Giyilebilir sensörler ile kas yükü tahmini

Assessment of workload intensity and muscle load measurement is important to understand the quality of the intended motion. There have been numerous attempts to quantify workload levels for different motion for different parts of the body using wearable sensors. This thesis investigates human locomotion, particularly walking and running, which are fundamental activities of daily life. Electromyography (EMG) is a technique to quantify the level of muscle activity. In this study, the aim is to lay foundation for feature engineering and to build a machine learning model to predict EMG sensor output with minimal number of sensors during walking. Two pressure insole sensors and a single EMG - IMU combined sensor have been used during the experiments. The process starts with building a machine learning model to predict EMG sensor output using only raw input signals, which provided a poor accuracy. In order to improve the model, lagged features, derivatives and window-based statistics like moving averages and standard deviations are added to feature set and accuracy is improved. Then the focus is put on hyperparameter tuning and regularization is introduced to decrease overfitting. Although these steps led to incremental improvements, the test performance eventually plateaued with overfitting. In order to overcome this issue, data is shuffled before splitting it into training and test sets. After shuffling during training and test data split, the model delivered its best results—both training and test accuracy improved, and overfitting was significantly reduced.