Optimal locations and computational frameworks of FSR and IMU sensors for measuring gait abnormalities
Neuromuscular diseases cause abnormal joint movements and drastically alter gait patterns in patients. The analysis of abnormal gait patterns can provide clinicians with an in-depth insight into implementing appropriate rehabilitation therapies. Wearable sensors are used to measure the gait patterns...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-04-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023024179 |
| _version_ | 1797836932014669824 |
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| author | Soumya K. Manna M.A. Hannan Bin Azhar Ann Greace |
| author_facet | Soumya K. Manna M.A. Hannan Bin Azhar Ann Greace |
| author_sort | Soumya K. Manna |
| collection | DOAJ |
| description | Neuromuscular diseases cause abnormal joint movements and drastically alter gait patterns in patients. The analysis of abnormal gait patterns can provide clinicians with an in-depth insight into implementing appropriate rehabilitation therapies. Wearable sensors are used to measure the gait patterns of neuromuscular patients due to their non-invasive and cost-efficient characteristics. FSR and IMU sensors are the most popular and efficient options. When assessing abnormal gait patterns, it is important to determine the optimal locations of FSRs and IMUs on the human body, along with their computational framework. The gait abnormalities of different types and the gait analysis systems based on IMUs and FSRs have therefore been investigated. After studying a variety of research articles, the optimal locations of the FSR and IMU sensors were determined by analysing the main pressure points under the feet and prime anatomical locations on the human body. A total of seven locations (the big toe, heel, first, third, and fifth metatarsals, as well as two close to the medial arch) can be used to measure gate cycles for normal and flat feet. It has been found that IMU sensors can be placed in four standard anatomical locations (the feet, shank, thigh, and pelvis). A section on computational analysis is included to illustrate how data from the FSR and IMU sensors are processed. Sensor data is typically sampled at 100 Hz, and wireless systems use a range of microcontrollers to capture and transmit the signals. The findings reported in this article are expected to help develop efficient and cost-effective gait analysis systems by using an optimal number of FSRs and IMUs. |
| first_indexed | 2024-04-09T15:17:55Z |
| format | Article |
| id | doaj.art-1f5b3175ee0e485a87101668f4d23723 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-09T15:17:55Z |
| publishDate | 2023-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-1f5b3175ee0e485a87101668f4d237232023-04-29T14:55:45ZengElsevierHeliyon2405-84402023-04-0194e15210Optimal locations and computational frameworks of FSR and IMU sensors for measuring gait abnormalitiesSoumya K. Manna0M.A. Hannan Bin Azhar1Ann Greace2Corresponding author.; School of Engineering, Technology and Design, Canterbury Christ Church University, CT11QU, UKSchool of Engineering, Technology and Design, Canterbury Christ Church University, CT11QU, UKSchool of Engineering, Technology and Design, Canterbury Christ Church University, CT11QU, UKNeuromuscular diseases cause abnormal joint movements and drastically alter gait patterns in patients. The analysis of abnormal gait patterns can provide clinicians with an in-depth insight into implementing appropriate rehabilitation therapies. Wearable sensors are used to measure the gait patterns of neuromuscular patients due to their non-invasive and cost-efficient characteristics. FSR and IMU sensors are the most popular and efficient options. When assessing abnormal gait patterns, it is important to determine the optimal locations of FSRs and IMUs on the human body, along with their computational framework. The gait abnormalities of different types and the gait analysis systems based on IMUs and FSRs have therefore been investigated. After studying a variety of research articles, the optimal locations of the FSR and IMU sensors were determined by analysing the main pressure points under the feet and prime anatomical locations on the human body. A total of seven locations (the big toe, heel, first, third, and fifth metatarsals, as well as two close to the medial arch) can be used to measure gate cycles for normal and flat feet. It has been found that IMU sensors can be placed in four standard anatomical locations (the feet, shank, thigh, and pelvis). A section on computational analysis is included to illustrate how data from the FSR and IMU sensors are processed. Sensor data is typically sampled at 100 Hz, and wireless systems use a range of microcontrollers to capture and transmit the signals. The findings reported in this article are expected to help develop efficient and cost-effective gait analysis systems by using an optimal number of FSRs and IMUs.http://www.sciencedirect.com/science/article/pii/S2405844023024179Gait abnormalitiesMeasurement of gaitSensor locationComputational framework |
| spellingShingle | Soumya K. Manna M.A. Hannan Bin Azhar Ann Greace Optimal locations and computational frameworks of FSR and IMU sensors for measuring gait abnormalities Heliyon Gait abnormalities Measurement of gait Sensor location Computational framework |
| title | Optimal locations and computational frameworks of FSR and IMU sensors for measuring gait abnormalities |
| title_full | Optimal locations and computational frameworks of FSR and IMU sensors for measuring gait abnormalities |
| title_fullStr | Optimal locations and computational frameworks of FSR and IMU sensors for measuring gait abnormalities |
| title_full_unstemmed | Optimal locations and computational frameworks of FSR and IMU sensors for measuring gait abnormalities |
| title_short | Optimal locations and computational frameworks of FSR and IMU sensors for measuring gait abnormalities |
| title_sort | optimal locations and computational frameworks of fsr and imu sensors for measuring gait abnormalities |
| topic | Gait abnormalities Measurement of gait Sensor location Computational framework |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023024179 |
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