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Rahemtulla, Z. 
ORCID: 0000-0002-4695-821X,
2023.
Electronic textile garments for fall and near-fall detection.
PhD, Nottingham Trent University.
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Zahra Rahemtulla 2023.pdf - Published version Download (14MB) | Preview |
Abstract
The world population is ageing and one of the biggest detriments to the quality of life of older people is falls. The aim of this thesis is to develop an electronic textiles (E-textile) garment using electronic yarn (E-yarn) technology for near-fall and fall detection. Near-falls are a loss of balance that can be corrected. An increased number in near-falls is seen as a precursor for falls. If near-falls can be detected, hopefully this can lead to fall prevention.
The first step to creating an E-textile for near-fall was to determine the appropriate sensor for near-fall detection. Within the literature there are more studies conducted on fall detection systems rather than near-fall detection. Consequently, both types of system were reviewed. Informed by the literature, it was concluded that an inertial measurement unit (IMU) would be used to manufacturing a motion sensing E-yarn.
Once the sensor had been determined, the optimal placement of the sensor on the body needed to be found. In accordance with the literature six locations were explored, the waist, chest, wrist, lower back, thigh and ankle. A pilot study was conducted, and the results showed that either the waist, thigh or ankle were best.
Interviews and a focus group were held to design an E-textile garment that an older person would be willing to wear. Interviews on clothing preferences, attitudes towards falls, and wearable technology for fall prevention were conducted. Non-functioning prototypes were made and shared with a focus group to determine which would be used in the final design. The design chosen was an over-sock.
Lastly, a functioning E-textile garment was developed and tested on young healthy volunteers. The E-textile garment can accurately classify between three types of activities of daily living and three type of falls with an accuracy of 85.7%. When classifying between ADLs and the falls, the accuracy of detection was 99.4%. Furthermore, when classifying between the ADLs, the falls, and a near-fall event an accuracy of 94.2% was achieved.
This thesis contributes new knowledge to the field of E-textiles by using human centered design to create an E-textile garment people are willing to wear. It also has created the first near-fall and fall detection system in the form of an E-textile and presents the first E-yarn to contain an IMU.
| Item Type: | Thesis | ||||||||||||
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| Creators: | Rahemtulla, Z. | ||||||||||||
| Contributors: |
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| Date: | March 2023 | ||||||||||||
| Rights: | This work is the intellectual property of the author. You may copy up to 5% of this work for private study, or personal, non-commercial research. Any re-use of the information contained within this document should be fully referenced, quoting the author, title, university, degree level and pagination. Queries or requests for any other use, or if a more substantial copy is required, should be directed in the owner of the Intellectual Property Rights. | ||||||||||||
| Divisions: | Schools > School of Art and Design | ||||||||||||
| Record created by: | Linda Sullivan | ||||||||||||
| Date Added: | 19 Dec 2023 16:39 | ||||||||||||
| Last Modified: | 19 Dec 2023 16:39 | ||||||||||||
| URI: | https://irep.ntu.ac.uk/id/eprint/50573 |
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