Design of a multi-sensors wearable platform for remote monitoring of knee rehabilitation

Smart wearables are a promising tool for the objective and quantifiable monitoring of patients' capabilities during remote at-home assessments. A novel platform for the remote assessment of patients undergoing knee rehabilitation has been presented in this paper, SKYRE. The challenges associated with the design of the SKYRE platform are described. The platform consists of a multi-sensor wearable garment and an associated ICT architecture, with the aim of capturing real-time objective assessment of physical rehabilitation exercises and support clinicians in their decision-making process as well as provide guidance to the end-users so as to increase their awareness and compliance. The overall system architecture is defined based on usersâ requirements and industrial design, and both hardware and software platforms have been thoroughly discussed in detail, including electronic design, textile integration, prototyping process, and firmware development, as well as the mobile application and web portal implementation. Multiple sensing technologies are adopted, including motion capture, electromyography measurements, and muscle electro-stimulation. The developed system, SKYRE, meets the end-usersâ requirements, and the validation shows that the system presents results comparable to gold-standard technologies. SKYRE therefore might represent a valid alternative for patients and clinicians willing to perform a remote objective assessment of the rehabilitation process following knee surgery

Wearables for Movement Analysis in Healthcare

Quantitative movement analysis is widely used in clinical practice and research to investigate movement disorders objectively and in a complete way. Conventionally, body segment kinematic and kinetic parameters are measured in gait laboratories using marker-based optoelectronic systems, force plates, and electromyographic systems. Although movement analyses are considered accurate, the availability of specific laboratories, high costs, and dependency on trained users sometimes limit its use in clinical practice. A variety of compact wearable sensors are available today and have allowed researchers and clinicians to pursue applications in which individuals are monitored in their homes and in community settings within different fields of study, such movement analysis. Wearable sensors may thus contribute to the implementation of quantitative movement analyses even during out-patient use to reduce evaluation times and to provide objective, quantifiable data on the patients’ capabilities, unobtrusively and continuously, for clinical purposes

A multi-sensors wearable system for remote assessment of physiotherapy exercises during ACL rehabilitation

In this paper, the challenges associated with the design of a novel multi-sensor wearable system for the objective assessment of exercises during lower-limbs rehabilitation are described. The overall system architecture is defined, and finally both the implemented hardware and software platforms are illustrated in detail. Multiple sensing technologies are adopted including motion data, electromyography measurements, and muscle electro-stimulation. The software stack provides guidance to the users throughout the rehabilitation therapy sessions, and allows clinicians to access the data collected remotely in real-time thus supporting their clinical evaluation. Finally, preliminary results of the comparison between the knee joint angle estimated by the developed system against a gold-standard inertial-based system are provided showing promising results for future validation

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

Wearable inertial sensors for human movement analysis

Introduction: The present review aims to provide an overview of the most common uses of wearable inertial sensors in the field of clinical human movement analysis.Areas covered: Six main areas of application are analysed: gait analysis, stabilometry, instrumented clinical tests, upper body mobility assessment, daily-life activity monitoring and tremor assessment. Each area is analyzed both from a methodological and applicative point of view. The focus on the methodological approaches is meant to provide an idea of the computational complexity behind a variable/parameter/index of interest so that the reader is aware of the reliability of the approach. The focus on the application is meant to provide a practical guide for advising clinicians on how inertial sensors can help them in their clinical practice.Expert commentary: Less expensive and more easy to use than other systems used in human movement analysis, wearable sensors have evolved to the point that they can be considered ready for being part of routine clinical routine

Evaluating footwear “in the wild”: Examining wrap and lace trail shoe closures during trail running

