Publications by authors named "Dimitrios-Sokratis Komaris"

7 Publications

  • Page 1 of 1

Dynamic stability during stair negotiation after total knee arthroplasty.

Clin Biomech (Bristol, Avon) 2021 Jul 10;87:105410. Epub 2021 Jun 10.

Department of Biomedical Engineering, University of Strathclyde, Glasgow, Scotland, UK.

Background: The assessment of dynamic stability is crucial for the prevention of falls in the elderly and people with functional impairments. Evidence that total knee arthroplasty improves balance in patients with severe osteoarthritis is scarce and no information exists about how the surgery affects dynamic stability during stair negotiation.

Methods: This study aims to investigate if patients before and one year after surgery are less stable compared to asymptomatic controls. Seventeen control and twenty-seven patient participants with end-stage knee osteoarthritis that were scheduled to undergo unilateral total knee arthroplasty were recruited in this study. Participants' assessment was carried out by means of marker-based optical full-body motion capture with force platforms. The extrapolated Centre of mass and the margin of stability metrics were used to examine dynamic stability during stair ascent and descent.

Findings: Patient participants, during both pre-operative and post-operative assessments, were equally balanced to the asymptomatic controls during stair gait (p > .188). Additionally, the patients' overall stability did not improve significantly one year after arthroplasty surgery (p > .252).

Interpretation: Even if pain from arthritis and fear of falling is decreased following surgery, our results indicate that stability in stair walking in not affected by osteoarthritis and total knee arthroplasty.

Clinical Trial Registration Number: NCT02422251.
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http://dx.doi.org/10.1016/j.clinbiomech.2021.105410DOI Listing
July 2021

Identifying car ingress movement strategies before and after total knee replacement.

Int Biomech 2020 12;7(1):9-18

Department of Biomedical Engineering, University of Strathclyde , Glasgow, Scotland.

: Post-operative performance of knee bearings is typically assessed in activities of daily living by means of motion capture. Biomechanical studies predominantly explore common tasks such as walking, standing and stair climbing, while overlooking equally demanding activities such as embarking a vehicle. : The aim of this work is to evaluate changes in the movement habits of patients after total knee arthroplasty surgery in comparison to healthy age-matched control participants. : A mock-up car was fabricated based on the architecture of a common vehicle. Ten control participants and 10 patients with severe osteoarthritis of the knee attended a single- and three-motion capture session(s), respectively. Participants were asked to enter the car and sit comfortably adopting a driving position. Three trials per session were used for the identification of movement strategies by means of hierarchical clustering. Task completion time was also measured. : Patients' movement behaviour didn't change significantly following total knee arthroplasty surgery. Control participants favoured different movement strategies compared to patients post-operatively. Group membership, height and sidedness of the affected joint were found to be non-significant in task completion time. : This study describes an alternative movement identification technique for the analysis of the ingress movement that may be used to clinically assess knee bearings and aid in movement simulations and vehicle design.
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http://dx.doi.org/10.1080/23335432.2020.1716847DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8130714PMC
December 2020

Implant design affects walking and stair navigation after total knee arthroplasty: a double-blinded randomised controlled trial.

J Orthop Surg Res 2021 Mar 6;16(1):177. Epub 2021 Mar 6.

Department of Biomedical Engineering, University of Strathclyde, Glasgow, Scotland.

Background: Dissimilar total knee arthroplasty implant designs offer different functional characteristics. This is the first work in the literature to fully assess the Columbus ultra-congruent mobile (UCR) system with a rotating platform.

Methods: This is a double-blinded randomised controlled trial, comparing the functional performance of the low congruent fixed (CR DD), ultra-congruent fixed (UC) and UCR Columbus Total Knee Systems. The pre-operative and post-operative functional performance of twenty-four osteoarthritic patients was evaluated against nine control participants when carrying out everyday tasks. Spatiotemporal, kinematic and kinetic gait parameters in walking and stair navigation were extracted by means of motion capture.

