Conference proceeding
Auxetic cTPU Soft Strain Sensor Augmented with Artificial Intelligence for Human Gait Analytics
2025 IEEE 5th International Conference on Human-Machine Systems (ICHMS), pp.269-275
IEEE International Conference on Human-Machine Systems, 5th (Abu Dhabi, United Arab Emirates, 26/05/2025–28/05/2025)
2025
Metrics
1 Record Views
Abstract
An inclined demand for flexible and wearable electronics, driven by progress in artificial intelligence technologies, underscores the significance of auxetic sensors in healthcare, medical rehabilitation, soft robotics, and humanmachine interfaces. To achieve widespread adoption, these sensors should exhibit high sensitivity, exceptional stretchability, and long-lasting durability. This paper delves into the potential for developing such wearable soft strain sensors based on conductive thermoplastic polyurethane (cTPU) 3D printed as auxetic soft metamaterials. Simultaneous empirical mechanical and electrical measurements for the auxetic soft sensor designs were compared with numerical simulations. Following the auxetic cTPU soft strain sensor was tested in human gait analytics to predict the gait type of the wearer by employing a single auxetic cTPU soft strain sensor. The results suggest that such soft sensors based on metamaterials are genuine candidates for applications in robotic, healthcare and human-robot interfaces when realized with 3D printing and artificial intelligence.
Details
- Title
- Auxetic cTPU Soft Strain Sensor Augmented with Artificial Intelligence for Human Gait Analytics
- Creators
- Rahim Mutlu - Southern Cross UniversityAbeer ElKhouly - University of Wollongong in DubaiUmar Asghar - University of Wollongong in DubaiCiara Odriscoll - University of Wollongong in Dubai
- Publication Details
- 2025 IEEE 5th International Conference on Human-Machine Systems (ICHMS), pp.269-275
- Conference
- IEEE International Conference on Human-Machine Systems, 5th (Abu Dhabi, United Arab Emirates, 26/05/2025–28/05/2025)
- Publisher
- IEEE
- Identifiers
- 991013316121402368
- Copyright
- © 2025 by the Institute of Electrical and Electronics Engineers, Inc. All rights reserved.
- Academic Unit
- Faculty of Science and Engineering
- Language
- English
- Resource Type
- Conference proceeding