Browsing by Author "Unal, Ramazan"

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Citation - Scopus: 7
ANT-M: Design of Passive Lower-Limb Exoskeleton for Weight-Bearing Assistance in Industry
(Springer International Publishing, 2019) Guncan, Berkay; Unal, Ramazan
This study describes the optimized design of a passive lower limb exoskeleton for workers in the industry. The exoskeleton is aimed at helping workers who carry heavy loads, by supporting their posture and reducing stress in their knees which would prevent future injuries. However, most of the previous passive designs are insufficient in a way that they are bulky. Therefore, this study is focused on achieving lightweight passive exoskeleton. Topology optimization has been carried out to reach this goal. The results are validated using finite elements methods, in ANSYS environment. © 2018 Elsevier B.V., All rights reserved.
Biyonik Elin Faaliyete Hazırlanmasında Kaldırılacak Cisme Dair Ağırlık Algısının Beyin Sinyalleriyle Belirlenmesi
(2022) Ulutabanca, Halil; Altindis, Fatih; Unal, Ramazan; Yilmaz, Bulent; Sarrafıkhosrowshah, Mahsa
The upper extremity prostheses vary due to the patient?s articulation level and the methods used to move them. There are prostheses that are either cosmetic, or that work with shoulder movement (mechanical), or controlled by myoelectronic and electroencephalography (EEG) signals. However, intuitive and unnatural control of the prosthesis places a great mental burden on the user. In this project, the aim is to develop a system to improve the control of the bionic hand prosthesis by using EEG and EMG signals together, by making use of the user's visual weight perception. With this system, it is aimed to reduce the physical and mental burden/discomfort patients may experience while using a mechanical prosthesis. The preconditioning of the prototype hand to be produced is provided by evaluating the weight of the objects seen by the patients to the extent that the brain perceives them visually. In this way, the force exerted by the patient on the shoulder while holding the object will decrease and the mental load will be alleviated. For this purpose, EEG and electromyography (EMG) signals of the subjects were taken and processed, and then a real-time implementation was developed. In the first stage, a study was conducted that aimed to operate the prosthesis by using the motor intention waves of the prosthesis users and the classification success of the machine learning approaches (detection of the intention to activate the prosthesis) was examined by taking EEG data from 30 healthy participants. In the second stage, EEG and EMG signals of 31 healthy participants were recorded synchronously while reaching for the object, lifting the object and leaving the object in the starting position. After the features of these signals were determined, it was determined that the object was heavy, medium weight or light using various classification approaches. In parallel with biosignal processing studies, prosthetic hand and wrist designs and three- dimensional prints were obtained. It is aimed to use the shoulder movement to open and close the prosthetic hand, and to control the wrist stiffness, to process the biosignals and drive a tiny motor with high torque with the automatic decision produced. In addition, the characterization of the prosthesis was made. As a result of the classification of the multi-channel EEG signals from 20 healthy individuals with Fourier-based synchrosequeezing transform (FSST) and singular value decomposition (SVD) approaches by extracting features, the goal was to control the stiffness of the wrist part of the prosthesis. As a result, it was possible for the system to detect the weight of the object the user sees while employing the prosthesis and to precondition the prosthesis according to this weight when they want to hold and move that object.
