Abstract
Human Activity Recognition (HAR) is an important part of ambient intelligence systems since it can provide user-context information, thus allowing a greater personalization of services. One of the problems with HAR systems is that the labeling process for the training data is costly, which has hindered its practical application. A common approach is to train a general model with the aggregated data from all users. The problem is that for a new target user, this model can perform poorly because it is biased towards the majority type of users and does not take into account the particular characteristics of the target user. To overcome this limitation, a user-dependent model can be trained with data only from the target user that will be optimal for this particular user; however, this requires a considerable amount of labeled data, which is cumbersome to obtain. In this work, we propose a method to build a personalized model for a given target user that does not require large amounts of labeled data. Our method uses data already labeled by a community of users to complement the scarce labeled data of the target user. Our results showed that the personalized model outperformed the general and the user-dependent models when labeled data is scarce.
Highlights
In recent years Human Activity Recognition (HAR) [1,2] has gained a lot of attention because of its wide range of applications in several areas, such as health and elder care, sports, etc. [3,4,5]
Our models’ evaluation procedure consists of sampling a small percent p of instances from the target user ut to be used as the train set τt and uses the remaining data to test the performance of the General Model, User-Dependent
We proposed a method based on class similarities between a collection of previous users and a specific user to build Personalized Models when labeled data for this one is scarce, obtaining the benefits of a “crowdsourcing” approach, where the community data is fit to the individual case
Summary
In recent years Human Activity Recognition (HAR) [1,2] has gained a lot of attention because of its wide range of applications in several areas, such as health and elder care, sports, etc. [3,4,5]. One of the problems in HAR systems is that the labeling process for the training data tends to be tedious, time consuming, difficult, and prone to errors This problem has really hindered the practical application of HAR systems, limiting them to the most basic activities for which a general model is enough, as is the case for the pedometer function or alerting the user who spends too much time sitting down; both functions are available in some fitness devices and smartwatches. In the field of recommender systems (e.g., movie, music, book recommenders), this is known as the cold-start problem [9] and it includes the situation when there is a new user but nothing or little is known about him/her, in which case it becomes difficult to recommend an item, service, etc.
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