The Stumblemeter: Design and Validation of a System That Detects and Classifies Stumbles during Gait

Stumbling during gait is commonly encountered in patients who suffer from mild to serious walking problems, e.g., after stroke, in osteoarthritis, or amputees using a lower leg prosthesis. Instead of self-reporting, an objective assessment of the number of stumbles in daily life would inform clinici...

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Veröffentlicht in:	Sensors (Basel, Switzerland) Jg. 21; H. 19; S. 6636
Hauptverfasser:	Hartog, Dylan den, Harlaar, Jaap, Smit, Gerwin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 06.10.2021 MDPI
Schlagworte:	accelerometer Activities of daily living Algorithms Amputation detection Falls Fitness equipment Gait gyroscope inertial measurement unit Machine learning Older people Prevention Prostheses Sensors stumbling Velocity Walking
ISSN:	1424-8220, 1424-8220
Online-Zugang:	Volltext
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Abstract	Stumbling during gait is commonly encountered in patients who suffer from mild to serious walking problems, e.g., after stroke, in osteoarthritis, or amputees using a lower leg prosthesis. Instead of self-reporting, an objective assessment of the number of stumbles in daily life would inform clinicians more accurately and enable the evaluation of treatments that aim to achieve a safer walking pattern. An easy-to-use wearable might fulfill this need. The goal of the present study was to investigate whether a single inertial measurement unit (IMU) placed at the shank and machine learning algorithms could be used to detect and classify stumbling events in a dataset comprising of a wide variety of daily movements. Ten healthy test subjects were deliberately tripped by an unexpected and unseen obstacle while walking on a treadmill. The subjects stumbled a total of 276 times, both using an elevating recovery strategy and a lowering recovery strategy. Subjects also performed multiple Activities of Daily Living. During data processing, an event-defined window segmentation technique was used to trace high peaks in acceleration that could potentially be stumbles. In the reduced dataset, time windows were labelled with the aid of video annotation. Subsequently, discriminative features were extracted and fed to train seven different types of machine learning algorithms. Trained machine learning algorithms were validated using leave-one-subject-out cross-validation. Support Vector Machine (SVM) algorithms were most successful, and could detect and classify stumbles with 100% sensitivity, 100% specificity, and 96.7% accuracy in the independent testing dataset. The SVM algorithms were implemented in a user-friendly, freely available, stumble detection app named Stumblemeter. This work shows that stumble detection and classification based on SVM is accurate and ready to apply in clinical practice.
AbstractList	Stumbling during gait is commonly encountered in patients who suffer from mild to serious walking problems, e.g., after stroke, in osteoarthritis, or amputees using a lower leg prosthesis. Instead of self-reporting, an objective assessment of the number of stumbles in daily life would inform clinicians more accurately and enable the evaluation of treatments that aim to achieve a safer walking pattern. An easy-to-use wearable might fulfill this need. The goal of the present study was to investigate whether a single inertial measurement unit (IMU) placed at the shank and machine learning algorithms could be used to detect and classify stumbling events in a dataset comprising of a wide variety of daily movements. Ten healthy test subjects were deliberately tripped by an unexpected and unseen obstacle while walking on a treadmill. The subjects stumbled a total of 276 times, both using an elevating recovery strategy and a lowering recovery strategy. Subjects also performed multiple Activities of Daily Living. During data processing, an event-defined window segmentation technique was used to trace high peaks in acceleration that could potentially be stumbles. In the reduced dataset, time windows were labelled with the aid of video annotation. Subsequently, discriminative features were extracted and fed to train seven different types of machine learning algorithms. Trained machine learning algorithms were validated using leave-one-subject-out cross-validation. Support Vector Machine (SVM) algorithms were most successful, and could detect and classify stumbles with 100% sensitivity, 100% specificity, and 96.7% accuracy in the independent testing dataset. The SVM algorithms were implemented in a user-friendly, freely available, stumble detection app named Stumblemeter. This work shows that stumble detection and classification based on SVM is accurate and ready to apply in clinical practice. Stumbling during gait is commonly encountered in patients who suffer from mild to serious walking problems, e.g., after stroke, in osteoarthritis, or amputees using a lower leg prosthesis. Instead of self-reporting, an objective assessment of the number of stumbles in daily life would inform clinicians more accurately and enable the evaluation of treatments that aim to achieve a safer walking pattern. An easy-to-use wearable might fulfill this need. The goal of the present study was to investigate