An Online Method for Supporting and Monitoring Repetitive Physical Activities Based on Restricted Boltzmann Machines
Human activity recognition has been widely used to monitor users during physical activities. By embedding a pre-trained model into wearable devices with an inertial measurement unit, it is possible to identify the activity being executed, count steps and activity duration time, and even predict when...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-09-01
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| Series: | Journal of Sensor and Actuator Networks |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2224-2708/12/5/70 |
| _version_ | 1827720656995745792 |
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| author | Marcio Alencar Raimundo Barreto Eduardo Souto Horacio Oliveira |
| author_facet | Marcio Alencar Raimundo Barreto Eduardo Souto Horacio Oliveira |
| author_sort | Marcio Alencar |
| collection | DOAJ |
| description | Human activity recognition has been widely used to monitor users during physical activities. By embedding a pre-trained model into wearable devices with an inertial measurement unit, it is possible to identify the activity being executed, count steps and activity duration time, and even predict when the user should hydrate himself. Despite these interesting applications, these approaches are limited by a set of pre-trained activities, making them unable to learn new human activities. In this paper, we introduce a novel approach for generating runtime models to give the users feedback that helps them to correctly perform repetitive physical activities. To perform a distributed analysis, the methodology focuses on applying the proposed method to each specific body segment. The method adopts the Restricted Boltzmann Machine to learn the patterns of repetitive physical activities and, at the same time, provides suggestions for adjustments if the repetition is not consistent with the model. The learning and the suggestions are both based on inertial measurement data mainly considering movement acceleration and amplitude. The results show that by applying the model’s suggestions to the evaluation data, the adjusted output was up to 3.68x more similar to the expected movement than the original data. |
| first_indexed | 2024-03-10T21:07:29Z |
| format | Article |
| id | doaj.art-083a1a1eda344ad4ae61693fba82b08d |
| institution | Directory Open Access Journal |
| issn | 2224-2708 |
| language | English |
| last_indexed | 2024-03-10T21:07:29Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Sensor and Actuator Networks |
| spelling | doaj.art-083a1a1eda344ad4ae61693fba82b08d2023-11-19T17:02:56ZengMDPI AGJournal of Sensor and Actuator Networks2224-27082023-09-011257010.3390/jsan12050070An Online Method for Supporting and Monitoring Repetitive Physical Activities Based on Restricted Boltzmann MachinesMarcio Alencar0Raimundo Barreto1Eduardo Souto2Horacio Oliveira3Institute of Computing, Federal University of Amazonas, Manaus 1200, BrazilInstitute of Computing, Federal University of Amazonas, Manaus 1200, BrazilInstitute of Computing, Federal University of Amazonas, Manaus 1200, BrazilInstitute of Computing, Federal University of Amazonas, Manaus 1200, BrazilHuman activity recognition has been widely used to monitor users during physical activities. By embedding a pre-trained model into wearable devices with an inertial measurement unit, it is possible to identify the activity being executed, count steps and activity duration time, and even predict when the user should hydrate himself. Despite these interesting applications, these approaches are limited by a set of pre-trained activities, making them unable to learn new human activities. In this paper, we introduce a novel approach for generating runtime models to give the users feedback that helps them to correctly perform repetitive physical activities. To perform a distributed analysis, the methodology focuses on applying the proposed method to each specific body segment. The method adopts the Restricted Boltzmann Machine to learn the patterns of repetitive physical activities and, at the same time, provides suggestions for adjustments if the repetition is not consistent with the model. The learning and the suggestions are both based on inertial measurement data mainly considering movement acceleration and amplitude. The results show that by applying the model’s suggestions to the evaluation data, the adjusted output was up to 3.68x more similar to the expected movement than the original data.https://www.mdpi.com/2224-2708/12/5/70physical activitiespattern recognitionrestricted boltzmann machinemovement adjustments |
| spellingShingle | Marcio Alencar Raimundo Barreto Eduardo Souto Horacio Oliveira An Online Method for Supporting and Monitoring Repetitive Physical Activities Based on Restricted Boltzmann Machines Journal of Sensor and Actuator Networks physical activities pattern recognition restricted boltzmann machine movement adjustments |
| title | An Online Method for Supporting and Monitoring Repetitive Physical Activities Based on Restricted Boltzmann Machines |
| title_full | An Online Method for Supporting and Monitoring Repetitive Physical Activities Based on Restricted Boltzmann Machines |
| title_fullStr | An Online Method for Supporting and Monitoring Repetitive Physical Activities Based on Restricted Boltzmann Machines |
| title_full_unstemmed | An Online Method for Supporting and Monitoring Repetitive Physical Activities Based on Restricted Boltzmann Machines |
| title_short | An Online Method for Supporting and Monitoring Repetitive Physical Activities Based on Restricted Boltzmann Machines |
| title_sort | online method for supporting and monitoring repetitive physical activities based on restricted boltzmann machines |
| topic | physical activities pattern recognition restricted boltzmann machine movement adjustments |
| url | https://www.mdpi.com/2224-2708/12/5/70 |
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