RESUMO
Osseointegrated (OI) prostheses are a promising alternative to traditional socket prostheses. They can enhance the quality of life of amputees by avoiding fit and comfort issues commonly associated with sockets. The main structural element of the OI prosthesis is a biocompatible metallic implant that is surgically implanted into the bone of the residual limb. The implant is designed to provide a conducive surface for the host bone to osseointegrate with. The osseointegration process of the implant is difficult to clinically evaluate, leading to conservative postoperative rehabilitation approaches. Elastic stress waves generated in an OI prosthesis have been previously proposed to interrogate the implant-bone interface for quantitative assessment of the osseointegration process. This paper provides a detailed overview of the various elastic stress wave methods previously explored for in situ characterization of OI implants. Specifically, the paper explores the use of electromechanical impedance spectroscopy (EIS) to assess the OI process in compress-type OI prostheses. The EIS approach measures the electrical impedance spectrum of lead zirconate titanate elements bonded to the free end of the implant. The research utilizes both numerical simulation and experimental verification to establish that the electromechanical impedance spectrum is sensitive (between 400 and 460 kHz) to both the degree and location of osseointegration. A baseline-free OI index is proposed to quantify the degree of osseointegration at the implant-bone interface and to assess the stability of the OI implant for clinical decision making.
RESUMO
Wireless monitoring has emerged in recent years as a promising technology that could greatly impact the field of structural monitoring and infrastructure asset management. This paper is a summary of research efforts that have resulted in the design of numerous wireless sensing unit prototypes explicitly intended for implementation in civil structures. Wireless sensing units integrate wireless communications and mobile computing with sensors to deliver a relatively inexpensive sensor platform. A key design feature of wireless sensing units is the collocation of computational power and sensors; the tight integration of computing with a wireless sensing unit provides sensors with the opportunity to self-interrogate measurement data. In particular, there is strong interest in using wireless sensing units to build structural health monitoring systems that interrogate structural data for signs of damage. After the hardware and the software designs of wireless sensing units are completed, the Alamosa Canyon Bridge in New Mexico is utilized to validate their accuracy and reliability. To improve the ability of low-cost wireless sensing units to detect the onset of structural damage, the wireless sensing unit paradigm is extended to include the capability to command actuators and active sensors.