Abstract. In this study, the aim is to personalize inertial sensor databased human activity recognition models using incremental learning. At first, the recognition is based on user-independent model. However, when personal streaming data becomes available, the incremental learning-based recognition model can be updated, and therefore personalized, based on the data without user-interruption. The used incremental learning algorithm is Learn++ which is an ensemble method that can use any classifier as a base classifier. In fact, study compares three different base classifiers: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and classification and regression tree (CART). Experiments are based on publicly open data set and they show that already a small personal training data set can improve the classification accuracy. Improvement using LDA as base classifier is 4.6 percentage units, using QDA 2.0 percentage units, and 2.3 percentage units using CART. However, if the user-independent model used in the first phase of the recognition process is not accurate enough, personalization cannot improve recognition accuracy. This study focuses on human activity recognition based on inertial sensor data collected using smartphone sensors. One of the main challenge of the field is that people are different: they are unique for instance in terms of physical characteristics, health state or gender. All of these have an effect to the data that are collected for model training. In fact, it is shown that user-independent models do not work accurately for instance if they are trained with healthy study subjects and tested with subjects who have difficulties to move [1] . Thus, if the aim is to train a model that works with everybody, the focus of research should be on personal and personalized prediction models instead of user-independent models. However, the challenge of personal and personalized models is that they require personal training data. This normally would require an extensive, separate data collection session for each user making the approach unusable out-of-the-box. There are some attempts to personalize the recognition process without userinterruption. Siirtola et. al [2] presented a two step approach especially designed for devices that include several different types of sensors. In the first step, userindependent model that uses data from all sensors was used to label personal streaming data. This information is then used to build a light, and energy efficient personal model that uses only a sub-set of available sensors. Using the approach, recognition accuracy can be improved but the problem of the
We use the whole streaming data to personalize classification models, as done in REF .
Personalizing human activity recognition models using incremental learning
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Computer Science Mathematics