Biomechanical Gait Variable Estimation Using Wearable Sensors after Unilateral Total Knee Arthroplasty

Total knee arthroplasty is a common surgical treatment for end-stage osteoarthritis of the knee. The majority of existing studies that have explored the relationship between recovery and gait biomechanics have been conducted in laboratory settings. However, seamless gait parameter monitoring in real-world conditions may provide a better understanding of recovery post-surgery. The purpose of this study was to estimate kinematic and kinetic gait variables using two ankle-worn wearable sensors in individuals after unilateral total knee arthroplasty. Eighteen subjects at least six months post-unilateral total knee arthroplasty participated in this study. Four biomechanical gait variables were measured using an instrumented split-belt treadmill and motion capture systems. Concurrently, eleven inertial gait variables were extracted from two ankle-worn accelerometers. Subsets of the inertial gait variables for each biomechanical gait variable estimation were statistically selected. Then, hierarchical regressions were created to determine the directional contributions of the inertial gait variables for biomechanical gait variable estimations. Selected inertial gait variables significantly predicted trial-averaged biomechanical gait variables. Moreover, strong directionally-aligned relationships were observed. Wearable-based gait monitoring of multiple and sequential kinetic gait variables in daily life could provide a more accurate understanding of the relationships between movement patterns and recovery from total knee arthroplasty

KNEE JOINT LOADING FOLLOWING ANTERIOR CRUCIATE LIGAMENT RECONSTRUCTION: LINK TO PATIENT REPORTED OUTCOMES AND A NOVEL METHOD TO MONITOR WITH WEARABLE SENSORS

Recovery from anterior cruciate ligament reconstruction (ACLR) commonly results in undesirable physical and patient-reported outcomes (PROs). Identification of modifiable factors such as knee contact force (KCF) early in rehabilitation that can improve these outcomes is important due to the rapid decrease in function, quality of life, and joint health in this population. Additionally, if noninvasive measurement of KCFs outside of a traditional laboratory were possible, clinicians could optimize patient treatment with personalized care. Therefore, there are two primary aims to this thesis: 1) quantify the link between KCF and PROs which measure pain, ability to perform activities of daily living, and quality of life 6 months after ACLR; and 2) develop a novel method to monitor KCF outside the laboratory using unobtrusive wearable sensors. To address the first aim, eighty subjects were enrolled six months following ACLR. Patient-reported quality of life, ability to perform activities of daily living, and pain were evaluated with the KOOS QOL, ADL, and Pain subscales, respectively. A musculoskeletal model was utilized to estimate peak KCF. Subjects with scores above the patient acceptable symptom state (PASS) threshold for the KOOS QOL and ADL demonstrated greater ACLR limb peak KCF (p = 0.001 and p = 0.017, respectively), which was not found with KOOS Pain-dichotomized groups (p = 0.079). To address the second aim, nine healthy subjects walked at a wide range of speeds on an instrumented treadmill. Thirteen insole force features were calculated as potential predictors of peak KCF and KCF impulse per step, estimated with musculoskeletal modeling. Prediction error was calculated as 10-fold cross validated median symmetric accuracy. Pearson product-moment correlation coefficients defined the relationship between variable pairs. Models developed per-limb demonstrated lower prediction error (KCF impulse: 2.19%; peak KCF: 3.50%) than those developed per-subject (KCF impulse: 3.40%; peak KCF: 6.47%). A number of insole features were associated with peak KCF (7 strong, 4 moderate), but not KCF impulse (all negligible). The findings from the first aim demonstrate that subjects with poor quality of life or ability to complete everyday activities underload their knee, possibly accelerating their path to osteoarthritis development. The findings from the second aim suggest that KCFs can be monitored with force-sensing insoles

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

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

Technological advancements in the analysis of human motion and posture management through digital devices

