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Currently, imaging methods are used to diagnose loosening of endoprosthetic implants, but fail to achieve 100% accuracy. In this study, a passive sensor array which is based on the interaction between magnetic oscillators inside the implant and an excitation coil outside the patient was investigated. The excited oscillators produce sound in the audible range, which varies according to the extent of loosening. By performing several experimental tests, the sensor array was optimized to guarantee reproducible and selective excitation of the sound emission. Variation in the distance between the oscillators demonstrated a definite influence on the quality of the generated sound signal. Furthermore, a numerical design analysis using the boundary element method was generated for consideration of the magnetic field and the selectivity of the oscillators during excitation. The numerical simulation of the coil showed the higher selectivity of a coil with a C-shape compared to a cylindrical coil. Based on these investigations, the passive sensor system reveals the potential for detection of implant loosening. Future aims include the further miniaturization of the oscillators and measurements to determine the sensitivity of the proposed sensor system.

BackgroundContemporary resuscitation guidelines for basic life support recommend an immediate onset of cardiac compressions in case of cardiac arrest followed by rescue breaths. Effective ventilation is often omitted due to fear of doing harm and fear of infectious diseases. In order to improve ventilation a pre-stage of an automatic respirator was developed for use by laypersons.MethodsFifty-two healthy volunteers were ventilated by means of a prototype respirator via a full-face mask in a pilot study. The pre-stage public access ventilator (PAV) consisted of a low-cost self-designed turbine, with sensors for differential pressure, flow, FO2, FCO2 and 3-axis acceleration measurement. Sensor outputs were used to control the respirator and to recognize conditions relevant for efficiency of ventilation and patients’ safety. Different respiratory manoeuvres were applied: a) pressure controlled ventilation (PCV), b) PCV with controlled leakage and c) PCV with simulated airway occlusion. Sensor signals were analysed to detect leakage and airway occlusion. Detection based upon sensor signals was compared with evaluation based on clinical observation and additional parameters such as exhaled CO2.ResultsPressure controlled ventilation could be realized in all volunteers. Leakage was recognized with 93.5% sensitivity and 93.5% specificity. Simulated airway occlusion was detected with 91.8% sensitivity and 91.7% specificity.ConclusionThe pre-stage PAV was able to detect potential complications relevant for patients’ safety such as leakage and airway occlusion in a proof of principle study. Prospectively, this device provides a respectable basis for the development of an automatic emergency respirator and may help to improve bystander resuscitation.Electronic supplementary materialThe online version of this article (10.1186/s12873-017-0150-5) contains supplementary material, which is available to authorized users.

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