Evaluation of Different Pressure-Based Foot Contact Event Detection Algorithms across Different Slopes and Speeds

If validated, in-shoe pressure measuring technology allows for the field-based quantification of running gait, including kinematic and kinetic measures. Different algorithmic methods have been proposed to determine foot contact events from in-shoe pressure insole systems, however, these methods have...

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Veröffentlicht in:	Sensors (Basel, Switzerland) Jg. 23; H. 5; S. 2736
Hauptverfasser:	Blades, Samuel, Marriott, Hunter, Hundza, Sandra, Honert, Eric C., Stellingwerff, Trent, Klimstra, Marc
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI AG 02.03.2023 MDPI
Schlagworte:	Accuracy Adult Algorithms Biomechanical Phenomena Comparative analysis Data processing event detection Exercise equipment Female Fitness equipment Foot - physiology Gait gait analysis Humans Investigations Kinematics Laboratories Male Pressure Reproducibility of Results Running Sensors Signal processing Wearable computers wearable technology Young Adult United States > US running algorithms event detection pressure wearable technology smart insoles gait analysis
ISSN:	1424-8220, 1424-8220
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Abstract	If validated, in-shoe pressure measuring technology allows for the field-based quantification of running gait, including kinematic and kinetic measures. Different algorithmic methods have been proposed to determine foot contact events from in-shoe pressure insole systems, however, these methods have not been evaluated for accuracy, reliability against a gold standard using running data across different slopes, and speeds. Using data from a plantar pressure measurement system, seven different foot contact event detection algorithms based on pressure signals (pressure sum) were compared to vertical ground reaction force data collected from a force instrumented treadmill. Subjects ran on level ground at 2.6, 3.0, 3.4, and 3.8 m/s, six degrees (10.5%) inclined at 2.6, 2.8, and 3.0 m/s, and six degrees declined at 2.6, 2.8, 3.0, and 3.4 m/s. The best performing foot contact event detection algorithm showed maximal mean absolute errors of only 1.0 ms and 5.2 ms for foot contact and foot off, respectively, on level grade, when compared to a 40 N ascending and descending force threshold from the force treadmill data. Additionally, this algorithm was unaffected by grade and had similar levels of errors across all grades.
AbstractList	If validated, in-shoe pressure measuring technology allows for the field-based quantification of running gait, including kinematic and kinetic measures. Different algorithmic methods have been proposed to determine foot contact events from in-shoe pressure insole systems, however, these methods have not been evaluated for accuracy, reliability against a gold standard using running data across different slopes, and speeds. Using data from a plantar pressure measurement system, seven different foot contact event detection algorithms based on pressure signals (pressure sum) were compared to vertical ground reaction force data collected from a force instrumented treadmill. Subjects ran on level ground at 2.6, 3.0, 3.4, and 3.8 m/s, six degrees (10.5%) inclined at 2.6, 2.8, and 3.0 m/s, and six degrees declined at 2.6, 2.8, 3.0, and 3.4 m/s. The best performing foot contact event detection algorithm showed maximal mean absolute errors of only 1.0 ms and 5.2 ms for foot contact and foot off, respectively, on level grade, when compared to a 40 N ascending and descending force threshold from the force treadmill data. Additionally, this algorithm was unaffected by grade and had similar levels of errors across all grades. If validated, in-shoe pressure measuring technology allows for the field-based quantification of running gait, including kinematic and kinetic measures. Different algorithmic methods have been proposed to determine foot contact events from in-shoe pressure insole systems, however, these methods have not been evaluated for accuracy, reliability against a gold standard using running data across different slopes, and speeds. Using data from a plantar pressure measurement system, seven different foot contact event detection algorithms based on pressure signals (pressure sum) were compared to vertical ground reaction force data collected from a force instrumented treadmill. Subjects ran on level ground at 2.6, 3.0, 3.4, and 3.8 m/s, six degrees (10.5%) inclined at 2.6, 2.8, and 3.0 m/s, and six degrees declined at 2.6, 2.8, 3.0, and 3.4 m/s. The best performing foot contact event detection algorithm showed maximal mean absolute errors of only 1.0 ms and 5.2 ms for foot contact and foot off, respectively, on level grade, when compared to a 40 N ascending and descending force threshold from the force treadmill data. Additionally, this algorithm was unaffected by grade and had similar levels of errors across all grades.If validated, in-shoe pressure measuring technology allows for the field-based quantification of running gait, including kinematic and kinetic measures. Different algorithmic methods have been proposed to determine foot contact events from in-shoe pressure insole systems, however, these methods have not been evaluated for accuracy, reliability against a gold standard using running data across different slopes, and speeds. Using data from a plantar pressure measurement system, seven different foot contact event detection algorithms based on pressure signals (pressure sum) were compared to vertical ground reaction force data collected from a force instrumented treadmill. Subjects ran on level ground at 2.6, 3.0, 3.4, and 3.8 m/s, six degrees (10.5%) inclined at 2.6, 2.8, and 3.0 m/s, and six degrees declined at 2.6, 2.8, 3.0, and 3.4 m/s. The best performing foot contact event detection algorithm showed maximal mean absolute errors of only 1.0 ms and 5.2 ms for foot contact and foot off, respectively, on level grade, when compared to a 40 N ascending and descending force threshold from the force treadmill data. Additionally, this algorithm was unaffected by grade and had similar levels of errors across all grades.
