Abstract
Smartphone-based approaches for Human Activity Recognition have become prevalent in recent years. Despite the amount of research undertaken in the field, issues such as cross-subject variability are still posing an obstacle to the deployment of solutions in large scale, free-living settings. Personalized methods (i.e. aiming to adapt a generic classifier to a specific target user) attempt to solve this problem. The lack of labeled data for training purposes, however, represents a major barrier. This is especially the case when taking into consideration that personalization generally requires labeled data to be user-specific. This paper presents a novel personalization method combining a semi-population based approach with user adaptation. Personalization is achieved through the following. Firstly, the proposed method identifies a subset of users from the available population as best candidates for initializing the classifier to the target user. Subsequently, a semi-population Neural Network classifier is trained using data from this subset of users. The classifier's network weights are then updated using a small amount of labeled data from the target user subsequently implementing personalization. This approach was validated on a large publicly available dataset collected in a free-living scenario. The personalized approach using the proposed method has shown to improve the overall F-score to 74.4% compared to 70.9% when using a generic non-personalized approach. Results obtained, with statistical significance being confirmed on a set of 57 users, indicate that model initialization using the semi-population approach can reduce the amount of labeled data required for personalization. As such, the proposed method for model initialization could facilitate the real-world deployment of systems implementing personalization by reducing the amount of data needed for personalization.
Highlights
Human Activity Recognition (HAR) finds several potential applications in pervasive systems, varying from Ambient Assisted Living (AAL) to generic home automation scenarios
Accuracy of smartphone-based HAR solutions, has been reported as ranging between 85% and 95% for detection of simple activities, whereas lower accuracies have been reported for more complex activities (e.g. Activities of Daily Living (ADL)) [2], [10]
This work presents the following contributions: (i) a semi-population based method is proposed to identify a subset of users as good candidates to initialize the parameters of a personalized model, (ii) an online training mechanism is proposed to further adapt the model to the target user, and (iii) the results are evaluated using Vaizman’s publicly available dataset [6] which provides data collected in free-living and unconstrained conditions using a smartphone
Summary
Human Activity Recognition (HAR) finds several potential applications in pervasive systems, varying from Ambient Assisted Living (AAL) to generic home automation scenarios. Semi-population based approaches have been proposed [12], aiming at personalizing models by identifying a subset of users having characteristics that are similar to the target user Their deployment in a free-living context, inherently requires methods to implement online training. This work presents the following contributions: (i) a semi-population based method is proposed to identify a subset of users (from the available training population) as good candidates to initialize the parameters of a personalized model, (ii) an online training mechanism is proposed to further adapt the model to the target user, and (iii) the results are evaluated using Vaizman’s publicly available dataset [6] which provides data collected in free-living and unconstrained conditions using a smartphone.
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