Characterizing the Sensing Response of Carbon Nanocomposite-Based Wearable Sensors on Elbow Joint Using an End Point Robot and Virtual Reality.


Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
28 Jul 2024
Historique:
received: 14 06 2024
revised: 25 07 2024
accepted: 26 07 2024
medline: 10 8 2024
pubmed: 10 8 2024
entrez: 10 8 2024
Statut: epublish

Résumé

Physical therapy is often essential for complete recovery after injury. However, a significant population of patients fail to adhere to prescribed exercise regimens. Lack of motivation and inconsistent in-person visits to physical therapy are major contributing factors to suboptimal exercise adherence, slowing the recovery process. With the advancement of virtual reality (VR), researchers have developed remote virtual rehabilitation systems with sensors such as inertial measurement units. A functional garment with an integrated wearable sensor can also be used for real-time sensory feedback in VR-based therapeutic exercise and offers affordable remote rehabilitation to patients. Sensors integrated into wearable garments offer the potential for a quantitative range of motion measurements during VR rehabilitation. In this research, we developed and validated a carbon nanocomposite-coated knit fabric-based sensor worn on a compression sleeve that can be integrated with upper-extremity virtual rehabilitation systems. The sensor was created by coating a commercially available weft knitted fabric consisting of polyester, nylon, and elastane fibers. A thin carbon nanotube composite coating applied to the fibers makes the fabric electrically conductive and functions as a piezoresistive sensor. The nanocomposite sensor, which is soft to the touch and breathable, demonstrated high sensitivity to stretching deformations, with an average gauge factor of ~35 in the warp direction of the fabric sensor. Multiple tests are performed with a Kinarm end point robot to validate the sensor for repeatable response with a change in elbow joint angle. A task was also created in a VR environment and replicated by the Kinarm. The wearable sensor can measure the change in elbow angle with more than 90% accuracy while performing these tasks, and the sensor shows a proportional resistance change with varying joint angles while performing different exercises. The potential use of wearable sensors in at-home virtual therapy/exercise was demonstrated using a Meta Quest 2 VR system with a virtual exercise program to show the potential for at-home measurements.

Identifiants

pubmed: 39123940
pii: s24154894
doi: 10.3390/s24154894
pii:
doi:

Substances chimiques

Nanotubes, Carbon 0
Carbon 7440-44-0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : NIH HHS
ID : 3P20GM103446-23S4
Pays : United States
Organisme : National Science Foundation
ID : 2329838
Organisme : University of Delaware College of Engineering's INAccelerator Grant
ID : NA

Auteurs

Amit Chaudhari (A)

Center for Composite Materials, University of Delaware, Newark, DE 19716, USA.

Rakshith Lokesh (R)

Department of Biomedical Engineering, University of Delaware, Newark, DE 19716, USA.

Vuthea Chheang (V)

Department of Computer and Information Sciences, University of Delaware, Newark, DE 19716, USA.

Sagar M Doshi (SM)

Center for Composite Materials, University of Delaware, Newark, DE 19716, USA.

Roghayeh Leila Barmaki (RL)

Department of Computer and Information Sciences, University of Delaware, Newark, DE 19716, USA.

Joshua G A Cashaback (JGA)

Department of Biomedical Engineering, University of Delaware, Newark, DE 19716, USA.

Erik T Thostenson (ET)

Department of Mechanical Engineering, Department of Materials Science and Engineering, and Center for Composite Materials, University of Delaware, Newark, DE 19716, USA.

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Classifications MeSH