ArticleSensors (Basel, Switzerland)2023
Unsupervised Gait Event Identification with a Single Wearable Accelerometer and/or Gyroscope: A Comparison of Methods across Running Speeds, Surfaces, and Foot Strike Patterns.
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 7 papers.
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Who cites it
7 citing papers in PubMed, 22 citations in OpenAlex.
- Wearable Sensor Technology and Biomechanical Gait Analysis in Post-Stroke Rehabilitation: A Review with Implications for the Saudi Healthcare System.Neurosciences (Riyadh, Saudi Arabia) · 2026Review
- Development and Validation of an Algorithm for Foot Contact Detection in High-Dynamic Sports Movements Using Inertial Measurement Units.Sensors (Basel, Switzerland) · 2026Article
- Identifying Fatigue-Related Gait Patterns Using Multiple Inertial Measurement Units and Statistical Parametric Mapping: A Continuous Analysis of an Outdoor Full Marathon in Male Recreational Runners.Sports medicine - open · 2025Article
- Identifying right and left impact using the derivative of linear resultant acceleration from a single sacrum-mounted IMU.Wearable technologies · 2025Article
- Knee Angle Estimation with Dynamic Calibration Using Inertial Measurement Units for Running.Sensors (Basel, Switzerland) · 2024Article
- A 0.05 m Change in Inertial Measurement Unit Placement Alters Time and Frequency Domain Metrics during Running.Sensors (Basel, Switzerland) · 2024Article
- Acceleration-Based Estimation of Vertical Ground Reaction Forces during Running: A Comparison of Methods across Running Speeds, Surfaces, and Foot Strike Patterns.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
We evaluated 18 methods capable of identifying initial contact (IC) and terminal contact (TC) gait events during human running using data from a single wearable sensor on the shank or sacrum. We adapted or created code to automatically execute each method, then applied it to identify gait events from 74 runners across different foot strike angles, surfaces, and speeds. To quantify error, estimated gait events were compared to ground truth events from a time-synchronized force plate. Based on our findings, to identify gait events with a wearable on the shank, we recommend the Purcell or Fadillioglu method for IC (biases +17.4 and -24.3 ms; LOAs -96.8 to +131.6 and -137.0 to +88.4 ms) and the Purcell method for TC (bias +3.5 ms; LOAs -143.9 to +150.9 ms). To identify gait events with a wearable on the sacrum, we recommend the Auvinet or Reenalda method for IC (biases -30.4 and +29.0 ms; LOAs -149.2 to +88.5 and -83.3 to +141.3 ms) and the Auvinet method for TC (bias -2.8 ms; LOAs -152.7 to +147.2 ms). Finally, to identify the foot in contact with the ground when using a wearable on the sacrum, we recommend the Lee method (81.9% accuracy).
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