Multimodal Classification Technique for Fall Detection of Alzheimer’s Patients by Integration of a Novel Piezoelectric Crystal Accelerometer and Aluminum Gyroscope with Vision Data
Smart expert systems line up with various applications to enhance the quality of lifestyle of human beings, such as major applications for smart health monitoring systems. An intelligent assistive system is one such application to assist Alzheimer’s patients in carrying out day-to-day activities and...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Advances in Materials Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/9258620 |
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| author | V. Mohan Gowda Megha P. Arakeri Vasireddy Raghu Ram Prasad |
| author_facet | V. Mohan Gowda Megha P. Arakeri Vasireddy Raghu Ram Prasad |
| author_sort | V. Mohan Gowda |
| collection | DOAJ |
| description | Smart expert systems line up with various applications to enhance the quality of lifestyle of human beings, such as major applications for smart health monitoring systems. An intelligent assistive system is one such application to assist Alzheimer’s patients in carrying out day-to-day activities and real-time monitoring by the caretakers. Fall detection is one of the tasks of an assistive system; many existing methods primarily focus on either vision or sensor data. Vision-based methods suffer from false positive results because of occlusion, and sensor-based methods yield false results because of the patient’s long-term lying posture. We address this problem by proposing a multimodel fall detection system (MMFDS) with hybrid data, which includes both vision and sensor data. Random forest and long-term recurrent convolution networks (LRCN) are the primary classification algorithms for sensor data and vision data, respectively. MMFDS integrates sensor and vision data to enhance fall detection accuracy by incorporating an ensemble approach named majority voting for the hybrid data. On evaluating the proposed work on the UP fall detection dataset, accuracy was enhanced to 99.2%, with an improvement in precision, F1 score, and recall. |
| format | Article |
| id | doaj-art-d01adddf8796460690a4120be4ccfe0f |
| institution | OA Journals |
| issn | 1687-8442 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Materials Science and Engineering |
| spelling | doaj-art-d01adddf8796460690a4120be4ccfe0f2025-08-20T02:20:03ZengWileyAdvances in Materials Science and Engineering1687-84422022-01-01202210.1155/2022/9258620Multimodal Classification Technique for Fall Detection of Alzheimer’s Patients by Integration of a Novel Piezoelectric Crystal Accelerometer and Aluminum Gyroscope with Vision DataV. Mohan Gowda0Megha P. Arakeri1Vasireddy Raghu Ram Prasad2Department of Computer Science and EngineeringDepartment of Information Science & EngineeringFaculty of Electrical and Computer EngineeringSmart expert systems line up with various applications to enhance the quality of lifestyle of human beings, such as major applications for smart health monitoring systems. An intelligent assistive system is one such application to assist Alzheimer’s patients in carrying out day-to-day activities and real-time monitoring by the caretakers. Fall detection is one of the tasks of an assistive system; many existing methods primarily focus on either vision or sensor data. Vision-based methods suffer from false positive results because of occlusion, and sensor-based methods yield false results because of the patient’s long-term lying posture. We address this problem by proposing a multimodel fall detection system (MMFDS) with hybrid data, which includes both vision and sensor data. Random forest and long-term recurrent convolution networks (LRCN) are the primary classification algorithms for sensor data and vision data, respectively. MMFDS integrates sensor and vision data to enhance fall detection accuracy by incorporating an ensemble approach named majority voting for the hybrid data. On evaluating the proposed work on the UP fall detection dataset, accuracy was enhanced to 99.2%, with an improvement in precision, F1 score, and recall.http://dx.doi.org/10.1155/2022/9258620 |
| spellingShingle | V. Mohan Gowda Megha P. Arakeri Vasireddy Raghu Ram Prasad Multimodal Classification Technique for Fall Detection of Alzheimer’s Patients by Integration of a Novel Piezoelectric Crystal Accelerometer and Aluminum Gyroscope with Vision Data Advances in Materials Science and Engineering |
| title | Multimodal Classification Technique for Fall Detection of Alzheimer’s Patients by Integration of a Novel Piezoelectric Crystal Accelerometer and Aluminum Gyroscope with Vision Data |
| title_full | Multimodal Classification Technique for Fall Detection of Alzheimer’s Patients by Integration of a Novel Piezoelectric Crystal Accelerometer and Aluminum Gyroscope with Vision Data |
| title_fullStr | Multimodal Classification Technique for Fall Detection of Alzheimer’s Patients by Integration of a Novel Piezoelectric Crystal Accelerometer and Aluminum Gyroscope with Vision Data |
| title_full_unstemmed | Multimodal Classification Technique for Fall Detection of Alzheimer’s Patients by Integration of a Novel Piezoelectric Crystal Accelerometer and Aluminum Gyroscope with Vision Data |
| title_short | Multimodal Classification Technique for Fall Detection of Alzheimer’s Patients by Integration of a Novel Piezoelectric Crystal Accelerometer and Aluminum Gyroscope with Vision Data |
| title_sort | multimodal classification technique for fall detection of alzheimer s patients by integration of a novel piezoelectric crystal accelerometer and aluminum gyroscope with vision data |
| url | http://dx.doi.org/10.1155/2022/9258620 |
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