Quantifying the Effects of Knee Joint Biomechanics on Acoustical Emissions

The knee is one of the most injured body parts, causing 18 million patients to be seen in clinics every year. Because the knee is a weight-bearing joint, it is prone to pathologies such as osteoarthritis and ligamentous injuries. Existing technologies for monitoring knee health can provide accurate assessment and diagnosis for acute injuries. However, they are mainly confined to clinical or laboratory settings only, time-consuming, expensive, and not well-suited for longitudinal monitoring. Developing a novel technology for joint health assessment beyond the clinic can further provide insights on the rehabilitation process and quantitative usage of the knee joint. To better understand the underlying properties and fundamentals of joint sounds, this research will investigate the relationship between the changes in the knee joint structure (i.e. structural damage and joint contact force) and the JAEs while developing novel techniques for analyzing these sounds. We envision that the possibility of quantifying joint structure and joint load usage from these acoustic sensors would advance the potential of JAE as the next biomarker of joint health that can be captured with wearable technology. First, we developed a novel processing technique for JAEs that quantify on the structural change of the knee from injured athletes and human lower-limb cadaver models. Second, we quantified whether JAEs can detect the increase in the mechanical stress on the knee joint using an unsupervised graph mining algorithm. Lastly, we quantified the directional bias of the load distribution between medial and lateral compartment using JAEs. Understanding and monitoring the quantitative usage of knee loads in daily activities can broaden the implications for longitudinal joint health monitoring.Ph.D

Body sensor networks: smart monitoring solutions after reconstructive surgery

Advances in reconstructive surgery are providing treatment options in the face of major trauma and cancer. Body Sensor Networks (BSN) have the potential to offer smart solutions to a range of clinical challenges. The aim of this thesis was to review the current state of the art devices, then develop and apply bespoke technologies developed by the Hamlyn Centre BSN engineering team supported by the EPSRC ESPRIT programme to deliver post-operative monitoring options for patients undergoing reconstructive surgery. A wireless optical sensor was developed to provide a continuous monitoring solution for free tissue transplants (free flaps). By recording backscattered light from 2 different source wavelengths, we were able to estimate the oxygenation of the superficial microvasculature. In a custom-made upper limb pressure cuff model, forearm deoxygenation measured by our sensor and gold standard equipment showed strong correlations, with incremental reductions in response to increased cuff inflation durations. Such a device might allow early detection of flap failure, optimising the likelihood of flap salvage. An ear-worn activity recognition sensor was utilised to provide a platform capable of facilitating objective assessment of functional mobility. This work evolved from an initial feasibility study in a knee replacement cohort, to a larger clinical trial designed to establish a novel mobility score in patients recovering from open tibial fractures (OTF). The Hamlyn Mobility Score (HMS) assesses mobility over 3 activities of daily living: walking, stair climbing, and standing from a chair. Sensor-derived parameters including variation in both temporal and force aspects of gait were validated to measure differences in performance in line with fracture severity, which also matched questionnaire-based assessments. Monitoring the OTF cohort over 12 months with the HMS allowed functional recovery to be profiled in great detail. Further, a novel finding of continued improvements in walking quality after a plateau in walking quantity was demonstrated objectively. The methods described in this thesis provide an opportunity to revamp the recovery paradigm through continuous, objective patient monitoring along with self-directed, personalised rehabilitation strategies, which has the potential to improve both the quality and cost-effectiveness of reconstructive surgery services.Open Acces

