Classification of Daily Activities by Different Machine Learning Models Based on Characteristics in the Time Domain

Portable physical activity monitors provide detailed, continuous and objective measurements of individual physical activity in the environment of daily activities. A major problem with wristbands, pedometers, and smartphones that use accelerometer technology is that they measure involuntary jerks as steps. Therefore, they generate inaccurate values ​​resulting in erroneous data. Therefore, the purpose of this study is to determine and contrast the performance obtained for the classification of daily activities of different machine learning models based on characteristics in the time domain of signals obtained from accelerometry collected at various points of the body. The development of the activity identifier is based on models and characteristics of existing developments of calorie counters by accelerometric signals; these features are extracted in the time domain. The following classifiers were applied: Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Gaussian Naive Bayes, Decision Tree, Random Forest, Light Gradient Boosting and Extreme Gradient Boosting. The performance of each model was measured by how accurately it emerged to classify 4 daily activities based on the test set. The results show that, to have an accuracy greater than 70% in most models, at least 2 accelerometers are required.