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Dr. Dimitrios Tsaopoulos
Institute for Bio-Economy and Agri-Technology (IBO), Centre of Research and Technology-Hellas (CERTH), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, Greece

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0 Biomechanics
0 Gait Analysis
0 Knee
0 Locomotion
0 Motion Analysis

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Journal article
Published: 05 March 2021 in Sensors
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This study aims to explore the possibility of estimating a multitude of kinematic and dynamic quantities using subject-specific musculoskeletal models in real-time. The framework was designed to operate with marker-based and inertial measurement units enabling extensions far beyond dedicated motion capture laboratories. We present the technical details for calculating the kinematics, generalized forces, muscle forces, joint reaction loads, and predicting ground reaction wrenches during walking. Emphasis was given to reduce computational latency while maintaining accuracy as compared to the offline counterpart. Notably, we highlight the influence of adequate filtering and differentiation under noisy conditions and its importance for consequent dynamic calculations. Real-time estimates of the joint moments, muscle forces, and reaction loads closely resemble OpenSim’s offline analyses. Model-based estimation of ground reaction wrenches demonstrates that even a small error can negatively affect other estimated quantities. An application of the developed system is demonstrated in the context of rehabilitation and gait retraining. We expect that such a system will find numerous applications in laboratory settings and outdoor conditions with the advent of predicting or sensing environment interactions. Therefore, we hope that this open-source framework will be a significant milestone for solving this grand challenge.

ACS Style

Dimitar Stanev; Konstantinos Filip; Dimitrios Bitzas; Sokratis Zouras; Georgios Giarmatzis; Dimitrios Tsaopoulos; Konstantinos Moustakas. Real-Time Musculoskeletal Kinematics and Dynamics Analysis Using Marker- and IMU-Based Solutions in Rehabilitation. Sensors 2021, 21, 1804 .

AMA Style

Dimitar Stanev, Konstantinos Filip, Dimitrios Bitzas, Sokratis Zouras, Georgios Giarmatzis, Dimitrios Tsaopoulos, Konstantinos Moustakas. Real-Time Musculoskeletal Kinematics and Dynamics Analysis Using Marker- and IMU-Based Solutions in Rehabilitation. Sensors. 2021; 21 (5):1804.

Chicago/Turabian Style

Dimitar Stanev; Konstantinos Filip; Dimitrios Bitzas; Sokratis Zouras; Georgios Giarmatzis; Dimitrios Tsaopoulos; Konstantinos Moustakas. 2021. "Real-Time Musculoskeletal Kinematics and Dynamics Analysis Using Marker- and IMU-Based Solutions in Rehabilitation." Sensors 21, no. 5: 1804.

Journal article
Published: 02 March 2021 in Applied Sciences
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The present study deals with human awareness, which is a very important aspect of human–robot interaction. This feature is particularly essential in agricultural environments, owing to the information-rich setup that they provide. The objective of this investigation was to recognize human activities associated with an envisioned synergistic task. In order to attain this goal, a data collection field experiment was designed that derived data from twenty healthy participants using five wearable sensors (embedded with tri-axial accelerometers, gyroscopes, and magnetometers) attached to them. The above task involved several sub-activities, which were carried out by agricultural workers in real field conditions, concerning load lifting and carrying. Subsequently, the obtained signals from on-body sensors were processed for noise-removal purposes and fed into a Long Short-Term Memory neural network, which is widely used in deep learning for feature recognition in time-dependent data sequences. The proposed methodology demonstrated considerable efficacy in predicting the defined sub-activities with an average accuracy of 85.6%. Moreover, the trained model properly classified the defined sub-activities in a range of 74.1–90.4% for precision and 71.0–96.9% for recall. It can be inferred that the combination of all sensors can achieve the highest accuracy in human activity recognition, as concluded from a comparative analysis for each sensor’s impact on the model’s performance. These results confirm the applicability of the proposed methodology for human awareness purposes in agricultural environments, while the dataset was made publicly available for future research.

ACS Style

Athanasios Anagnostis; Lefteris Benos; Dimitrios Tsaopoulos; Aristotelis Tagarakis; Naoum Tsolakis; Dionysis Bochtis. Human Activity Recognition through Recurrent Neural Networks for Human–Robot Interaction in Agriculture. Applied Sciences 2021, 11, 2188 .

