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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Vydáno v:	Sensors (Basel, Switzerland) Ročník 21; číslo 19; s. 6636
Hlavní autoři:	Hartog, Dylan den, Harlaar, Jaap, Smit, Gerwin
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Basel MDPI AG 06.10.2021 MDPI
Témata:	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
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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
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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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References	Santhiranayagam (ref_11) 2017; 53 Pavol (ref_18) 2001; 56 Patel (ref_19) 2012; 9 Rosenblatt (ref_9) 2014; 51 ref_14 ref_36 Fisher (ref_37) 1936; 7 ref_35 ref_34 Weerdesteyn (ref_5) 2008; 45 ref_32 Srygley (ref_12) 2009; 90 Berg (ref_3) 1997; 26 ref_15 Schillings (ref_30) 2000; 83 Eng (ref_17) 1994; 102 Shirota (ref_43) 2015; 12 Ackerman (ref_4) 2020; 29 Hunter (ref_6) 2016; 9 King (ref_29) 2019; 16 Schein (ref_38) 2007; 68 Byju (ref_10) 2016; 49 Zhang (ref_27) 2010; 19 ref_25 Opitz (ref_41) 1999; 11 ref_24 ref_23 Begg (ref_8) 2007; 25 ref_22 ref_21 Sehatzadeh (ref_13) 2019; 19 ref_20 ref_42 Weiss (ref_26) 2010; 3 ref_40 Jadhav (ref_39) 2016; 5 Acharya (ref_33) 2014; 103 ref_28 Winter (ref_7) 1992; 72 Shirota (ref_31) 2014; 47 Blake (ref_2) 1988; 17 Mackenzie (ref_16) 2006; 20 Li (ref_1) 2006; 96
References_xml	– volume: 83 start-page: 2093 year: 2000 ident: ref_30 article-title: Muscular Responses and Movement Strategies During Stumbling Over Obstacles publication-title: J. Neurophysiol. doi: 10.1152/jn.2000.83.4.2093 – volume: 26 start-page: 261 year: 1997 ident: ref_3 article-title: Circumstances and consequences of falls in independent community-dwelling older adults publication-title: Age Ageing doi: 10.1093/ageing/26.4.261 – volume: 56 start-page: 428 year: 2001 ident: ref_18 article-title: Mechanisms Leading to a Fall from an Induced Trip in Healthy Older Adults publication-title: J. Gerontol. Ser. A Biol. Sci. Med. Sci. doi: 10.1093/gerona/56.7.M428 – ident: ref_20 doi: 10.1109/IEMBS.2007.4352627 – ident: ref_36 doi: 10.1017/CBO9781107298019 – ident: ref_24 – volume: 17 start-page: 365 year: 1988 ident: ref_2 article-title: Falls by elderly people at home: Prevalence and associated factors publication-title: Age Ageing doi: 10.1093/ageing/17.6.365 – volume: 51 start-page: 1229 year: 2014 ident: ref_9 article-title: Active dorsiflexing prostheses may reduce trip-related fall risk in people with transtibial amputation publication-title: J. Rehabil. Res. Dev. doi: 10.1682/JRRD.2014.01.0031 – volume: 19 start-page: 1 year: 2019 ident: ref_13 article-title: Osseointegrated Prosthetic Implants for People with Lower-Limb Amputation: A Health Technology Assessment publication-title: Ont. Health Technol. Assess. Ser. – ident: ref_32 doi: 10.1007/978-1-4614-6849-3 – volume: 45 start-page: 1195 year: 2008 ident: ref_5 article-title: Falls in individuals with stroke publication-title: J. Rehabil. Res. Dev. doi: 10.1682/JRRD.2007.09.0145 – volume: 12 start-page: 1 year: 2015 ident: ref_43 article-title: Transfemoral amputee recovery strategies following trips to their sound and prosthesis sides throughout swing phase publication-title: J. NeuroEng. Rehabil. doi: 10.1186/s12984-015-0067-8 – volume: 103 start-page: 34 year: 2014 ident: ref_33 article-title: Application of Feature Selection Methods in Educational Data Mining publication-title: Int. J. Comput. Appl. – volume: 90 start-page: 786 year: 2009 ident: ref_12 article-title: Self-Report of Missteps in Older Adults: A Valid Proxy of Fall Risk? publication-title: Arch. Phys. Med. Rehabil. doi: 10.1016/j.apmr.2008.11.007 – ident: ref_14 – volume: 47 start-page: 2679 year: 2014 ident: ref_31 article-title: Trip recovery strategies following perturbations of variable duration publication-title: J. Biomech. doi: 10.1016/j.jbiomech.2014.05.009 – volume: 53 start-page: 73 year: 2017 ident: ref_11 article-title: Non-MTC gait cycles: An adaptive toe trajectory control strategy in older adults publication-title: Gait Posture doi: 10.1016/j.gaitpost.2016.11.044 – volume: 11 start-page: 169 year: 1999 ident: ref_41 article-title: Popular Ensemble Methods: An Empirical Study publication-title: J. Artif. Intell. Res. doi: 10.1613/jair.614 – ident: ref_42 – ident: ref_35 – volume: 72 start-page: 45 year: 1992 ident: ref_7 article-title: Foot Trajectory in Human Gait: A Precise and Multifactorial Motor Control Task publication-title: Phys. Ther. doi: 10.1093/ptj/72.1.45 – volume: 25 start-page: 191 year: 2007 ident: ref_8 article-title: Minimum foot clearance during walking: Strategies for the minimisation of trip-related falls publication-title: Gait Posture doi: 10.1016/j.gaitpost.2006.03.008 – ident: ref_21 – volume: 68 start-page: 235 year: 2007 ident: ref_38 article-title: Active learning for logistic regression: An evaluation publication-title: Mach. Learn. doi: 10.1007/s10994-007-5019-5 – volume: 5 start-page: 1842 year: 2016 ident: ref_39 article-title: Comparative Study of K-NN, Naive Bayes and Decision Tree Classification Techniques publication-title: Int. J. Sci. Res. – volume: 19 start-page: 567 year: 2010 ident: ref_27 article-title: Towards Design of a Stumble Detection System for Artificial Legs publication-title: IEEE Trans. Neural. Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2011.2161888 – ident: ref_25 – volume: 9 start-page: 21 year: 2012 ident: ref_19 article-title: A review of wearable sensors and systems with application in rehabilitation publication-title: J. NeuroEng. Rehabil. doi: 10.1186/1743-0003-9-21 – volume: 16 start-page: 69 year: 2019 ident: ref_29 article-title: A novel system for introducing precisely-controlled, unanticipated gait perturbations for the study of stumble recovery publication-title: J. NeuroEng. Rehabil. doi: 10.1186/s12984-019-0527-7 – ident: ref_34 doi: 10.1007/978-3-319-10247-4 – volume: 20 start-page: 331 year: 2006 ident: ref_16 article-title: Validation of self-reported fall events in intervention studies publication-title: Clin. Rehabil. doi: 10.1191/0269215506cr947oa – volume: 7 start-page: 179 year: 1936 ident: ref_37 article-title: The use of multiple measurements in taxonomic problems publication-title: Ann. Eugen. doi: 10.1111/j.1469-1809.1936.tb02137.x – ident: ref_15 – volume: 96 start-page: 1192 year: 2006 ident: ref_1 article-title: Outdoor Falls Among Middle-Aged and Older Adults: A Neglected Public Health Problem publication-title: Am. J. Public Health doi: 10.2105/AJPH.2005.083055 – ident: ref_23 doi: 10.1109/ICISA.2011.5772404 – volume: 49 start-page: 4016 year: 2016 ident: ref_10 article-title: Alternative measures of toe trajectory more accurately predict the probability of tripping than minimum toe clearance publication-title: J. Biomech. doi: 10.1016/j.jbiomech.2016.10.045 – volume: 29 start-page: 436 year: 2020 ident: ref_4 article-title: Prevalence and correlates of falls in a middle-aged population with osteoarthritis: Data from the Osteoarthritis Initiative publication-title: Health Soc. Care Community – volume: 102 start-page: 339 year: 1994 ident: ref_17 article-title: Strategies for recovery from a trip in early and late swing during human walking publication-title: Exp. Brain Res. doi: 10.1007/BF00227520 – ident: ref_22 – ident: ref_40 doi: 10.1017/CBO9780511801389 – volume: 9 start-page: 170 year: 2016 ident: ref_6 article-title: Risk Factors for Falls in People with a Lower Limb Amputation: A Systematic Review publication-title: PMR doi: 10.1016/j.pmrj.2016.07.531 – ident: ref_28 doi: 10.1109/CCDC.2016.7531457 – volume: 3 start-page: 1 year: 2010 ident: ref_26 article-title: Automated detection of near falls: Algorithm development and preliminary results publication-title: BMC Res. Notes doi: 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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