A Novel Methodology for Human Kinematics Motion Detection Based on Smartphones Sensor Data Using Artificial Intelligence
Kinematic motion detection aims to determine a person’s actions based on activity data. Human kinematic motion detection has many valuable applications in health care, such as health monitoring, preventing obesity, virtual reality, daily life monitoring, assisting workers during industry manufacturi...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2023
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://depot.sorbonne.ae/handle/20.500.12458/1392 |
| الوسوم: |
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| _version_ | 1857415063995940864 |
|---|---|
| author | Raza, Ali |
| author2 | Al Nasar, Mohammad Rustom Hanandeh, Essam Said Abu Zitar, Raed Nasereddin, Ahmad Yacoub Abualigah, Laith |
| author2_role | author author author author author |
| author_facet | Raza, Ali Al Nasar, Mohammad Rustom Hanandeh, Essam Said Abu Zitar, Raed Nasereddin, Ahmad Yacoub Abualigah, Laith |
| author_role | author |
| dc.creator.none.fl_str_mv | Raza, Ali Al Nasar, Mohammad Rustom Hanandeh, Essam Said Abu Zitar, Raed Nasereddin, Ahmad Yacoub Abualigah, Laith |
| dc.date.none.fl_str_mv | 2023-04-11T08:35:15Z 2023-04-11T08:35:15Z 2023 |
| dc.identifier.none.fl_str_mv | https://depot.sorbonne.ae/handle/20.500.12458/1392 10.3390/technologies11020055 |
| dc.language.none.fl_str_mv | en |
| dc.relation.none.fl_str_mv | Technologies |
| dc.subject.none.fl_str_mv | human motion kinematics motion detection activity recognition smartphone sensors artificial intelligence machine learning |
| dc.title.none.fl_str_mv | A Novel Methodology for Human Kinematics Motion Detection Based on Smartphones Sensor Data Using Artificial Intelligence |
| dc.type.none.fl_str_mv | Controlled Vocabulary for Resource Type Genres::text::periodical::journal::contribution to journal::journal article |
| description | Kinematic motion detection aims to determine a person’s actions based on activity data. Human kinematic motion detection has many valuable applications in health care, such as health monitoring, preventing obesity, virtual reality, daily life monitoring, assisting workers during industry manufacturing, caring for the elderly. Computer vision-based activity recognition is challenging due to problems such as partial occlusion, background clutter, appearance, lighting, viewpoint, and changes in scale. Our research aims to detect human kinematic motions such as walking or running using smartphones’ sensor data within a high-performance framework. An existing dataset based on smartphones’ gyroscope and accelerometer sensor values is utilized for the experiments in our study. Sensor exploratory data analysis was conducted in order to identify valuable patterns and insights from sensor values. The six hyperparameters, tunned artificial indigence-based machine learning, and deep learning techniques were applied for comparison. Extensive experimentation showed that the ensemble learning-based novel ERD (ensemble random forest decision tree) method outperformed other state-of-the-art studies with high-performance accuracy scores. The proposed ERD method combines the random forest and decision tree models, which achieved a 99% classification accuracy score. The proposed method was successfully validated with the k-fold cross-validation approach. |
| id | sorbonner_78cd63def92a068a877468b35bbab8d4 |
| identifier_str_mv | 10.3390/technologies11020055 |
| language_invalid_str_mv | en |
| network_acronym_str | sorbonner |
| network_name_str | Sorbonne University Abu Dhabi repository |
| oai_identifier_str | oai:depot.sorbonne.ae:20.500.12458/1392 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | A Novel Methodology for Human Kinematics Motion Detection Based on Smartphones Sensor Data Using Artificial IntelligenceRaza, AliAl Nasar, Mohammad RustomHanandeh, Essam SaidAbu Zitar, RaedNasereddin, Ahmad YacoubAbualigah, Laithhuman motion kinematicsmotion detectionactivity recognitionsmartphone sensorsartificial intelligencemachine learningKinematic motion detection aims to determine a person’s actions based on activity data. Human kinematic motion detection has many valuable applications in health care, such as health monitoring, preventing obesity, virtual reality, daily life monitoring, assisting workers during industry manufacturing, caring for the elderly. Computer vision-based activity recognition is challenging due to problems such as partial occlusion, background clutter, appearance, lighting, viewpoint, and changes in scale. Our research aims to detect human kinematic motions such as walking or running using smartphones’ sensor data within a high-performance framework. An existing dataset based on smartphones’ gyroscope and accelerometer sensor values is utilized for the experiments in our study. Sensor exploratory data analysis was conducted in order to identify valuable patterns and insights from sensor values. The six hyperparameters, tunned artificial indigence-based machine learning, and deep learning techniques were applied for comparison. Extensive experimentation showed that the ensemble learning-based novel ERD (ensemble random forest decision tree) method outperformed other state-of-the-art studies with high-performance accuracy scores. The proposed ERD method combines the random forest and decision tree models, which achieved a 99% classification accuracy score. The proposed method was successfully validated with the k-fold cross-validation approach.2023-04-11T08:35:15Z2023-04-11T08:35:15Z2023Controlled Vocabulary for Resource Type Genres::text::periodical::journal::contribution to journal::journal articlehttps://depot.sorbonne.ae/handle/20.500.12458/139210.3390/technologies11020055enTechnologiesoai:depot.sorbonne.ae:20.500.12458/13922023-04-11T08:35:15Z |
| spellingShingle | A Novel Methodology for Human Kinematics Motion Detection Based on Smartphones Sensor Data Using Artificial Intelligence Raza, Ali human motion kinematics motion detection activity recognition smartphone sensors artificial intelligence machine learning |
| title | A Novel Methodology for Human Kinematics Motion Detection Based on Smartphones Sensor Data Using Artificial Intelligence |
| title_full | A Novel Methodology for Human Kinematics Motion Detection Based on Smartphones Sensor Data Using Artificial Intelligence |
| title_fullStr | A Novel Methodology for Human Kinematics Motion Detection Based on Smartphones Sensor Data Using Artificial Intelligence |
| title_full_unstemmed | A Novel Methodology for Human Kinematics Motion Detection Based on Smartphones Sensor Data Using Artificial Intelligence |
| title_short | A Novel Methodology for Human Kinematics Motion Detection Based on Smartphones Sensor Data Using Artificial Intelligence |
| title_sort | A Novel Methodology for Human Kinematics Motion Detection Based on Smartphones Sensor Data Using Artificial Intelligence |
| topic | human motion kinematics motion detection activity recognition smartphone sensors artificial intelligence machine learning |
| url | https://depot.sorbonne.ae/handle/20.500.12458/1392 |