A pervasive body sensor network for monitoring post-operative recovery

Over the past decade, miniaturisation and cost reduction brought about by the semiconductor industry has led to computers smaller in size than a pin head, powerful enough to carry out the processing required, and affordable enough to be disposable. Similar technological advances in wireless communication, sensor design, and energy storage have resulted in the development of wireless “Body Sensor Network (BSN) platforms comprising of tiny integrated micro sensors with onboard processing and wireless data transfer capability, offering the prospect of pervasive and continuous home health monitoring. In surgery, the reduced trauma of minimally invasive interventions combined with initiatives to reduce length of hospital stay and a socioeconomic drive to reduce hospitalisation costs, have all resulted in a trend towards earlier discharge from hospital. There is now a real need for objective, pervasive, and continuous post-operative home recovery monitoring systems. Surgical recovery is a multi-faceted and dynamic process involving biological, physiological, functional, and psychological components. Functional recovery (physical independence, activities of daily living, and mobility) is recognised as a good global indicator of a patient’s post-operative course, but has traditionally been difficult to objectively quantify. This thesis outlines the development of a pervasive wireless BSN system to objectively monitor the functional recovery of post-operative patients at home. Biomechanical markers were identified as surrogate measures for activities of daily living and mobility impairment, and an ear-worn activity recognition (e-AR) sensor containing a three-axis accelerometer and a pulse oximeter was used to collect this data. A simulated home environment was created to test a Bayesian classifier framework with multivariate Gaussians to model activity classes. A real-time activity index was used to provide information on the intensity of activity being performed. Mobility impairment was simulated with bracing systems and a multiresolution wavelet analysis and margin-based feature selection framework was used to detect impaired mobility. The e-AR sensor was tested in a home environment before its clinical use in monitoring post-operative home recovery of real patients who have undergone surgery. Such a system may eventually form part of an objective pervasive home recovery monitoring system tailored to the needs of today’s post-operative patient.Open acces

Novel Multimodal Sensing Systems for Wearable Knee Health Assessment

Wearable technologies for healthcare represent a popular research area, as they can provide quantitative metrics during rehabilitation, enable long-term, at-home monitoring of chronic conditions, and facilitate preventative—versus reactive—medical interventions. Moreover, their low cost makes them accessible to broad subject populations and enables more frequent measures of biomarkers. Such technologies are particularly useful for areas of medicine where the diagnostic or evaluation tools are expensive, not readily available, or time consuming. Orthopedics, in particular joint health assessment, is an area where wearable devices may provide clinicians and patients with more readily available quantitative data. The objective of this research is to investigate wearable, multimodal sensing technologies to facilitate joint health and rehabilitation monitoring, ultimately providing a “joint health score” based on evaluation of joint acoustics, electrical bioimpedance, inertial measures, and temperature data. This joint health score may be employed in various applications—including during rehabilitation after an acute injury and management of joint diseases, such as arthritis—providing an actionable metric for physicians based on the underlying physiological changes of the joint itself. This work specifically investigates the hardware for such a system. First, we examined microphones suited for wearable applications (e.g., miniature, inexpensive) that still provide robust measurements in terms of signal quality and consistency for repeated measurements. Second, we implemented a microcontroller-based system to sample high-throughput audio data as well as lower-rate electrical bioimpedance, inertial, and temperature data, which was incorporated into a fully untethered “brace.” Importantly, this work provides the fundamental hardware system for wearable knee joint health assessment.Ph.D

MEMS Technology for Biomedical Imaging Applications

Biomedical imaging is the key technique and process to create informative images of the human body or other organic structures for clinical purposes or medical science. Micro-electro-mechanical systems (MEMS) technology has demonstrated enormous potential in biomedical imaging applications due to its outstanding advantages of, for instance, miniaturization, high speed, higher resolution, and convenience of batch fabrication. There are many advancements and breakthroughs developing in the academic community, and there are a few challenges raised accordingly upon the designs, structures, fabrication, integration, and applications of MEMS for all kinds of biomedical imaging. This Special Issue aims to collate and showcase research papers, short commutations, perspectives, and insightful review articles from esteemed colleagues that demonstrate: (1) original works on the topic of MEMS components or devices based on various kinds of mechanisms for biomedical imaging; and (2) new developments and potentials of applying MEMS technology of any kind in biomedical imaging. The objective of this special session is to provide insightful information regarding the technological advancements for the researchers in the community

