2017

Automation in Construction

Automated classification and quantification of spatio-temporal parameters in cross-country skiing skating technique by analysis of inertialsensor data and sensor insole data

Maxwell Fordjour Antwi-Afari, Heng Lia, JoonOh Seo, Arnold Yu Lok Wong

Dept. of Building and Real Estate, Hong Kong Polytechnic University

Schlagwörter

Insole pressure sensors Loss of balance, Supervised machine learning

Insole pressure sensors Loss of balance, Supervised machine learning

Zusammenfassung

Fall on the same level is the leading cause of non-fatal injuries in construction workers; however, identifying loss of balance events associated with specific unsafe surface conditions in a timely manner remain challenging. The objective of the current study was to develop a novel method to detect and classify loss of balance events that could lead to falls on the same level by using foot plantar pressure distributions data captured from wearable insole pressure sensors. Ten healthy volunteers participated in experimental trials, simulating four major loss of balance events (e.g., slip, trip, unexpected step-down, and twisted ankle) to collect foot plantar pressure distributions data. Supervised machine learning algorithms were used to learn the unique foot plantar pressure patterns, and then to automatically detect loss of balance events. We compared classification performance by varying window sizes, feature groups and types of classifiers, and the best classification accuracy (97.1%) was achieved when using the Random Forest classifier with all feature groups and a window size of 0.32 s. This study is important to researchers and site managers because it uses foot plantar pressure distribution data to objectively distinguish various potential loss of balance events associated with specific unsafe surface conditions. The proposed approach can allow practitioners to proactively conduct automated fall risk monitoring to minimize the risk of falls on the same level on sites.

Moticon's Schlussfolgerungen

This thesis focuses on the automated classification of techniques in classic cross-country skiing using modern instrumentation to analyze human movement with a high level of detail. The primary objective was to develop a method to divide skiing cycles into distinct phases (gliding, kick, and reposition phases) and calculate the time spent on each phase using data from Moticon sensor insoles and inertial measurement units (IMUs). Additionally, it was aimed to characterize the change in the center of pressure (CoP) during these phases and compare these characteristics between athletes of different skill levels. Data collection involved collecting data from six athletes, three elite and three intermediate, performing different skiing techniques on a treadmill and under snow conditions. Phases were visually identified and later determined by analyzing accelerometer data. Results showed that elite athletes spent a larger percentage of their cycle time in the gliding phase compared to intermediate athletes. The CoP analysis during the gliding phase indicated distinct patterns that varied by skill level. These findings suggest the potential for automated classification of skiing techniques and the possibility of distinguishing skill levels based on the identified features.

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