Monitoring Wound Health through Bandages with Passive LC Resonant Sensors.
Sadaf CharkhabiKyle J JacksonAndee M BeierleAdam R CarrEric M ZellnerNigel Forest ReuelPublished in: ACS sensors (2020)
This paper details a passive, inductor-capacitor (LC) resonant sensor embedded in a commercial dressing for low-cost, contact-free monitoring of a wound; this would enable tracking of the healing process while keeping the site closed and sterile. Spiral LC resonators were fabricated from flexible, copper-coated polyimide and interrogated using external reader antennas connected to a two-port vector network analyzer; the forward transmission scattering parameter (S21) magnitude was collected, and the resonant frequency (MHz) and the peak-to-peak amplitude of the resonant feature were identified. These increase during the healing process as the permittivity and conductivity of the tissue change. The sensor was first tested on gelatin-based tissue-mimicking phantoms that simulate layers of muscle, blood, fat, and skin at varying phases of wound healing. Finite element modeling was also used to verify the empirical results based on the expected variations in dielectric properties of the tissue. The performance of the resonant sensors for in vivo applications was investigated by conducting animal studies using canine patients that presented with a natural wound as well as a controlled cohort of rat models with surgically administered wounds. Finally, transfer functions are presented that relate the resonant frequency to wound size using an exponential model (R2 = 0.58-0.96). The next steps in sensor design and fabrication as well as the reading platform to achieve the goal of a universal calibration curve are then discussed.
Keyphrases
- low cost
- wound healing
- energy transfer
- end stage renal disease
- simultaneous determination
- chronic kidney disease
- surgical site infection
- healthcare
- public health
- newly diagnosed
- ejection fraction
- finite element
- mass spectrometry
- adipose tissue
- mental health
- prognostic factors
- liquid chromatography
- peritoneal dialysis
- social media
- deep learning
- high throughput
- risk assessment
- tissue engineering
- working memory
- resting state
- single cell
- hyaluronic acid
- fatty acid
- climate change