Abstract
We have developed, modeled, fabricated, and tested a passive wireless sensor system that exhibits a linear frequency-displacement relationship. The displacement sensor is comprised of two anti-aligned Archimedean coils separated by an insulating dielectric layer. There are no electrical connections between the two coils and there are no onboard electronics. The two coils are inductively and capacitively coupled due to their close proximity. The sensor system is interrogated wirelessly by monitoring the return loss parameter from a vector network analyzer. The resonant frequency of the sensor is dependent on the displacement between the two coils. Due to changes in the inductive and capacitive coupling between the coils at different distances, the resonant frequency is modulated by coil separation. In a specified range, the frequency shift can be linearized with respect to coil separation. Batch fabrication techniques were used to fabricate copper coils for experimental testing with air as the dielectric. Through testing, we validated the performance of sensors as predicted within acceptable errors. Because of its simplicity, this displacement sensor has potential applications for in vivo sensing.
Highlights
Recent trends in healthcare have placed an emphasis on cost savings
We have developed a simple, passive, wireless implantable sensing system which is small enough to obviate the need for modification of implants, inexpensive enough to introduce negligible increase in cost, and simple enough that it is robust in the in vivo environment
We have developed an analytical relationship between the geometry of the coils and the sensor resonant frequency using a lumped constant LC model [8, 13]
Summary
Recent trends in healthcare have placed an emphasis on cost savings. For patients undergoing surgery, strategies which facilitate shorter recovery times, result in fewer complications, and require fewer revisions can affect substantial cost reductions. Objective real-time quantitative patient-specific data after surgery can provide critical information on the progression of recovery or healing of a patient. These data can be used to customize care and influence the rehabilitation regimen on a patient-specific basis. In orthopaedic surgery and neurosurgery, implantable sensors are a potential enabling technology for obtaining patient-specific data. Post-operatively, the sensors can provide objective quantitative data informing the healthcare team about the progression of healing (or lack thereof). This can guide post-operative care and rehabilitation therapy which can improve outcomes and reduce recovery time. Early detection of a fracture non-union can be achieved by measuring load sharing between the implant and bone following surgery [1]
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