A NOVEL MULTI-MODAL, WEARABLE SENSING SYSTEM TO AUTOMATICALLY QUANTIFY CHANGES IN EXTRAVASCULAR FLUID LEVELS

The buildup of static edematous fluids (swelling) in the tissue is indicative of a serious medical condition that can lead to long-term tissue damage, reduction in mobility and in some cases loss of limb. This swelling can be due to internal factors such as an immunoresponse to injuries or infections, or external factors such as a leakage of infused intravenous medication to the surrounding tissue (i.e., IV infiltration or extravasation). Detecting and tracking changes in a tissue’s extracellular fluid content is crucial in diagnosing the severity of the injury and/or infection, and thereby preventing irreversible tissue damage. However, current methods for quantifying fluid levels in the extravascular space are either (1) manual and subjective, relying heavily on the medical staff’s expertise, or (2) costly and timely, such as X-rays or magnetic resonance imaging (MRI). In this dissertation, I present non-invasive wearable technologies that utilize localized bioimpedance contextualized by the tissue’s kinematics to robustly quantify changes in the biological tissue’s extracellular fluid content. Towards this goal, several robust and miniaturized systems are designed and implemented by researching different integrated circuits, analog front ends, and novel physiology-driven calibration techniques that together increase the system’s accuracy and reduce its size and power consumption. Next, novel methods and algorithms are developed to allow for unobtrusive real-time detection of changes in extracellular fluid content. The systems, methods and algorithms were validated in human subjects studies, animal models and cadaver models for ankle edema tracking, and in human subjects studies and animal tissue for intravenous infiltration detection.Ph.D

Physical and chemical sensing applications of polypyrrole-coated foams

We live in a world of information, and emerging technologies, which compel us to look for new ways to collect, process, and distribute information. Today we are faced with an information overload problem as users struggle to locate the right information in the right way at the right time. In my view this is an “overload” of trivial information coupled with a gap in access to important information. Digitization of information and communications has seen the rise and rise of computers to a now ubiquitous position in our society. However, the problem remains as to how to merge the digital world with sensing, and respond to changes in the real world. Ubiquitous information systems are needed that will automatically sense and importantly, respond to changes in their environment and usage in order to deliver a more intelligent, proactive and personalized information service. These systems may be wearable, enabling them to disappear into our personal space, enhancing rather than burdening our daily activities. Conventional sensors are generally unsuitable for wearable body monitoring devices either due to their physical structure or their functional requirements. This thesis examines this area of wearable sensors, detailing the development and characterisation of novel sensing materials and outlines their performance in various on-body monitoring applications

A Reticulation of Skin-Applied Strain Sensors for Motion Capture

The purpose of this research is to develop a system of motion capture based on skin-applied strain sensors. These elastic sensors are of interest because they can be applied to the body without restricting motion and are well suited to operate in more practical environments, such as sports fields, gymnasiums, and outdoor areas. This combination is currently not available in the field of motion capture. The current issues with strain sensor motion capture technology is the accurate is not sufficient for motion analysis and axial rotation monitoring of joints is not available. This project will build and test a sensor arrangement designed to measure axial joint rotation and a calibration that compensates for crosstalk from other joint motions. An arrangement of four strain sensors was created to capture hip and knee motion indirectly through a geometric relationship. Sensors were arranged around the hip and knee with compression pants that emulate the pressure sensation of kinesiology tape. This pressure is desirable for high level athletes are comfortable with this feeling, meaning most wearers would likely agree. This prototype was tested on six participants of varying height with Institutional Review Board approval and was referenced against a passive marker, visual motion capture system with ten cameras. The test results show the geometric calibration with crosstalk compensation is the most successful general calibration. The overall root-mean-square error of the hip flexion, hip abduction, hip rotation, and knee flexion measurements were 4.6±1.2° (ρ = 0.95), 4.7±1.5° (ρ = 0.82), 6.7±2.0° (ρ = 0.89), and 6.2±1.3° (ρ = 0.96) respectively, compared to a commercial xSens system with 5.7±2.1° (ρ = 0.99), 4.1±2.0° (ρ = 0.91), 6.5±2.8° (ρ = 0.68), and 4.4±2.0° (ρ = 0.99). The geometric calibration without crosstalk compensation tends to miss the relation of the data but may be sufficient for small ranges of motion. Specifically, axial rotation sensing capacity was shown to be important for the accuracy of other sensor’s angle readings. The gaussian processes regression (GPR) tended to overfit the calibration data. In conclusion, the geometric calibration with crosstalk compensation created the most successful, stable, and general calibration. This testing was performed with a $200 prototype and produced results comparable to a$20,000 commercial system