Toward
the adoption of artificial intelligence-enabled wearable
sensors interconnected with intelligent medical objects, this contactless
multi-intelligent wearable technology provides a solution for healthcare
to monitor hard-to-heal wounds and create optimal efficiencies for
clinical professionals by minimizing the risk of disease infection.
This article addressed a flexible artificial intelligence-guiding
(FLEX-AI) wearable sensor that can be operated with a deep artificial
neural network (deep ANN) algorithm for chronic wound monitoring via short-range communication toward a seamless, MXENE-attached,
radio frequency-tuned, and wound dressing-integrated (SMART-WD) bandage.
Based on a supervised training set of on-contact pH-responsive voltage
output, the confusion matrix for healing-stage recognition from this
deep ANN machine learning revealed an accuracy of 94.6% for the contactless
measurement. The core analytical design of these smart bandages integrated
wound dressing of poly(vinyl acrylic) gel@PANI/Cu2O NPs
for instigating pH-responsive current during the wound healing process.
Effectively, a chip-free bandage tag was fabricated with a capacitive
Mxene/PTFE electret and adhesive acrylic inductance to match the resonance
frequency generated by the intelligent wearable antenna. Under zero-current
electrochemical potential, the wound dressing attained a slope of
−76 mV/pH. With the higher activation voltage applied toward
the wound dressing electrodes, cuprous ions intercalated more into
the hybrid PVA gel/PANI shell, resulting in an exponential increase
of the two-terminal current response. The healing phase diagram was
classified into regimes of fast-curing, slow-curing, and no-curing
for skin disease treatment with corticosteroids. Ultimately, the near-field
sensing technology offers adequate information for guiding treatment
decisions as well as drug effectiveness for wound care.