Symptomatic knee osteoarthritis (OA) is a painful condition that reduces mobility, independence, and quality of life for both men and women. About 10% of people aged over 55 years have painful disabling knee OA of whom one quarter are severely disabled. Although a variety of factors influence the pathogenesis of OA, medial compartment knee joint contact loading plays a significant role and importantly, is a modifiable risk factor. Traditional knee unloader braces are a common conservative treatment strategy for those with medial compartment knee OA. Unloader braces are designed to unload the medial compartment of the knee joint thereby slowing disease progression, reducing pain, and improving joint function and mobility. Medial compartment unloading provided by a brace abduction moment (BAM) counteracts the external knee adduction moment (KAM). Unfortunately, traditional knee unloader braces have mixed efficacy and the limited benefits (minimal reduction in pain and minor functional improvements) of wearing the brace do not often outweigh the brace discomfort and hassle of use. One of the shortcomings of traditional knee unloader braces is that the BAM must be manually set by the brace user through strapping and or condyle pad adjustment. This results in infrequent adjustment which often causes an inappropriate BAM setting for a given activity, e.g. strapping has loosened resulting in a BAM that doesn?t provide enough knee unloading during gait, or the subject is at rest but the BAM remains high causing pressure and discomfort at the brace interface. Even when walking BAM modulation within a step could be beneficial. In this scenario, BAM could be reduced during swing phase but then return to a normal level during stance phase. For these reasons, we propose that a new class of smart active braces that dynamically adapt the amount of unloading for a given individual and their activity. This new class of braces has the potential to provide greater medial compartment knee unloading and better pain relief while simultaneously being more comfortable. Therefore, the purpose of this research study is to determine optimal BAM modulation profiles that provide better medial compartment knee unloading, improve brace comfort and reduce knee pain as compared to conventional passive knee unloader braces. To achieve our aims, we have developed an offboard robotic knee exoskeleton (RKE) to serve as a laboratory tool to investigate the potential benefits of active braces with dynamic BAM modulation. For this study, twenty participants will be recruited to walk on an instrumented treadmill while wearing the (RKE). The participants will experience 56 different BAM modulation schemes as parameterized by the BAM turn on time (Ton), turn off time (Toff) and peak Nm BAM level (Pk). Real-time data analysis will allow us to immediately compare the efficacy of each BAM modulation scheme in terms of medial compartment knee unloading (knee adduction angular impulse, KAAI) and brace comfort (brace abduction angular impulse, BAAI). Study participants will then experience a direct comparison of walking with the RKE emulating a traditional passive unloader brace versus walking with the RKE programmed with the identified optimal BAM. Here the participants will self-select their own preferred unloading level (BAM Pk) for each brace type. Self-reported knee pain and brace comfort, as well as KAAI and BAAI of the passive versus active brace will then be compared. These results will allow us to determine if dynamic active braces that deliver intra-step BAM modulation can provide equal or better medial compartment knee unloading (KAAI) while simultaneously being more comfortable and greater pain relief. The development of improved knee unloader braces, that are smart, dynamic, and adaptive have the potential to greatly increase brace efficacy. Improved conservative treatment strategies for medial compartment knee OA could reduce surgical intervention and its associated risks and significantly improve the quality of life for veterans burdened by this disease.
In the United States an estimated 9 million adults have symptomatic knee Osteoarthritis (OA), which can cause pain and reduce one?s quality of life. OA significantly affects our aging veteran population. Knee unloader braces are often prescribed to knee OA patients; however their efficacy is limited and they are often abandoned. Since their invention over 50 years ago, knee unloader braces have not changed much yet at the same time we have seen a revolution in robotics, sensing, actuation, machine learning, and control. The purpose of this proposal is to create a new class of robotic knee exoskeletons that utilized these 21st century advances to improve the efficacy, comfort, and performance of knee unloader braces. Veterans are expected to greatly benefit from the proposed study, which will yield key knowledge needed to advance a new generation of knee unloader braces that slow disease progression.