AMA Style

Athanasios Anagnostis, Lefteris Benos, Dimitrios Tsaopoulos, Aristotelis Tagarakis, Naoum Tsolakis, Dionysis Bochtis. Human Activity Recognition through Recurrent Neural Networks for Human–Robot Interaction in Agriculture. Applied Sciences. 2021; 11 (5):2188.

Chicago/Turabian Style

Athanasios Anagnostis; Lefteris Benos; Dimitrios Tsaopoulos; Aristotelis Tagarakis; Naoum Tsolakis; Dionysis Bochtis. 2021. "Human Activity Recognition through Recurrent Neural Networks for Human–Robot Interaction in Agriculture." Applied Sciences 11, no. 5: 2188.

Journal article
Published: 01 March 2021 in Healthcare
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Knee osteoarthritis (KOA) is a multifactorial disease which is responsible for more than 80% of the osteoarthritis disease’s total burden. KOA is heterogeneous in terms of rates of progression with several different phenotypes and a large number of risk factors, which often interact with each other. A number of modifiable and non-modifiable systemic and mechanical parameters along with comorbidities as well as pain-related factors contribute to the development of KOA. Although models exist to predict the onset of the disease or discriminate between asymptotic and OA patients, there are just a few studies in the recent literature that focused on the identification of risk factors associated with KOA progression. This paper contributes to the identification of risk factors for KOA progression via a robust feature selection (FS) methodology that overcomes two crucial challenges: (i) the observed high dimensionality and heterogeneity of the available data that are obtained from the Osteoarthritis Initiative (OAI) database and (ii) a severe class imbalance problem posed by the fact that the KOA progressors class is significantly smaller than the non-progressors’ class. The proposed feature selection methodology relies on a combination of evolutionary algorithms and machine learning (ML) models, leading to the selection of a relatively small feature subset of 35 risk factors that generalizes well on the whole dataset (mean accuracy of 71.25%). We investigated the effectiveness of the proposed approach in a comparative analysis with well-known FS techniques with respect to metrics related to both prediction accuracy and generalization capability. The impact of the selected risk factors on the prediction output was further investigated using SHapley Additive exPlanations (SHAP). The proposed FS methodology may contribute to the development of new, efficient risk stratification strategies and identification of risk phenotypes of each KOA patient to enable appropriate interventions.

ACS Style

Christos Kokkotis; Serafeim Moustakidis; Vasilios Baltzopoulos; Giannis Giakas; Dimitrios Tsaopoulos. Identifying Robust Risk Factors for Knee Osteoarthritis Progression: An Evolutionary Machine Learning Approach. Healthcare 2021, 9, 260 .

AMA Style

Christos Kokkotis, Serafeim Moustakidis, Vasilios Baltzopoulos, Giannis Giakas, Dimitrios Tsaopoulos. Identifying Robust Risk Factors for Knee Osteoarthritis Progression: An Evolutionary Machine Learning Approach. Healthcare. 2021; 9 (3):260.

Chicago/Turabian Style

Christos Kokkotis; Serafeim Moustakidis; Vasilios Baltzopoulos; Giannis Giakas; Dimitrios Tsaopoulos. 2021. "Identifying Robust Risk Factors for Knee Osteoarthritis Progression: An Evolutionary Machine Learning Approach." Healthcare 9, no. 3: 260.

Journal article
Published: 11 February 2021 in Diagnostics
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Osteoarthritis is a joint disease that commonly occurs in the knee (KOA). The continuous increase in medical data regarding KOA has triggered researchers to incorporate artificial intelligence analytics for KOA prognosis or treatment. In this study, two approaches are presented to predict the progression of knee joint space narrowing (JSN) in each knee and in both knees combined. A machine learning approach is proposed with the use of multidisciplinary data from the osteoarthritis initiative database. The proposed methodology employs: (i) A clustering process to identify groups of people with progressing and non-progressing JSN; (ii) a robust feature selection (FS) process consisting of filter, wrapper, and embedded techniques that identifies the most informative risk factors; (iii) a decision making process based on the evaluation and comparison of various classification algorithms towards the selection and development of the final predictive model for JSN; and (iv) post-hoc interpretation of the features’ impact on the best performing model. The results showed that bounding the JSN progression of both knees can result to more robust prediction models with a higher accuracy (83.3%) and with fewer risk factors (29) compared to the right knee (77.7%, 88 risk factors) and the left knee (78.3%, 164 risk factors), separately.

ACS Style

Charis Ntakolia; Christos Kokkotis; Serafeim Moustakidis; Dimitrios Tsaopoulos. Prediction of Joint Space Narrowing Progression in Knee Osteoarthritis Patients. Diagnostics 2021, 11, 285 .