Biyonik Elin Faaliyete Hazırlanmasında Kaldırılacak Cisme dair Ağırlık Algısının Beyin Sinyalleriyle Belirlenmesi
(2022) Ulutabanca, Halil; Altindis, Fatih; Unal, Ramazan; Yilmaz, Bulent; Sarrafıkhosrowshah, Mahsa
The upper extremity prostheses vary due to the patient?s articulation level and the methods used to move them. There are prostheses that are either cosmetic, or that work with shoulder movement (mechanical), or controlled by myoelectronic and electroencephalography (EEG) signals. However, intuitive and unnatural control of the prosthesis places a great mental burden on the user. In this project, the aim is to develop a system to improve the control of the bionic hand prosthesis by using EEG and EMG signals together, by making use of the user's visual weight perception. With this system, it is aimed to reduce the physical and mental burden/discomfort patients may experience while using a mechanical prosthesis. The preconditioning of the prototype hand to be produced is provided by evaluating the weight of the objects seen by the patients to the extent that the brain perceives them visually. In this way, the force exerted by the patient on the shoulder while holding the object will decrease and the mental load will be alleviated. For this purpose, EEG and electromyography (EMG) signals of the subjects were taken and processed, and then a real-time implementation was developed. In the first stage, a study was conducted that aimed to operate the prosthesis by using the motor intention waves of the prosthesis users and the classification success of the machine learning approaches (detection of the intention to activate the prosthesis) was examined by taking EEG data from 30 healthy participants. In the second stage, EEG and EMG signals of 31 healthy participants were recorded synchronously while reaching for the object, lifting the object and leaving the object in the starting position. After the features of these signals were determined, it was determined that the object was heavy, medium weight or light using various classification approaches. In parallel with biosignal processing studies, prosthetic hand and wrist designs and three- dimensional prints were obtained. It is aimed to use the shoulder movement to open and close the prosthetic hand, and to control the wrist stiffness, to process the biosignals and drive a tiny motor with high torque with the automatic decision produced. In addition, the characterization of the prosthesis was made. As a result of the classification of the multi-channel EEG signals from 20 healthy individuals with Fourier-based synchrosequeezing transform (FSST) and singular value decomposition (SVD) approaches by extracting features, the goal was to control the stiffness of the wrist part of the prosthesis. As a result, it was possible for the system to detect the weight of the object the user sees while employing the prosthesis and to precondition the prosthesis according to this weight when they want to hold and move that object.
Design of a Knee Exoskeleton Assisting During Walking, Sit-to-Stand, and Stair Ascending
(2021) Unal, Ramazan; Bilgi, Muhammet Furkan
Bu çalışmada, yürümede, ayağa kalkmada ve merdiven çıkmada ağırlık kabulüne yardımcı olan bir diz dış iskeletini sunuyoruz. Dış iskelet cihazının çalışma prensibi, doğal insan yürüyüşü, ayağa kalkma ve merdiven çıkma hareketlerinin biyo-mekanik verilerinden esinlenmiştir. İlk olarak, diz ekleminin ağırlık kabul davranışını belirlemek için doğal yürüyüş verileri analiz edilmiştir. Ardından, elastik bir elemanla ayağa kalkma hareketi için bir model oluşturulmuştur. Bundan sonra, merdiven çıkma verileri benzer şekilde analiz edilmiştir. Ardından, bu hareketler için uygun elastik elemanları, bunların elastik katsayıları ve gerekirse motor gereksinimleri tanımlanmıştır. Son olarak, önerilen diz dış iskeletinin CAD modeli sunulmuştur.
Citation - Scopus: 2
Design of Bio-Joint Shaped Knee Exoskeleton Assisting for Walking and Sit-to
(Springer International Publishing, 2019) Kapci, Mehmet Fazil; Unal, Ramazan
In this study, a bio-joint shaped knee joint exoskeleton is presented. This design is meant for avoiding misalignment of the exoskeleton joint with the biological knee joint. For this purpose a cam mechanism has been designed to prevent the misalignment from translation of the femur on tibia. Additionally, walking and sit-to-stance is passively assisted with a spring element that is activated with the heel contact. A single spring is used for both walking and sit-to-stance, due to the similar characteristics of the gait cycle and initial phases of the sit-to-stance in joint stiffness. © 2018 Elsevier B.V., All rights reserved.
Citation - WoS: 99
Citation - Scopus: 118
A Human-Robot Interaction Perspective on Assistive and Rehabilitation Robotics
(Frontiers Media S.A., 2017) Beckerle, Philipp; Salvietti, Gionata; Unal, Ramazan; Prattichizzo, Domenico; Rossi, Simone; Castellini, Claudio; Bianchi, Matteo
Assistive and rehabilitation devices are a promising and challenging field of recent robotics research. Motivated by societal needs such as aging populations, such devices can support motor functionality and subject training. The design, control, sensing, and assessment of the devices become more sophisticated due to a human in the loop. This paper gives a human-robot interaction perspective on current issues and opportunities in the field. On the topic of control and machine learning, approaches that support but do not distract subjects are reviewed. Options to provide sensory user feedback that are currently missing from robotic devices are outlined. Parallels between device acceptance and affective computing are made. Furthermore, requirements for functional assessment protocols that relate to real-world tasks are discussed. In all topic areas, the design of human-oriented frameworks and methods is dominated by challenges related to the close interaction between the human and robotic device. This paper discusses the aforementioned aspects in order to open up new perspectives for future robotic solutions.