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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Beschreibung
Zusammenfassung: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.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s23052736