Results: The UC implant provided better post-operative function, closely followed by the UCR design. However, both the UC and UCR groups exhibited restricted post-operative sagittal RoM (walking, 52.1 ± 4.4° and 53.2 ± 6.6°, respectively), whilst patients receiving a UCR implant did not show an improvement in their tibiofemoral axial rotation despite the bearing's mobile design (walking, CR DD 13.2 ± 4.6°, UC 15.3 ± 6.7°, UCR 13.5 ± 5.4°). Patients with a CR DD fixed bearing showed a statistically significant post-operative improvement in their sagittal RoM when walking (56.8 ± 4.6°).

Conclusion: It was concluded that both ultra-congruent designs in this study, the UC and UCR bearings, showed comparable functional performance and improvement after TKA surgery. The CR DD group showed the most prominent improvement in the sagittal RoM during walking.

Trial Registration: The study is registered under the clinical trial registration number: NCT02422251 . Registered on April 21, 2015.
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http://dx.doi.org/10.1186/s13018-021-02311-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7936496PMC
March 2021

Continuous home monitoring of Parkinson's disease using inertial sensors: A systematic review.

PLoS One 2021 4;16(2):e0246528. Epub 2021 Feb 4.

Tyndall National Institute, University College Cork, Cork, Ireland.

Parkinson's disease (PD) is a progressive neurological disorder of the central nervous system that deteriorates motor functions, while it is also accompanied by a large diversity of non-motor symptoms such as cognitive impairment and mood changes, hallucinations, and sleep disturbance. Parkinsonism is evaluated during clinical examinations and appropriate medical treatments are directed towards alleviating symptoms. Tri-axial accelerometers, gyroscopes, and magnetometers could be adopted to support clinicians in the decision-making process by objectively quantifying the patient's condition. In this context, at-home data collections aim to capture motor function during daily living and unobstructedly assess the patients' status and the disease's symptoms for prolonged time periods. This review aims to collate existing literature on PD monitoring using inertial sensors while it focuses on papers with at least one free-living data capture unsupervised either directly or via videotapes. Twenty-four papers were selected at the end of the process: fourteen investigated gait impairments, eight of which focused on walking, three on turning, two on falls, and one on physical activity; ten articles on the other hand examined symptoms, including bradykinesia, tremor, dyskinesia, and motor state fluctuations in the on/off phenomenon. In summary, inertial sensors are capable of gathering data over a long period of time and have the potential to facilitate the monitoring of people with Parkinson's, providing relevant information about their motor status. Concerning gait impairments, kinematic parameters (such as duration of gait cycle, step length, and velocity) were typically used to discern PD from healthy subjects, whereas for symptoms' assessment, researchers were capable of achieving accuracies of over 90% in a free-living environment. Further investigations should be focused on the development of ad-hoc hardware and software capable of providing real-time feedback to clinicians and patients. In addition, features such as the wearability of the system and user comfort, set-up process, and instructions for use, need to be strongly considered in the development of wearable sensors for PD monitoring.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0246528PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7861548PMC
February 2021

Motion Capture Technology in Industrial Applications: A Systematic Review.

Sensors (Basel) 2020 Oct 5;20(19). Epub 2020 Oct 5.

Tyndall National Institute, University College Cork, Cork T23, Ireland.