AMA Style

Charis Ntakolia, Christos Kokkotis, Serafeim Moustakidis, Dimitrios Tsaopoulos. Prediction of Joint Space Narrowing Progression in Knee Osteoarthritis Patients. Diagnostics. 2021; 11 (2):285.

Chicago/Turabian Style

Charis Ntakolia; Christos Kokkotis; Serafeim Moustakidis; Dimitrios Tsaopoulos. 2021. "Prediction of Joint Space Narrowing Progression in Knee Osteoarthritis Patients." Diagnostics 11, no. 2: 285.

Journal article
Published: 28 September 2020 in Applied Sciences
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Knee Osteoarthritis (KOA) is a multifactorial disease that causes low quality of life, poor psychology and resignation from life. Furthermore, KOA is a big data problem in terms of data complexity, heterogeneity and size as it has been commonly considered in the literature with most of the reported studies being limited in the amount of information they can adequately process. The aim of this paper is: (i) To provide a robust feature selection (FS) approach that could identify important risk factors which contribute to the prediction of KOA and (ii) to develop machine learning (ML) prediction models for KOA. The current study considers multidisciplinary data from the osteoarthritis initiative (OAI) database, the available features of which come from heterogeneous sources such as questionnaire data, physical activity indexes, self-reported data about joint symptoms, disability and function as well as general health and physical exams’ data. The novelty of the proposed FS methodology lies on the combination of different well-known approaches including filter, wrapper and embedded techniques, whereas feature ranking is decided on the basis of a majority vote scheme to avoid bias. The validation of the selected factors was performed in data subgroups employing seven well-known classifiers in five different approaches. A 74.07% classification accuracy was achieved by SVM on the group of the first fifty-five selected risk factors. The effectiveness of the proposed approach was evaluated in a comparative analysis with respect to classification errors and confusion matrices to confirm its clinical relevance. The results are the basis for the development of reliable tools for the prediction of KOA progression.

ACS Style

Christos Kokkotis; Serafeim Moustakidis; Giannis Giakas; Dimitrios Tsaopoulos. Identification of Risk Factors and Machine Learning-Based Prediction Models for Knee Osteoarthritis Patients. Applied Sciences 2020, 10, 6797 .

AMA Style

Christos Kokkotis, Serafeim Moustakidis, Giannis Giakas, Dimitrios Tsaopoulos. Identification of Risk Factors and Machine Learning-Based Prediction Models for Knee Osteoarthritis Patients. Applied Sciences. 2020; 10 (19):6797.

Chicago/Turabian Style

Christos Kokkotis; Serafeim Moustakidis; Giannis Giakas; Dimitrios Tsaopoulos. 2020. "Identification of Risk Factors and Machine Learning-Based Prediction Models for Knee Osteoarthritis Patients." Applied Sciences 10, no. 19: 6797.

Review
Published: 20 August 2020 in Frontiers in Bioengineering and Biotechnology
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The anterior cruciate ligament (ACL) constitutes one of the most important stabilizing tissues of the knee joint whose rapture is very prevalent. ACL reconstruction (ACLR) from a graft is a surgery which yields the best outcome. Taking into account the complicated nature of this operation and the high cost of experiments, finite element (FE) simulations can become a valuable tool for evaluating the surgery in a pre-clinical setting. The present study summarizes, for the first time, the current advancement in ACLR in both clinical and computational level. It also emphasizes on the material modeling and properties of the most popular grafts as well as modeling of different surgery techniques. It can be concluded that more effort is needed to be put toward more realistic simulation of the surgery, including also the use of two bundles for graft representation, graft pretension and artificial grafts. Furthermore, muscles and synovial fluid need to be included, while patellofemoral joint is an important bone that is rarely used. More realistic models are also required for soft tissues, as most articles used isotropic linear elastic models and springs. In summary, accurate and realistic FE analysis in conjunction with multidisciplinary collaboration could contribute to ACLR improvement provided that several important aspects are carefully considered.

ACS Style

Lefteris Benos; Dimitar Stanev; Leonidas Spyrou; Konstantinos Moustakas; Dimitrios E. Tsaopoulos. A Review on Finite Element Modeling and Simulation of the Anterior Cruciate Ligament Reconstruction. Frontiers in Bioengineering and Biotechnology 2020, 8, 1 .

