Wearables for independent living in older adults: Gait and falls

Solutions are needed to satisfy care demands of older adults to live independently. Wearable technology (wearables) is one approach that offers a viable means for ubiquitous, sustainable and scalable monitoring of the health of older adults in habitual free-living environments. Gait has been presented as a relevant (bio)marker in ageing and pathological studies, with objective assessment achievable by inertial-based wearables. Commercial wearables have struggled to provide accurate analytics and have been limited by non-clinically oriented gait outcomes. Moreover, some research-grade wearables also fail to provide transparent functionality due to limitations in proprietary software. Innovation within this field is often sporadic, with large heterogeneity of wearable types and algorithms for gait outcomes leading to a lack of pragmatic use. This review provides a summary of the recent literature on gait assessment through the use of wearables, focusing on the need for an algorithm fusion approach to measurement, culminating in the ability to better detect and classify falls. A brief presentation of wearables in one pathological group is presented, identifying appropriate work for researchers in other cohorts to utilise. Suggestions for how this domain needs to progress are also summarised

An Instrumented Walking-Aid to Assess and Retrain Gait

An instrumented walking-aid, the iWA system, has been developed to measure kinematic and kinetic properties of walking aid (WA) use and deliver feedback to improve gait. The clinical requirements, technical specification and design of the system are developed through clinical collaboration. The development of the system is described, including hardware components and data analysis used to process the measured data for assessment. The system measurements are validated under controlled laboratory conditions. The iWA system is evaluated in a typical UK clinical environment by a participant in a rehabilitation session. The resultant data successfully capture the quality of the participant’s walking aid use and agree with clinical opinion, supporting the efficacy of this approach

Intelligent Biosignal Processing in Wearable and Implantable Sensors

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine

Polymeric Microsensors for Intraoperative Contact Pressure Measurement

Biocompatible sensors have been demonstrated using traditional microfabrication techniques modified for polymer substrates and utilize only materials suitable for implantation or bodily contact. Sensor arrays for the measurement of the load condition of polyethylene spacers in the total knee arthroplasty (TKA) prosthesis have been developed. Arrays of capacitive sensors are used to determine the three-dimensional strain within the polyethylene prosthesis component. Data from these sensors can be used to give researchers a better understanding of component motion, loading, and wear phenomena for a large range of activities. This dissertation demonstrates both analytically and experimentally the fabrication of these sensor arrays using biocompatible polymer substrates and dielectrics while preserving industry-standard microfabrication processing for micron-level resolution. An array of sensors for real-time measurement of pressure profiles is the long-term goal of this research. A custom design using capacitive-based sensors is an excellent selection for such measurement, giving high spatial resolution across the sensing surface and high load resolution for pressures applied normal to that surface while operating at low power

Validation of an Accelerometry Based Method of Human Gait Analysis

Gait analysis is the quantification of locomotion. Understanding the science behind the way we move is of interest to a wide variety of fields. Medical professionals might use gait analysis to track the rehabilitation progress of a patient. An engineer may want to design wearable robotics to augment a human operator. Use cases even extend into the sport and entertainment industries. Typically, a gait analysis is performed in a highly specialized laboratory containing cumbersome expensive equipment. The process is tedious and requires specially trained operators. Continued development of small and cheap inertial measurement units (IMUs) over an alternative to current methods of gait analysis. These devices are portable and simple to use allowing gait analysis to be done outside the laboratory in real world environments. Unfortunately, while current IMU based gait analysis systems are able to quantify a subject\u27s joint kinematics they are unable to measure joint kinetics as could be done in a traditional gait laboratory. A novel musculoskeletal model-based movement analysis system using accelerometers has been developed that can calculate both joint kinematics and joint kinetics. The aim of this master\u27s thesis is to validate this accelerometer based gait analysis against the industry standard optical motion capture gait analysi

Shear-promoted drug encapsulation into red blood cells: a CFD model and μ-PIV analysis

The present work focuses on the main parameters that influence shear-promoted encapsulation of drugs into erythrocytes. A CFD model was built to investigate the fluid dynamics of a suspension of particles flowing in a commercial micro channel. Micro Particle Image Velocimetry (μ-PIV) allowed to take into account for the real properties of the red blood cell (RBC), thus having a deeper understanding of the process. Coupling these results with an analytical diffusion model, suitable working conditions were defined for different values of haematocrit

Systems for Noninvasive Assessment of Biomechanical Load in the Lower Limb

Every move you make—and, yes, every step you take—is the result of action at a joint, and so proper joint function is pivotal to the way we explore and interact with the world around us. Unfortunately, joint function is often disrupted by injuries, chronic disorders, or neurological deficits, which can, in turn, disrupt quality of life. Many forms of joint dysfunction derive from adverse biomechanical loading conditions—that is, the forces and torques to which our limbs are subjected—and, thus, techniques for monitoring these loads during daily life may improve our understanding of how injuries and disorders arise and progress—and, most importantly, how best to treat them. The standard methods for assessing these loading conditions, however, are almost all benchtop-bound and confined to laboratories or clinics, so their utility in at-home or ambulatory settings—where they may be most impactful—is limited. In an attempt to address this void, in this work, we present three novel techniques for extracting information related to joint loading using a synthesis of noninvasive / wearable sensing and machine learning. First, we detail the development of an adjustable-stiffness ankle exoskeleton with multimodal sensing capabilities and use it to explore how humans interact with external elastic loading of the ankle during walking. Then, in an attempt to peer “under the skin,” we develop a novel form-factor for capturing joint sounds— the skin-surface vibrations produced by articulating structures internal to the joint—and demonstrate that these noninvasive measurements can be used to discriminate levels of axial loading at the knee. Finally, taking the concept of joint acoustics one step further, we introduce a new, active acoustics-based technique whereby the tensile loading of a particular tissue—the Achilles tendon—can be estimated by measuring the tissue’s mechanical response to a burst vibration on the skin surface. Using this approach, we are able to assess this loading state (and, by association, the net moment at the ankle) reliably across several activities of daily life, and, through a proof-of-concept study, we demonstrate how the technique can effectively translate to a fully wearable device. Collectively, the efforts reported in this thesis represent a novel, multi-path approach to assessing biomechanical loading states in the lower limb and the effects thereof. These tools and insights may serve as a basis for future development of wearable, accessible technologies for monitoring joint load during daily life, thereby reducing injury risk, tracking disease progress, assessing the efficacy of treatment, and accelerating recovery.Ph.D