The rapid technological advancements of Industry 4.0 have opened up new vectors for novel industrial processes that require advanced sensing solutions for their realization. Motion capture (MoCap) sensors, such as visual cameras and inertial measurement units (IMUs), are frequently adopted in industrial settings to support solutions in robotics, additive manufacturing, teleworking and human safety. This review synthesizes and evaluates studies investigating the use of MoCap technologies in industry-related research. A search was performed in the Embase, Scopus, Web of Science and Google Scholar. Only studies in English, from 2015 onwards, on primary and secondary industrial applications were considered. The quality of the articles was appraised with the AXIS tool. Studies were categorized based on type of used sensors, beneficiary industry sector, and type of application. Study characteristics, key methods and findings were also summarized. In total, 1682 records were identified, and 59 were included in this review. Twenty-one and 38 studies were assessed as being prone to medium and low risks of bias, respectively. Camera-based sensors and IMUs were used in 40% and 70% of the studies, respectively. Construction (30.5%), robotics (15.3%) and automotive (10.2%) were the most researched industry sectors, whilst health and safety (64.4%) and the improvement of industrial processes or products (17%) were the most targeted applications. Inertial sensors were the first choice for industrial MoCap applications. Camera-based MoCap systems performed better in robotic applications, but camera obstructions caused by workers and machinery was the most challenging issue. Advancements in machine learning algorithms have been shown to increase the capabilities of MoCap systems in applications such as activity and fatigue detection as well as tool condition monitoring and object recognition.
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http://dx.doi.org/10.3390/s20195687DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7583783PMC
October 2020

Effects of segment masses and cut-off frequencies on the estimation of vertical ground reaction forces in running.

J Biomech 2020 01 9;99:109552. Epub 2019 Dec 9.

Tyndall National Institute, University College Cork, Lee Maltings Complex, Dyke Parade, T12R5CP Cork, Ireland.

The purpose of this study is to examine the effect of the body's mass distribution to segments and the filtering of kinematic data on the estimation of vertical ground reaction forces from positional data. A public dataset of raw running biomechanics was used for the purposes of the analysis, containing recordings of twenty-eight competitive or elite athletes running on an instrumented treadmill at three different speeds. A grid-search on half of the trials was employed to seek the values of the parameters that optimise the approximation of biomechanical loads. Two-way ANOVAs were then conducted to examine the significance of the parameterised factors in the modelled waveforms. The reserved recordings were used to validate the predictive accuracy of the model. The cut-off filtering frequencies of the pelvis and thigh markers were correlated to running speed and heel-strike patterns, respectively. Optimal segment masses were in agreement with standardised literature reported values. Root mean square errors for slow running (2.5 m/s) were on average equal to 0.1 (body weight normalized). Errors increased with running speeds to 0.13 and 0.18 for 3.5 m/s and 4.5 m/s, respectively. This study accurately estimated vertical ground reaction forces for slow-paced running by only considering the kinematics of the pelvis and thighs. Future studies should consider configuring the filtering of kinematic inputs based on the location of markers and type of running.
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http://dx.doi.org/10.1016/j.jbiomech.2019.109552DOI Listing
January 2020

Identification of Movement Strategies During the Sit-to-Walk Movement in Patients With Knee Osteoarthritis.

J Appl Biomech 2018 Apr 6;34(2):96-103. Epub 2018 Apr 6.

1 University of Strathclyde.

Patients with osteoarthritis of the knee commonly alter their movement to compensate for lower limb weakness and alleviate joint pain. Movement alterations may lead to weight-bearing asymmetries, and potentially to the progression of the disease. This study presents a novel numerical procedure for the identification of sit-to-walk strategies and differences in movement habits between control adults and persons with knee osteoarthritis. Ten control and 12 participants with osteoarthritis performed the sit-to-walk task in a motion capture laboratory. Participants sat on a stool with the height adjusted to 100% of their knee height, then stood and walked to pick up an object from a table in front of them. Different movement strategies were identified by means of hierarchical clustering. Trials were also classified as to whether the left and right extremities used a bilateral or an asymmetrical strategy. Participants with osteoarthritis used significantly more asymmetrical arm strategies (P = .03) while adopting the pushing through the chair strategy more often than the control subjects (P = .02). The results demonstrated that the 2 groups favor different sit-to-walk strategies. Asymmetrical arm behavior possibly indicates a compensation for the weakness of the affected leg. The proposed procedure may be useful to rapidly assess postoperative outcomes and developing rehabilitation strategies.
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http://dx.doi.org/10.1123/jab.2016-0279DOI Listing
April 2018
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