Trail running participation has grown over the last two decades. As a result, there have been an increasing number of studies examining the sport. Despite these increases, there is a lack of understanding regarding the effects of footwear on trail running biomechanics in ecologically valid conditions. The purpose of our study was to evaluate how a Wrap vs. Lace closure (on the same shoe) impacts running biomechanics on a trail. Thirty subjects ran a trail loop in each shoe while wearing a global positioning system (GPS) watch, heart rate monitor, inertial measurement units (IMUs), and plantar pressure insoles. The Wrap closure reduced peak foot eversion velocity (measured via IMU), which has been associated with fit. The Wrap closure also increased heel contact area, which is also associated with fit. This increase may be associated with the subjective preference for the Wrap. Lastly, runners had a small but significant increase in running speed in the Wrap shoe with no differences in heart rate nor subjective exertion. In total, the Wrap closure fit better than the Lace closure on a variety of terrain. This study demonstrates the feasibility of detecting meaningful biomechanical differences between footwear features in the wild using statistical tools and study design. Evaluating footwear in ecologically valid environments often creates additional variance in the data. This variance should not be treated as noise; instead, it is critical to capture this additional variance and challenges of ecologically valid terrain if we hope to use biomechanics to impact the development of new products

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

Exploring the Application of Wearable Movement Sensors in People with Knee Osteoarthritis

People with knee osteoarthritis have difficulty with functional activities, such as walking or get into/out of a chair. This thesis explored the clinical relevance of biomechanics and how wearable sensor technology may be used to assess how people move when their clinician is unable to directly observe them, such as at home or work. The findings of this thesis suggest that artificial intelligence can be used to process data from sensors to provide clinically important information about how people perform troublesome activities

Comparison of knee loading during walking via musculoskeletal modelling using marker-based and IMU-based approaches

openThe current thesis is the result of the candidate's work over a six-month period with the assistance of the supervisor and co-supervisors, thanks to the collaboration between the Human Movement Bioengineering Laboratory Research group at the University of Padova (Italy) and the Human Movement Biomechanics Research group at KU Leuven (Belgium). Gait analysis, at a clinical level, is a diagnostic test with multiple potentials, in particular in identifying functional limitations related to a pathological path. Three-dimensional motion capture is now consolidated as an approach for human movement research studies and consists of a set of very precise measurements, the latter are processed by biomechanical models, and curves relating to the kinematics and indirect dynamics, i.e., the joint angles and the relative forces and moments, can be obtained. These results are considered fully reliable and based on these curves it is decided how to intervene on the specific subject to make the path as less pathological as possible. However, the use of wearable sensors (IMUs) consisting of accelerometers, gyroscopes, and magnetic sensors for gait analysis, has increased in the last decade due to the low production costs, portability, and small size that have allowed for studies in everyday life conditions. Inertial capture (InCap) systems have become an appealing alternative to 3D Motion Capture (MoCap) systems due to the ability of inertial measurement units (IMUs) to estimate the orientation of 3D sensors and segments. Musculoskeletal modelling and simulation provide the ideal framework to examine quantities in silico that cannot be measured in vivo, such as musculoskeletal loading, muscle forces and joint contact forces. The specific software used in this study is Opensim: an open-source software that allows modelling, analysis, and simulation of the musculoskeletal system. The aim of this thesis is to compare a marker-based musculoskeletal modelling approach with an IMUs-based one, in terms of kinematics, dynamics, and muscle activations. In particular, the project will focus on knee loading, using an existing musculoskeletal model of the lower limb. The current project was organized as follows: first, the results for the MoCap approach were obtained, following a specific workflow that used the COMAK IK tool and the COMAK algorithm to get the secondary knee kinematics, muscle activations, and knee contact forces. Where COMAK is a modified static optimization algorithm that solves for muscle activations and secondary kinematics to obtain measured primary DOF accelerations while minimizing muscle activation. Then these results were used to make a comparison with those obtained by the inertial-based approach, with the attempt to use as little information as possible from markers while estimating kinematics from IMU data using an OpenSim toolbox called OpenSense. Afterward, in order to promote an approach more independent from the constraints of a laboratory, the Zero Moment Point (ZMP) method was used to estimate the center of pressure position of the measured ground reaction forces (GRFs), and a specific Matlab code was implemented to improve this estimation. Using the measured GRFs with the new CoPs, the results of Inverse Dynamics, muscle activations, and finally knee loading were calculated and compared to the MoCap results. The final step was to conduct a statistical analysis to compare the two approaches and emphasize the importance of using IMUs for gait analysis, particularly to study knee mechanics