An increasing demand for total hip replacement (THR) surgeries, due to an ageing population and an increasing number of younger patients undergoing THR surgery, has resulted in a proportional increase in the number of patients requiring revision surgery to remedy complications or failures of their THR implant components. Revision surgery of THR implants is expensive and time consuming and places an increasing burden on health funding and surgical resources. Consequently, a large emphasis has been placed on developing more accurate diagnostic methods which allow early diagnosis of THR implant wear and complications. This thesis investigates the potential of acoustic emission (AE) monitoring as a method for the early diagnosis of THR implant wear and complications.

A detailed data collection protocol is presented for the use of an AE monitoring device on THR patients in the clinical environment (in-vivo) and on implants retrieved from THR patients in the laboratory environment (in-vitro). The AE monitoring device samples AEs at 100 kHz using ultrasonic receivers placed on the skin surface over the hip joint of THR patients. During in-vivo monitoring, THR study participants perform basic motions such as walking, sitting to standing, and stair ascents and descents. In-vitro data collection utilises the same AE sensing equipment to record AEs from robotic and manual manipulations of THR implants retrieved during the revision surgery of study participants.

A detailed analysis was performed on the time and frequency domain characteristics of the in- vivo AEs collected from 117 THR patients and 23 control study participants. In-vitro data was collected and analysed from the retrieved implants of five THR patients with ceramic implant bearing components. Dominant frequencies of in-vivo AE signals were found to be in the 1-4 kHz range. In-vitro AEs from implant main bearing surface manipulations yielded dominant

frequency content in the same 1-4 kHz range. However, in-vitro AEs from relative motion at the trunnion Morse taper of the femoral implant components yielded squeaking AEs with dominant frequency content in the 18-22 kHz range.

Further work developed and implemented an event detection algorithm to automate the detection of regions of interest (AE events) from the large dataset of AE signals. The AE events from patients who progressed onto revision surgery were analysed and grouped using the clinically identified reasons for revision of excessive noise, loosening, and excessive wear. A statistical analysis of AE event parameters was performed on the grouped data and the analysis indicated that single parameters alone were not an effective way to categorise participant AE data. A multidimensional analysis of the event parameters was performed, which took the form of two-parameter scatter plot comparisons and a principal component analysis. The multidimensional analyses showed that AEs were generally similar across all groups. However, some definite differences in the distributions of AE events from the different groups was apparent, particularly those within the loosening group of THR participants. The overall results from the AE analysis showed promise in the diagnostic potential of the AE monitoring technique. However, the results fell well short of being able to lead to a reliable failure diagnosis in their current state.

Finally, a system to concurrently collect combined AE and gait data from THR patients was developed. The combined system provides the ability to continuously link the AE signals with the specific motion of the patient, and therefore to the motion of the implant components. The system itself and detailed data collection protocols were developed and combined AE and gait data was collected and analysed from three THR study participants. The data from the combined system has shown promising intra- and inter-participant consistency with substantial AE activity observed to occur repeatedly during a subset of the stance phase of the gait cycle.

Audible squeaking was also observed to occur consistently during the terminal stance phase for one of the participants, repeatedly occurring within the 30-40% range of the gait cycle. The initial testing from the combined AE and gait analysis has indicated relationships likely exist between AEs and particular implant motions and loadings.

While further research is needed before the AE monitoring technique could be used as a clinical diagnostic method that might augment existing methods, this thesis presents some key steps towards this final overarching goal. Critical assessment of the current methods is made, and recommendations for key future research are presented.