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MLFatigueDetection Machine Learning Based Walking Fatigue Detection
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Resumo(s)
Leg fatigue can influence the gait patterns, therefore declining the postural stability
and the motor performance, increasing the risk of falls. In order to improve the earlier
detection of risks and the application of fall prevention strategies, automated solutions
based on gait analysis must be developed. A sector of the population at risk is the workforce
where a majority of workers admits to be fatigued and where falls can lead to serious
workplace injuries or even deaths. In these cases, having the ability to detect if the user
is fatigued in real time by simply using the motion sensors on the smartphone and processing
it with machine learning can lead to the prevention of falls and the consequences
these bring.
Phones andwearable devices were studied for their ability to be used to extract inertial
sensor’s data to provide enough information for the fatigue detection. Supervised machine
learning algorithms, such as Support Vector Machines (SVM) and Neural Networks,
will be used to process this information for fatigue level classification. Their performance
will then be compared to find the best algorithm for fatigue detection. In addition to this
comparative work, different conditions for the data collection and processing were tested
in an effort to discover the optimal conditions for the implementation of the algorithms.
Descrição
Palavras-chave
Gait Patterns Fall risk Fall prevention Fatigue Inertial sensors Supervised learning