SmartSpacer: Design, Implementation, and In-Vitro Validation of a Multimodal Sensorized Knee Spacer for Continuous Infection Monitoring in Two-Stage Revision Arthroplasty

Abstract

Periprosthetic joint infection (PJI) is the leading cause of failure in two-stage revision total knee arthroplasty (TKA). The timing of reimplantation currently relies on subjective clinical assessment, as no established method enables continuous, objective, local monitoring of infection dynamics during the spacer interval. We present the SmartSpacer, a sensorized antibiotic-loaded PMMA knee spacer integrating a miniaturized PCB within the tibial component (65 x 45 x 12 mm). The system incorporates digital temperature sensors, a CMOS camera module, a spectrometer, an inertial measurement unit, and a Bluetooth Low Energy (BLE) 5.2 transceiver. Firmware was developed on Zephyr RTOS with aggressive power management. Validation experiments covered power consumption profiling, BLE signal transmission through air, phantom liquid, and ex-vivo porcine knee tissue, temperature accuracy against a calibrated PT100 reference, and motion detection in seven healthy volunteers across three activity protocols. Firmware optimization reduced quiescent current from 700-850 {micro}A to 8 {micro}A, projecting a battery life exceeding 600 days at a clinically relevant sampling rate of one image and one spectrum per hour -- more than an order of magnitude beyond the maximum spacer implantation duration. BLE connectivity was maintained reliably up to 6 m through tissue-equivalent phantom liquid and up to 8-9 m in open air. Temperature sensors achieved {+/-}0.16 {degrees}C steady-state accuracy with self-heating artefacts below 0.15 {degrees}C. Motion detection scaled proportionally with activity intensity, though inter-subject variability in crutch-walking indicated that patient-specific calibration will be required. The SmartSpacer introduces an in vivo wearable - a temporary, implantable knee spacer providing continuous, wireless, multiparametric monitoring within the joint space. It has the potential to transform two-stage revision arthroplasty from empirically timed to data-driven, individualized clinical decision-making.