effect sensors |
| topic | gait monitoring Hall-effect sensors magnetic sensors wearable sensors gait irregularities stride |
| url | https://www.mdpi.com/1424-8220/21/4/1206 |
| work_keys_str_mv | AT courtneychheng abnormalgaitdetectionusingwearablehalleffectsensors AT denisewilson abnormalgaitdetectionusingwearablehalleffectsensors |