Efficient Class-Incremental Learning Based on Bag-of-Sequencelets Model for Activity Recognition

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.

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Human daily activity recognition with wearable sensors based on incremental learning

2016 10th International Conference on Sensing Technology (ICST) ◽

10.1109/icsenst.2016.7796224 ◽

2016 ◽

Cited By ~ 3

Author(s):

Lingfei Mo ◽

Zengtao Feng ◽

Jingyi Qian

Keyword(s):

Activity Recognition ◽

Incremental Learning ◽

Daily Activity ◽

Wearable Sensors

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Human Activity Recognition-Oriented Incremental Learning with Knowledge Distillation

Journal of Circuits System and Computers ◽

10.1142/s0218126621500961 ◽

2020 ◽

pp. 2150096

Author(s):

Caijuan Chen ◽

Kaoru Ota ◽

Mianxiong Dong ◽

Chen Yu ◽

Hai Jin

Keyword(s):

Activity Recognition ◽

Incremental Learning ◽

Sample Selection ◽

Sensor Data ◽

Storage Space ◽

Knowledge Distillation ◽

Space Limitation ◽

Recognition Systems ◽

Public Datasets ◽

Specific Number

Recently, a variety of different machine learning methods improve the applicability of activity recognition systems in different scenarios. For many current activity recognition models, it is assumed that all data are prepared well in advance and the device has no storage space limitation. However, the process of the sensor data collection is dynamically changing over time, the activity category may be continuously increasing, and the device has limited storage space. Therefore, in this study, we propose a novel class incremental learning comprehensive solution towards activity recognition with knowledge distillation. Besides, we develop the representative sample selection method to select and update a specific number of preserved old samples. When new activity classes samples arrive, we only need the new classes samples and the representative old samples to preserve the network’s performance for old classes while identifying the new classes. Finally, we carry out experiments using two different public datasets, and they show good accuracy for old and new categories. Besides, the method can significantly reduce the space required to store old classes samples.

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An Intelligent Human Activity Recognition Method with Incremental Learning Capability for Bedridden Patients

2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) ◽

10.1109/icarcv.2018.8581232 ◽

2018 ◽

Author(s):

Shengwei Luo ◽

Chunhui Zhao ◽

Yongji Fu

Keyword(s):

Activity Recognition ◽

Human Activity ◽

Incremental Learning ◽

Human Activity Recognition ◽

Recognition Method ◽

Learning Capability

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