Audience	Academic
Author	Stellingwerff, Trent Marriott, Hunter Hundza, Sandra Klimstra, Marc Blades, Samuel Honert, Eric C.
AuthorAffiliation	1 School of Exercise Science, Physical & Health Education, University of Victoria, Victoria, BC V8P 5C2, Canada 2 Academy of Sport and Physical Activity, College of Health, Wellbeing and Life Sciences, Sheffield Hallam University, Sheffield S1 1WB, UK 4 Canadian Sport Institute Pacific, Victoria, BC V9E 2C5, Canada 3 Human Performance Laboratory, Department of Kinesiology, University of Calgary, Calgary, AB T2N 1N4, Canada
AuthorAffiliation_xml	– name: 3 Human Performance Laboratory, Department of Kinesiology, University of Calgary, Calgary, AB T2N 1N4, Canada – name: 2 Academy of Sport and Physical Activity, College of Health, Wellbeing and Life Sciences, Sheffield Hallam University, Sheffield S1 1WB, UK – name: 1 School of Exercise Science, Physical & Health Education, University of Victoria, Victoria, BC V8P 5C2, Canada – name: 4 Canadian Sport Institute Pacific, Victoria, BC V9E 2C5, Canada
Author_xml	– sequence: 1 givenname: Samuel orcidid: 0000-0001-7998-0459 surname: Blades fullname: Blades, Samuel – sequence: 2 givenname: Hunter surname: Marriott fullname: Marriott, Hunter – sequence: 3 givenname: Sandra surname: Hundza fullname: Hundza, Sandra – sequence: 4 givenname: Eric C. orcidid: 0000-0002-3865-5104 surname: Honert fullname: Honert, Eric C. – sequence: 5 givenname: Trent orcidid: 0000-0002-4704-8250 surname: Stellingwerff fullname: Stellingwerff, Trent – sequence: 6 givenname: Marc orcidid: 0000-0002-5512-0558 surname: Klimstra fullname: Klimstra, Marc
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Cites_doi	10.1016/j.jbiomech.2015.07.036 10.1016/j.comcom.2011.03.019 10.1016/j.gaitpost.2013.08.026 10.1016/j.gaitpost.2009.07.128 10.1016/j.jbiomech.2004.10.028 10.1016/j.gaitpost.2010.06.009 10.1111/sms.12397 10.3389/fspor.2020.571385 10.1016/j.jsams.2010.03.006 10.1016/0021-9290(94)00074-E 10.1080/02640414.2018.1545515 10.1016/S0021-9290(03)00229-X 10.1080/00140130210136963 10.1016/j.gaitpost.2006.09.077 10.1007/s40279-016-0474-4 10.1016/j.jbiomech.2004.04.023 10.1123/jab.2014-0178 10.1016/j.humov.2008.03.007 10.1088/0967-3334/35/1/45 10.1152/japplphysiol.00647.2020 10.3390/s22093338 10.1016/j.jsams.2012.01.004 10.1016/S0268-0033(01)00026-2 10.1123/jab.19.2.178 10.1016/j.gaitpost.2010.03.014 10.1007/s40279-022-01760-6 10.1016/j.gaitpost.2005.05.008 10.1016/j.jbiomech.2016.10.046 10.1016/j.proeng.2010.04.110 10.1016/j.jbiomech.2018.12.025
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SubjectTerms	Accuracy Adult Algorithms Biomechanical Phenomena Comparative analysis Data processing event detection Exercise equipment Female Fitness equipment Foot - physiology Gait gait analysis Humans Investigations Kinematics Laboratories Male Pressure Reproducibility of Results Running Sensors Signal processing Wearable computers wearable technology Young Adult
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Title	Evaluation of Different Pressure-Based Foot Contact Event Detection Algorithms across Different Slopes and Speeds
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