In this study, importance of user inputs is studied in the context of personalizing human activity recognition models using incremental learning. Inertial sensor data from three body positions are used, and the classification is based on Learn++ ensemble method. Three different approaches to update models are compared: non-supervised, semi-supervised and supervised. Non-supervised approach relies fully on predicted labels, supervised fully on user labeled data, and the proposed method for semi-supervised learning, is a combination of these two. In fact, our experiments show that by relying on predicted labels with high confidence, and asking the user to label only uncertain observations (from 12 to 26 of the observations depending on the used base classifier), almost as low error rates can be achieved as by using supervised approach. In fact, the difference was less than 2 -units. Moreover, unlike non-supervised approach, semi-supervised approach does not suffer from drastic concept drift, and thus, the error rate of the non-supervised approach is over 5 -units higher than using semi-supervised approach.
This study focuses on human activity recognition based on inertial sensor data collected using smartphone sensors. One of the main challenges of the field is that people are different: they are unique for instance in terms of physical characteristics, health state or gender. Due to this, it is shown that a model that provides accurate results for one person, does not necessarily work accurately with somebody else's data. For instance, user-independent models are not accurate if they are trained with healthy study subjects and tested with subjects who have difficulties to move @cite_8 . Personal recognition models provide better recognition rates, but the challenge is that they normally require personal training data, and therefore, a personal data gathering session @cite_1 .
@cite_1 @cite_8
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The last 20 years have seen ever-increasing research activity in the field of human activity recognition. With activity recognition having considerably matured, so has the number of challenges in designing, implementing, and evaluating activity recognition systems. This tutorial aims to provide a comprehensive hands-on introduction for newcomers to the field of human activity recognition. It specifically focuses on activity recognition using on-body inertial sensors. We first discuss the key research challenges that human activity recognition shares with general pattern recognition and identify those challenges that are specific to human activity recognition. We then describe the concept of an Activity Recognition Chain (ARC) as a general-purpose framework for designing and evaluating activity recognition systems. We detail each component of the framework, provide references to related research, and introduce the best practice methods developed by the activity recognition research community. We conclude with the educational example problem of recognizing different hand gestures from inertial sensors attached to the upper and lower arm. We illustrate how each component of this framework can be implemented for this specific activity recognition problem and demonstrate how different implementations compare and how they impact overall recognition performance. Mobile phones with built-in accelerometers promise a convenient, objective way to quantify everyday movements and classify those movements into activities. Using accelerometer data we estimate the activities of 18 healthy subjects and 8 patients with Parkinson’s disease – e.g. walking, standing, sitting, or simply holding the phone. We use standard machine learning classifiers (support vector machines, regularized logistic regression) to automatically select, weigh, and combine a large set of standard features for time series analysis. Using crossvalidation across all samples we are able to correctly identify 96.1 of the activities of healthy subjects and 92.2 of the activities of Parkinson’s patients. However, when applying the classification parameters derived from the set of healthy subjects to Parkinson’s patients, the percent correct lowers to 60.3 , due to different characteristics of movement. For a fairer comparison across populations we also applied subject-wise crossvalidation, identifying healthy subject activities with 86.0 accuracy and 75.1 accuracy for patients. We discuss the key differences between these populations, and why algorithms designed for and trained with healthy subject data are not reliable for activity recognition in populations with motor disabilities.