ArticleSensors (Basel, Switzerland)2023
Evaluation of Different Pressure-Based Foot Contact Event Detection Algorithms across Different Slopes and Speeds.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Validation of Insole Pressure Sensor Algorithms: Implications for In-Field Detection of Initial Contact and Hamstring Muscle Pre-Activity During Side-Cutting.Sensors (Basel, Switzerland) · 2026Article
- Outdoor Walking Classification Based on Inertial Measurement Unit and Foot Pressure Sensor Data.Sensors (Basel, Switzerland) · 2025Article
- Characteristic Changes of the Stance-Phase Plantar Pressure Curve When Walking Uphill and Downhill: Cross-Sectional Study.Journal of medical Internet research · 2024Article
- Effects of Midsole Hardness on the Mechanical Response Characteristics of the Plantar Fascia during Running.Bioengineering (Basel, Switzerland) · 2023Article
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Authors and funding
6 authors.
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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.
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