Adaptive Learning Approach for Human Activity Recognition Using Data from Smartphone Sensors
Every day humans interact with smartphones that have embedded sensors that enable the tracking of changing physical activities of the device owner. However, several problems arise with the recognition of multiple activities (such as walking, sitting, running, and other) on smartphones. Firstly, most...
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MDPI AG
2025-07-01
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| author | Leonidas Sakalauskas Ingrida Vaiciulyte |
| author_facet | Leonidas Sakalauskas Ingrida Vaiciulyte |
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| description | Every day humans interact with smartphones that have embedded sensors that enable the tracking of changing physical activities of the device owner. However, several problems arise with the recognition of multiple activities (such as walking, sitting, running, and other) on smartphones. Firstly, most of the devices do not recognize some activities well, such as walking upstairs or downstairs. Secondly, recognition algorithms are embedded into smartphone software and are static, unless updated. In this case, a recognition algorithm must be re-trained with training data of a specific size. Thus, an adaptive (also known as, online or incremental) learning algorithm would be useful in this situation. In this work, an adaptive learning and classification algorithm based on hidden Markov models (HMMs) is applied to human activity recognition, and an architecture model for smartphones is proposed. To create a self-learning method, a technique that involves building an incremental algorithm in a maximal likelihood framework has been developed. The adaptive algorithms created enable fast self-learning of the model parameters without requiring the device to store data obtained from sensors. It also does not require sending gathered data to a server over the network for additional processing, making them autonomous and independent from outside systems. Experiments involving the modeling of various activities as separate HMMs with different numbers of states, as well as modeling several activities connected to one HMM, were performed. A public dataset called the Activity Recognition Dataset was considered for this study. To generalize the results, different performance metrics were used in the validation of the proposed algorithm. |
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| institution | Kabale University |
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| language | English |
| publishDate | 2025-07-01 |
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| spelling | doaj-art-dfc3df2d9c6447cbba8740a7d7133c292025-08-20T03:58:26ZengMDPI AGApplied Sciences2076-34172025-07-011514773110.3390/app15147731Adaptive Learning Approach for Human Activity Recognition Using Data from Smartphone SensorsLeonidas Sakalauskas0Ingrida Vaiciulyte1Faculty of Informatics, Šiauliai University of Applied Sciences, Ausros st 40, LT-76241 Šiauliai, LithuaniaFaculty of Informatics, Šiauliai University of Applied Sciences, Ausros st 40, LT-76241 Šiauliai, LithuaniaEvery day humans interact with smartphones that have embedded sensors that enable the tracking of changing physical activities of the device owner. However, several problems arise with the recognition of multiple activities (such as walking, sitting, running, and other) on smartphones. Firstly, most of the devices do not recognize some activities well, such as walking upstairs or downstairs. Secondly, recognition algorithms are embedded into smartphone software and are static, unless updated. In this case, a recognition algorithm must be re-trained with training data of a specific size. Thus, an adaptive (also known as, online or incremental) learning algorithm would be useful in this situation. In this work, an adaptive learning and classification algorithm based on hidden Markov models (HMMs) is applied to human activity recognition, and an architecture model for smartphones is proposed. To create a self-learning method, a technique that involves building an incremental algorithm in a maximal likelihood framework has been developed. The adaptive algorithms created enable fast self-learning of the model parameters without requiring the device to store data obtained from sensors. It also does not require sending gathered data to a server over the network for additional processing, making them autonomous and independent from outside systems. Experiments involving the modeling of various activities as separate HMMs with different numbers of states, as well as modeling several activities connected to one HMM, were performed. A public dataset called the Activity Recognition Dataset was considered for this study. To generalize the results, different performance metrics were used in the validation of the proposed algorithm.https://www.mdpi.com/2076-3417/15/14/7731human activity recognitionhidden Markov modelsHMMsmartphone sensorsunsupervised learning |
| spellingShingle | Leonidas Sakalauskas Ingrida Vaiciulyte Adaptive Learning Approach for Human Activity Recognition Using Data from Smartphone Sensors Applied Sciences human activity recognition hidden Markov models HMM smartphone sensors unsupervised learning |
| title | Adaptive Learning Approach for Human Activity Recognition Using Data from Smartphone Sensors |
| title_full | Adaptive Learning Approach for Human Activity Recognition Using Data from Smartphone Sensors |
| title_fullStr | Adaptive Learning Approach for Human Activity Recognition Using Data from Smartphone Sensors |
| title_full_unstemmed | Adaptive Learning Approach for Human Activity Recognition Using Data from Smartphone Sensors |
| title_short | Adaptive Learning Approach for Human Activity Recognition Using Data from Smartphone Sensors |
| title_sort | adaptive learning approach for human activity recognition using data from smartphone sensors |
| topic | human activity recognition hidden Markov models HMM smartphone sensors unsupervised learning |
| url | https://www.mdpi.com/2076-3417/15/14/7731 |
| work_keys_str_mv | AT leonidassakalauskas adaptivelearningapproachforhumanactivityrecognitionusingdatafromsmartphonesensors AT ingridavaiciulyte adaptivelearningapproachforhumanactivityrecognitionusingdatafromsmartphonesensors |