whether a single inertial measurement unit (IMU) placed at the shank and machine learning algorithms could be used to detect and classify stumbling events in a dataset comprising of a wide variety of daily movements. Ten healthy test subjects were deliberately tripped by an unexpected and unseen obstacle while walking on a treadmill. The subjects stumbled a total of 276 times, both using an elevating recovery strategy and a lowering recovery strategy. Subjects also performed multiple Activities of Daily Living. During data processing, an event-defined window segmentation technique was used to trace high peaks in acceleration that could potentially be stumbles. In the reduced dataset, time windows were labelled with the aid of video annotation. Subsequently, discriminative features were extracted and fed to train seven different types of machine learning algorithms. Trained machine learning algorithms were validated using leave-one-subject-out cross-validation. Support Vector Machine (SVM) algorithms were most successful, and could detect and classify stumbles with 100% sensitivity, 100% specificity, and 96.7% accuracy in the independent testing dataset. The SVM algorithms were implemented in a user-friendly, freely available, stumble detection app named Stumblemeter. This work shows that stumble detection and classification based on SVM is accurate and ready to apply in clinical practice.Stumbling during gait is commonly encountered in patients who suffer from mild to serious walking problems, e.g., after stroke, in osteoarthritis, or amputees using a lower leg prosthesis. Instead of self-reporting, an objective assessment of the number of stumbles in daily life would inform clinicians more accurately and enable the evaluation of treatments that aim to achieve a safer walking pattern. An easy-to-use wearable might fulfill this need. The goal of the present study was to investigate whether a single inertial measurement unit (IMU) placed at the shank and machine learning algorithms could be used to detect and classify stumbling events in a dataset comprising of a wide variety of daily movements. Ten healthy test subjects were deliberately tripped by an unexpected and unseen obstacle while walking on a treadmill. The subjects stumbled a total of 276 times, both using an elevating recovery strategy and a lowering recovery strategy. Subjects also performed multiple Activities of Daily Living. During data processing, an event-defined window segmentation technique was used to trace high peaks in acceleration that could potentially be stumbles. In the reduced dataset, time windows were labelled with the aid of video annotation. Subsequently, discriminative features were extracted and fed to train seven different types of machine learning algorithms. Trained machine learning algorithms were validated using leave-one-subject-out cross-validation. Support Vector Machine (SVM) algorithms were most successful, and could detect and classify stumbles with 100% sensitivity, 100% specificity, and 96.7% accuracy in the independent testing dataset. The SVM algorithms were implemented in a user-friendly, freely available, stumble detection app named Stumblemeter. This work shows that stumble detection and classification based on SVM is accurate and ready to apply in clinical practice.
Author	Hartog, Dylan den Smit, Gerwin Harlaar, Jaap
AuthorAffiliation	1 Department of Biomechanical Engineering, Delft University of Technology, 2628 CD Delft, The Netherlands; Dylan.den.Hartog@hotmail.com (D.d.H.); G.Smit@tudelft.nl (G.S.) 2 Department Orthopedics & Sports Medicine, Erasmus Medical Center, 3015 GD Rotterdam, The Netherlands
AuthorAffiliation_xml	– name: 1 Department of Biomechanical Engineering, Delft University of Technology, 2628 CD Delft, The Netherlands; Dylan.den.Hartog@hotmail.com (D.d.H.); G.Smit@tudelft.nl (G.S.) – name: 2 Department Orthopedics & Sports Medicine, Erasmus Medical Center, 3015 GD Rotterdam, The Netherlands
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Cites_doi	10.1152/jn.2000.83.4.2093 10.1093/ageing/26.4.261 10.1093/gerona/56.7.M428 10.1109/IEMBS.2007.4352627 10.1017/CBO9781107298019 10.1093/ageing/17.6.365 10.1682/JRRD.2014.01.0031 10.1007/978-1-4614-6849-3 10.1682/JRRD.2007.09.0145 10.1186/s12984-015-0067-8 10.1016/j.apmr.2008.11.007 10.1016/j.jbiomech.2014.05.009 10.1016/j.gaitpost.2016.11.044 10.1613/jair.614 10.1093/ptj/72.1.45 10.1016/j.gaitpost.2006.03.008 10.1007/s10994-007-5019-5 10.1109/TNSRE.2011.2161888 10.1186/1743-0003-9-21 10.1186/s12984-019-0527-7 10.1007/978-3-319-10247-4 10.1191/0269215506cr947oa 10.1111/j.1469-1809.1936.tb02137.x 10.2105/AJPH.2005.083055 10.1109/ICISA.2011.5772404 10.1016/j.jbiomech.2016.10.045 10.1007/BF00227520 10.1017/CBO9780511801389 10.1016/j.pmrj.2016.07.531 10.1109/CCDC.2016.7531457 10.1186/1756-0500-3-62
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SubjectTerms	accelerometer Activities of daily living Algorithms Amputation detection Falls Fitness equipment Gait gyroscope inertial measurement unit Machine learning Older people Prevention Prostheses Sensors stumbling Velocity Walking
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Title	The Stumblemeter: Design and Validation of a System That Detects and Classifies Stumbles during Gait
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