Human Activity Recognition via an Accelerometer-Enabled-Smartphone Using Kernel Discriminant Analysis

2010 ◽  
Author(s):  
A. M. Khan ◽  
Y.-K. Lee ◽  
S. Y. Lee ◽  
T.-S. Kim
Keyword(s):  
Discriminant Analysis ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Kernel Discriminant Analysis

scholarly journals Context-aware incremental learning-based method for personalized human activity recognition

2021 ◽  
Author(s):  
Pekka Siirtola ◽  
Juha Röning
Keyword(s):  
Discriminant Analysis ◽  
Activity Recognition ◽  
Human Activity ◽  
Error Rate ◽  
Incremental Learning ◽  
Body Position ◽  
Human Activity Recognition ◽  
Streaming Data ◽  
Linear Discriminant ◽  
Independent Model

AbstractThis study introduces an ensemble-based personalized human activity recognition method relying on incremental learning, which is a method for continuous learning, that can not only learn from streaming data but also adapt to different contexts and changes in context. This adaptation is based on a novel weighting approach which gives bigger weight to those base models of the ensemble which are the most suitable to the current context. In this article, contexts are different body positions for inertial sensors. The experiments are performed in two scenarios: (S1) adapting model to a known context, and (S2) adapting model to a previously unknown context. In both scenarios, the models had to also adapt to the data of previously unknown person, as the initial user-independent dataset did not include any data from the studied user. In the experiments, the proposed ensemble-based approach is compared to non-weighted personalization method relying on ensemble-based classifier and to static user-independent model. Both ensemble models are experimented using three different base classifiers (linear discriminant analysis, quadratic discriminant analysis, and classification and regression tree). The results show that the proposed ensemble method performs much better than non-weighted ensemble model for personalization in both scenarios no matter which base classifier is used. Moreover, the proposed method outperforms user-independent models. In scenario 1, the error rate of balanced accuracy using user-independent model was 13.3%, using non-weighted personalization method 13.8%, and using the proposed method 6.4%. The difference is even bigger in scenario 2, where the error rate using user-independent model is 36.6%, using non-weighted personalization method 36.9%, and using the proposed method 14.1%. In addition, F1 scores also show that the proposed method performs much better in both scenarios that the rival methods. Moreover, as a side result, it was noted that the presented method can also be used to recognize body position of the sensor.

scholarly journals Incremental Learning to Personalize Human Activity Recognition Models: The Importance of Human AI Collaboration

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5151 ◽  
Author(s):  
Pekka Siirtola ◽  
Juha Röning
Keyword(s):  
Discriminant Analysis ◽  
Activity Recognition ◽  
Human Activity ◽  
Incremental Learning ◽  
Human Activity Recognition ◽  
Error Rates ◽  
Streaming Data ◽  
Human Intelligence ◽  
Linear Discriminant ◽  
Independent Model

This study presents incremental learning based methods to personalize human activity recognition models. Initially, a user-independent model is used in the recognition process. When a new user starts to use the human activity recognition application, personal streaming data can be gathered. Of course, this data does not have labels. However, there are three different ways to obtain this data: non-supervised, semi-supervised, and supervised. The non-supervised approach relies purely on predicted labels, the supervised approach uses only human intelligence to label the data, and the proposed method for semi-supervised learning is a combination of these two: It uses artificial intelligence (AI) in most cases to label the data but in uncertain cases it relies on human intelligence. After labels are obtained, the personalization process continues by using the streaming data and these labels to update the incremental learning based model, which in this case is Learn++. Learn++ is an ensemble method that can use any classifier as a base classifier, and this study compares three base classifiers: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and classification and regression tree (CART). Moreover, three datasets are used in the experiment to show how well the presented method generalizes on different datasets. The results show that personalized models are much more accurate than user-independent models. On average, the recognition rates are: 87.0% using the user-independent model, 89.1% using the non-supervised personalization approach, 94.0% using the semi-supervised personalization approach, and 96.5% using the supervised personalization approach. This means that by relying on predicted labels with high confidence, and asking the user to label only uncertain observations (6.6% of the observations when using LDA, 7.7% when using QDA, and 18.3% using CART), almost as low error rates can be achieved as by using the supervised approach, in which labeling is fully based on human intelligence.

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