What are the experimental, methodological and technical errors in automatic gait event detection algorithms?
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Experimental errors in automatic gait event detection algorithms include variations in accuracy based on gait speed. Methodological errors involve the complexity of algorithms hindering implementation on simple hardware systems. Technical errors encompass challenges in achieving real-time detection for clinical populations in free-living environments. These errors impact the validity and reliability of gait parameters computed, such as stance phase and stride length. Despite these challenges, studies have shown promising results with algorithms like the echo state network (ESN) demonstrating robustness and accuracy comparable to state-of-the-art methods. The proposed marker-based detection method has also shown high sensitivity and precision across different walking conditions and gait impairments. Overall, advancements in algorithm development aim to address these errors and improve the effectiveness of automatic gait event detection systems.
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| Papers (5) | Insight |
|---|---|
| The study validates running gait event detection algorithms in a semi-uncontrolled environment, highlighting errors in sacral-mounted IMU overestimating foot contact duration at speeds >3.57 m s−1. | |
| Experimental errors in automatic gait event detection algorithms include high sensitivity and low temporal error, with challenges in real-time applications for diverse user populations and walking conditions. | |
| Experimental errors in gait event detection algorithms can arise from data variability, requiring adjustments like rule-based state machines. The study focuses on using reservoir computing for robust real-time detection. | |
| Experimental errors in gait event detection algorithms include variations in accuracy with gait speed, with one algorithm showing late heel strike detection. Methodological errors include impact on stance phase assessment. | |
6 Citations | The proposed marker-based gait event detection method showed minimal errors in sensitivity, positive predictive values, and stride parameter calculations across various populations and walking conditions. |
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