Technological development of motion and posture analyses is rapidly progressing, especially in rehabilitation settings and sport biomechanics. Consequently, clear discrimination among different measurement systems is required to diversify their use as needed. This review aims to resume the currently used motion and posture analysis systems, clarify and suggest the appropriate approaches suitable for specific cases or contexts. The currently gold standard systems of motion analysis, widely used in clinical settings, present several limitations related to marker placement or long procedure time. Fully automated and markerless systems are overcoming these drawbacks for conducting biomechanical studies, especially outside laboratories. Similarly, new posture analysis techniques are emerging, often driven by the need for fast and non-invasive methods to obtain high-precision results. These new technologies have also become effective for children or adolescents with non-specific back pain and postural insufficiencies. The evolutions of these methods aim to standardize measurements and provide manageable tools in clinical practice for the early diagnosis of musculoskeletal pathologies and to monitor daily improvements of each patient. Herein, these devices and their uses are described, providing researchers, clinicians, orthopedics, physical therapists, and sports coaches an effective guide to use new technologies in their practice as instruments of diagnosis, therapy, and prevention

Towards Remote Gait Analysis: Combining Physics and Probabilistic Models for Estimating Human Joint Mechanics

The connected health movement and remote patient monitoring promise to revolutionize patient care in multiple clinical contexts. In orthopedics, continuous monitoring of human joint and muscle tissue loading in free-living conditions will enable novel insight concerning musculoskeletal disease etiology. These developments are necessary for comprehensive patient characterization, progression monitoring, and personalized therapy. This vision has motivated many recent advances in wearable sensor-based algorithm development that aim to perform biomechanical analyses traditionally restricted to confined laboratory spaces. However, these techniques have not translated to practical deployment for remote monitoring. Several barriers to translation have been identified including complex sensor arrays. Thus, the aim of this work was to lay the foundation for remote gait analysis and techniques for estimating clinically relevant biomechanics with a reduced sensor array. The first step in this process was to develop an open-source platform that generalized the processing pipeline for automated remote biomechanical analysis. The clinical utility of the platform was demonstrated for monitoring patient gait following knee surgery using continuous recordings of thighworn accelerometer data and rectus femoris electromyograms (EMG) during free-living conditions. Individual walking bouts were identified from which strides were extracted and characterized for patient evaluation. A novel, multifactorial asymmetry index was proposed based on temporal, EMG, and kinematic descriptors of gait that was able to differentiate between patients at different stages of recovery and that was more sensitive to recovery time than were indices of cumulative physical activity. The remainder of the work focused on algorithms for estimating joint moment and simulating muscle contraction dynamics using a reduced sensor array. A hybrid technique was proposed that combined both physics and probabilistic models in a complementary fashion. Specifically, the notion of a muscle synergy function was introduced that describes the mapping between excitations from a subset of muscles and excitations from other synergistic muscles. A novel model of these synergy functions was developed that enabled estimation of unmeasured muscle excitations using a measured subset. Data from thigh- and shank-worn inertial sensors were used to estimate segment kinematics and muscle-tendon unit (MTU) lengths using physics-based techniques and a model of the musculoskeletal geometry. These estimates of muscle excitation and MTU length were used as inputs for EMG-driven simulation of muscle contraction. Estimates of muscle force, power, and work as well as net joint moment from the proposed hybrid technique were compared to estimates from laboratory-based techniques. This presents the first sensor-only (four EMG and two inertial sensors) simulation of muscle contraction dynamics and joint moment estimation using machine learning only for estimating unmeasured muscle excitations. This work provides the basis for automated remote biomechanical analysis with reduced sensor arrays; from raw sensor recordings to estimates of muscle moment, force, and power. The proposed hybrid technique requires data from only four EMG and two inertial sensors and work has begun to seamlessly integrate these sensors into a knee brace for monitoring patients following knee surgery. Future work should build on these developments including further validation and design of methods utilizing remotely and longitudinally observed biomechanics for prognosis and optimizing patient-specific interventions

Proceedings XXI Congresso SIAMOC 2021

XXI Congresso Annuale della SIAMOC, modalità telematica il 30 settembre e il 1° ottobre 2021. Come da tradizione, il congresso vuole essere un’occasione di arricchimento e mutuo scambio, dal punto di vista scientifico e umano. Verranno toccati i temi classici dell’analisi del movimento, come lo sviluppo e l’applicazione di metodi per lo studio del movimento nel contesto clinico, e temi invece estremamente attuali, come la teleriabilitazione e il telemonitoraggio

