Recognizing human activities from smartphone sensors using hierarchical continuous hidden Markov models
Human activity recognition has been gaining more and more attention from researchers in recent years, particularly with the use of widespread and commercially available devices such as smartphones. However, most of the existing works focus on discriminative classifiers while neglecting the inherent...
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147716683687 |
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| _version_ | 1832553126162333696 |
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| author | Charissa Ann Ronao Sung-Bae Cho |
| author_facet | Charissa Ann Ronao Sung-Bae Cho |
| author_sort | Charissa Ann Ronao |
| collection | DOAJ |
| description | Human activity recognition has been gaining more and more attention from researchers in recent years, particularly with the use of widespread and commercially available devices such as smartphones. However, most of the existing works focus on discriminative classifiers while neglecting the inherent time-series and continuous characteristics of sensor data. To address this, we propose a two-stage continuous hidden Markov model framework, which also takes advantage of the innate hierarchical structure of basic activities. This kind of system architecture not only enables the use of different feature subsets on different subclasses, which effectively reduces feature computation overhead, but also allows for varying number of states and iterations. Experiments show that the hierarchical structure dramatically increases classification performance. We analyze the behavior of the accelerometer and gyroscope signals for each activity through graphs, and with added fine tuning of states and training iterations, the proposed method is able to achieve an overall accuracy of up to 93.18%, which is the best performance among the state-of-the-art classifiers for the problem at hand. |
| format | Article |
| id | doaj-art-64e704feda5649bc8a7249141d722ecf |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-64e704feda5649bc8a7249141d722ecf2025-02-03T05:55:22ZengWileyInternational Journal of Distributed Sensor Networks1550-14772017-01-011310.1177/1550147716683687Recognizing human activities from smartphone sensors using hierarchical continuous hidden Markov modelsCharissa Ann RonaoSung-Bae ChoHuman activity recognition has been gaining more and more attention from researchers in recent years, particularly with the use of widespread and commercially available devices such as smartphones. However, most of the existing works focus on discriminative classifiers while neglecting the inherent time-series and continuous characteristics of sensor data. To address this, we propose a two-stage continuous hidden Markov model framework, which also takes advantage of the innate hierarchical structure of basic activities. This kind of system architecture not only enables the use of different feature subsets on different subclasses, which effectively reduces feature computation overhead, but also allows for varying number of states and iterations. Experiments show that the hierarchical structure dramatically increases classification performance. We analyze the behavior of the accelerometer and gyroscope signals for each activity through graphs, and with added fine tuning of states and training iterations, the proposed method is able to achieve an overall accuracy of up to 93.18%, which is the best performance among the state-of-the-art classifiers for the problem at hand.https://doi.org/10.1177/1550147716683687 |
| spellingShingle | Charissa Ann Ronao Sung-Bae Cho Recognizing human activities from smartphone sensors using hierarchical continuous hidden Markov models International Journal of Distributed Sensor Networks |
| title | Recognizing human activities from smartphone sensors using hierarchical continuous hidden Markov models |
| title_full | Recognizing human activities from smartphone sensors using hierarchical continuous hidden Markov models |
| title_fullStr | Recognizing human activities from smartphone sensors using hierarchical continuous hidden Markov models |
| title_full_unstemmed | Recognizing human activities from smartphone sensors using hierarchical continuous hidden Markov models |
| title_short | Recognizing human activities from smartphone sensors using hierarchical continuous hidden Markov models |
| title_sort | recognizing human activities from smartphone sensors using hierarchical continuous hidden markov models |
| url | https://doi.org/10.1177/1550147716683687 |
| work_keys_str_mv | AT charissaannronao recognizinghumanactivitiesfromsmartphonesensorsusinghierarchicalcontinuoushiddenmarkovmodels AT sungbaecho recognizinghumanactivitiesfromsmartphonesensorsusinghierarchicalcontinuoushiddenmarkovmodels |