Combining Supervised and Unsupervised Learning Algorithms for Human Activity Recognition
Human activity recognition is an extensively researched topic in the last decade. Recent methods employ supervised and unsupervised deep learning techniques in which spatial and temporal dependency is modeled. This paper proposes a novel approach for human activity recognition using skeleton data. T...
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-09-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/18/6309 |
| _version_ | 1797517199960702976 |
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| author | Elena-Alexandra Budisteanu Irina Georgiana Mocanu |
| author_facet | Elena-Alexandra Budisteanu Irina Georgiana Mocanu |
| author_sort | Elena-Alexandra Budisteanu |
| collection | DOAJ |
| description | Human activity recognition is an extensively researched topic in the last decade. Recent methods employ supervised and unsupervised deep learning techniques in which spatial and temporal dependency is modeled. This paper proposes a novel approach for human activity recognition using skeleton data. The method combines supervised and unsupervised learning algorithms in order to provide qualitative results and performance in real time. The proposed method involves a two-stage framework: the first stage applies an unsupervised clustering technique to group up activities based on their similarity, while the second stage classifies data assigned to each group using graph convolutional networks. Different clustering techniques and data augmentation strategies are explored for improving the training process. The results were compared against the state of the art methods and the proposed model achieved 90.22% Top-1 accuracy performance for NTU-RGB+D dataset (the performance was increased by approximately 9% compared with the baseline graph convolutional method). Moreover, inference time and total number of parameters stay within the same magnitude order. Extending the initial set of activities with additional classes is fast and robust, since there is no required retraining of the entire architecture but only to retrain the cluster to which the activity is assigned. |
| first_indexed | 2024-03-10T07:13:26Z |
| format | Article |
| id | doaj.art-5ce144b634004547a75382dc95f8b024 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T07:13:26Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5ce144b634004547a75382dc95f8b0242023-11-22T15:14:51ZengMDPI AGSensors1424-82202021-09-012118630910.3390/s21186309Combining Supervised and Unsupervised Learning Algorithms for Human Activity RecognitionElena-Alexandra Budisteanu0Irina Georgiana Mocanu1Computer Science Department, University Politehnica of Bucharest, RO-060042 Bucharest, RomaniaComputer Science Department, University Politehnica of Bucharest, RO-060042 Bucharest, RomaniaHuman activity recognition is an extensively researched topic in the last decade. Recent methods employ supervised and unsupervised deep learning techniques in which spatial and temporal dependency is modeled. This paper proposes a novel approach for human activity recognition using skeleton data. The method combines supervised and unsupervised learning algorithms in order to provide qualitative results and performance in real time. The proposed method involves a two-stage framework: the first stage applies an unsupervised clustering technique to group up activities based on their similarity, while the second stage classifies data assigned to each group using graph convolutional networks. Different clustering techniques and data augmentation strategies are explored for improving the training process. The results were compared against the state of the art methods and the proposed model achieved 90.22% Top-1 accuracy performance for NTU-RGB+D dataset (the performance was increased by approximately 9% compared with the baseline graph convolutional method). Moreover, inference time and total number of parameters stay within the same magnitude order. Extending the initial set of activities with additional classes is fast and robust, since there is no required retraining of the entire architecture but only to retrain the cluster to which the activity is assigned.https://www.mdpi.com/1424-8220/21/18/6309human activity recognitionskeletonspatial-temporal graph convolutional networkclusteringk-meansGaussian mixture model |
| spellingShingle | Elena-Alexandra Budisteanu Irina Georgiana Mocanu Combining Supervised and Unsupervised Learning Algorithms for Human Activity Recognition Sensors human activity recognition skeleton spatial-temporal graph convolutional network clustering k-means Gaussian mixture model |
| title | Combining Supervised and Unsupervised Learning Algorithms for Human Activity Recognition |
| title_full | Combining Supervised and Unsupervised Learning Algorithms for Human Activity Recognition |
| title_fullStr | Combining Supervised and Unsupervised Learning Algorithms for Human Activity Recognition |
| title_full_unstemmed | Combining Supervised and Unsupervised Learning Algorithms for Human Activity Recognition |
| title_short | Combining Supervised and Unsupervised Learning Algorithms for Human Activity Recognition |
| title_sort | combining supervised and unsupervised learning algorithms for human activity recognition |
| topic | human activity recognition skeleton spatial-temporal graph convolutional network clustering k-means Gaussian mixture model |
| url | https://www.mdpi.com/1424-8220/21/18/6309 |
| work_keys_str_mv | AT elenaalexandrabudisteanu combiningsupervisedandunsupervisedlearningalgorithmsforhumanactivityrecognition AT irinageorgianamocanu combiningsupervisedandunsupervisedlearningalgorithmsforhumanactivityrecognition |