The use of biofeedback for gait retraining: A mapping review

Background: Biofeedback seems to be a promising tool to improve gait outcomes for both healthy individuals and patient groups. However, due to differences in study designs and outcome measurements, it remains uncertain how different forms of feedback affect gait outcomes. Therefore, the aim of this study is to review primary biomechanical literature which has used biofeedback to alter gait-related outcomes in human participants. Methods: Medline, Cinahl, Cochrane, SPORTDiscus and Pubmed were searched from inception to December 2017 using various keywords and the following MeSHterms: biofeedback, feedback, gait, walking and running. From the included studies, sixteen different study characteristics were extracted. Findings: In this mapping review 173 studies were included. The most common feedback mode used was visual feedback (42%, n=73) and the majority fed-back kinematic parameters (36%, n=62). The design of the studies were poor: only 8% (n=13) of the studies had both a control group and a retention test; 69% (n=120) of the studies had neither. A retention test after 6 months was performed in 3% (n=5) of the studies, feedback was faded in 9% (n=15) and feedback was given in the field rather than the laboratory in 4% (n=8) of the studies. Interpretation: Further work on biofeedback and gait should focus on the direct comparison between different modes of feedback or feedback parameters, along with better designed and field based studies

Moving out of the lab:movement analyses in patients with osteoarthritis of the knee

Osteoarthritis of the knee is one of the main causes of physical limitations. In addition to osteoarthritis, obesity is also a growing public health problem. Research has shown that obese people are almost four times as likely to develop osteoarthritis of the knee. Patients with osteoarthritis of the knee develop compensation mechanisms during daily activities. This dissertation focuses on the analysis of biomechanical components in patients with osteoarthritis of the knee. The focus was on the knee adduction moment (KAM) during walking, stair climbing and sit-to-stand. A high KAM is associated with the onset and progression of osteoarthritis of the knee. Furthermore, this study focused on physical activity in patients with osteoarthritis of the knee with and without obesity. In this way, this research wanted to gain more insight into small changes in movement behaviour in these patients. Accelerometery is a good way to understand quantity and quality of physical activity. Patients with both osteoarthritis of the knee and obesity have a significantly increased KAM compared to healthy subjects. However, presence of only osteoarthritis of the knee, does not result in an increased KAM. Furthermore, more insight was gained into the actual physical activity and limitations in daily life in patients with osteoarthritis of the knee

Patient Movement Monitoring Based on IMU and Deep Learning

Osteoarthritis (OA) is the leading cause of disability among the aging population in the United States and is frequently treated by replacing deteriorated joints with metal and plastic components. Developing better quantitative measures of movement quality to track patients longitudinally in their own homes would enable personalized treatment plans and hasten the advancement of promising new interventions. Wearable sensors and machine learning used to quantify patient movement could revolutionize the diagnosis and treatment of movement disorders. The purpose of this dissertation was to overcome technical challenges associated with the use of wearable sensors, specifically Inertial Measurement Units (IMUs), as a diagnostic tool for osteoarthritic (OA) and total knee replacement patients (TKR) through a detailed biomechanical assessment and development of machine learning algorithms. Specifically, the first study developed a relevant dataset consisting of IMU and associated biomechanical parameters of OA and TKR patients performing various activities, created a machine learning-based framework to accurately estimate spatiotemporal movement characteristics from IMU during level ground walking, and defined optimum sensor configuration associated with the patient population and activity. The second study designed a framework to generate synthetic kinematic and associated IMU data as well as investigated the influence of adding synthetic data into training-measured data on deep learning model performance. The third study investigated the kinematic variation between two patient’s population across various activities: stair ascent, stair descent, and gait using principle component analysis PCA. Additionally, PCA-based autoencoders were developed to generate synthetic kinematics data for each patient population and activity. The fourth study investigated the potential use of a universal deep learning model for the estimation of lower extremities’ kinematics across various activities. Therefore, this model can be used as a global model for transfer learning methods in future research. This line of study resulted in a machine-learning framework that can be used to estimate biomechanical movements based on a stream of signals emitted from low-cost and portable IMUs. Eventually, this could lead to a simple clinical tool for tracking patients\u27 movements in their own homes and translating those movements into diagnostic metrics that clinicians will be able to use to tailor treatment to each patient\u27s needs in the future