AMA Style

Lefteris Benos, Dimitar Stanev, Leonidas Spyrou, Konstantinos Moustakas, Dimitrios E. Tsaopoulos. A Review on Finite Element Modeling and Simulation of the Anterior Cruciate Ligament Reconstruction. Frontiers in Bioengineering and Biotechnology. 2020; 8 ():1.

Chicago/Turabian Style

Lefteris Benos; Dimitar Stanev; Leonidas Spyrou; Konstantinos Moustakas; Dimitrios E. Tsaopoulos. 2020. "A Review on Finite Element Modeling and Simulation of the Anterior Cruciate Ligament Reconstruction." Frontiers in Bioengineering and Biotechnology 8, no. : 1.

Review
Published: 18 May 2020 in Applied Sciences
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Background: Musculoskeletal disorders (MSDs) have long been recognized as the most common risks that operation of agricultural machineries poses, thus, undermining the ability to labor and quality of life. The purpose of this investigation was to thoroughly review the recent scholarly literature on ergonomics in agricultural mechanized operations; Methods: Electronic database research over the last ten years was conducted based on specific inclusion criteria. Furthermore, an assessment of the methodological quality and strength of evidence of potential risk factors causing MSDs was performed; Results: The results demonstrated that ergonomics in agriculture is an interdisciplinary topic and concerns both developed and developing countries. The machines with driving seats seem to be associated with painful disorders of the low back, while handheld machines with disorders of the upper extremities. The main roots of these disorders are the whole-body vibration (WBV) and hand-arm transmitted vibration (HATV). However, personal characteristics, awkward postures, mechanical shocks and seat discomfort were also recognized to cause MSDs; Conclusions: The present ergonomic interventions aim mainly at damping of vibrations and improving the comfort of operator. Nevertheless, more collaborative efforts among physicians, ergonomists, engineers and manufacturers are required in terms of both creating new ergonomic technologies and increasing the awareness of workers for the involved risk factors.

ACS Style

Lefteris Benos; Dimitrios Tsaopoulos; Dionysis Bochtis. A Review on Ergonomics in Agriculture. Part II: Mechanized Operations. Applied Sciences 2020, 10, 3484 .

AMA Style

Lefteris Benos, Dimitrios Tsaopoulos, Dionysis Bochtis. A Review on Ergonomics in Agriculture. Part II: Mechanized Operations. Applied Sciences. 2020; 10 (10):3484.

Chicago/Turabian Style

Lefteris Benos; Dimitrios Tsaopoulos; Dionysis Bochtis. 2020. "A Review on Ergonomics in Agriculture. Part II: Mechanized Operations." Applied Sciences 10, no. 10: 3484.

Review
Published: 11 March 2020 in Applied Sciences
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Background: Agriculture involves several harmful diseases. Among the non-fatal ones, musculoskeletal disorders (MSDs) are the most prevalent, as they have reached epidemic proportions. The main aim of this investigation is to systematically review the major risk factors regarding MSDs as well as evaluate the existing ergonomic interventions. Methods: The search engines of Google Scholar, PubMed, Scopus, and ScienceDirect were used to identify relevant articles during the last decade. The imposed exclusive criteria assured the accuracy and current progress in this field. Results: It was concluded that MSDs affect both developed and developing countries, thus justifying the existing global concern. Overall, the most commonly studied task was harvesting, followed by load carrying, pruning, planting, and other ordinary manual operations. Repetitive movements in awkward postures, such as stooping and kneeling; individual characteristics; as well as improper tool design were observed to contribute to the pathogenesis of MSDs. Furthermore, low back disorders were reported as the main disorder. Conclusions: The present ergonomic interventions seem to attenuate the MSDs to a great extent. However, international reprioritization of the safety and health measures is required in agriculture along with increase of the awareness of the risk factors related to MSDs.

ACS Style

Lefteris Benos; Dimitrios Tsaopoulos; Dionysis Bochtis. A Review on Ergonomics in Agriculture. Part I: Manual Operations. Applied Sciences 2020, 10, 1905 .

AMA Style

Lefteris Benos, Dimitrios Tsaopoulos, Dionysis Bochtis. A Review on Ergonomics in Agriculture. Part I: Manual Operations. Applied Sciences. 2020; 10 (6):1905.

Chicago/Turabian Style

Lefteris Benos; Dimitrios Tsaopoulos; Dionysis Bochtis. 2020. "A Review on Ergonomics in Agriculture. Part I: Manual Operations." Applied Sciences 10, no. 6: 1905.