Stroppa, Fabıo
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Fabio, Stroppa
S., Fabıo
Stroppa,F.
Stroppa,Fabio
STROPPA, FABIO
Stroppa, F.
STROPPA, Fabıo
S.,Fabio
F. Stroppa
Stroppa, Fabio
Fabıo Stroppa
Stroppa, Fabıo
S., Fabio
Stroppa F.
Stroppa, FABIO
Fabıo STROPPA
FABIO STROPPA
S., Fabıo
Stroppa,F.
Stroppa,Fabio
STROPPA, FABIO
Stroppa, F.
STROPPA, Fabıo
S.,Fabio
F. Stroppa
Stroppa, Fabio
Fabıo Stroppa
Stroppa, Fabıo
S., Fabio
Stroppa F.
Stroppa, FABIO
Fabıo STROPPA
FABIO STROPPA
Job Title
Dr. Öğr. Üyesi
Email Address
Main Affiliation
Computer Engineering
Status
Current Staff
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Sustainable Development Goals
15
LIFE ON LAND
0
Research Products
16
PEACE, JUSTICE AND STRONG INSTITUTIONS
0
Research Products
14
LIFE BELOW WATER
0
Research Products
6
CLEAN WATER AND SANITATION
0
Research Products
3
GOOD HEALTH AND WELL-BEING
3
Research Products
17
PARTNERSHIPS FOR THE GOALS
0
Research Products
4
QUALITY EDUCATION
0
Research Products
2
ZERO HUNGER
0
Research Products
10
REDUCED INEQUALITIES
0
Research Products
7
AFFORDABLE AND CLEAN ENERGY
0
Research Products
13
CLIMATE ACTION
0
Research Products
1
NO POVERTY
0
Research Products
9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
2
Research Products
12
RESPONSIBLE CONSUMPTION AND PRODUCTION
0
Research Products
8
DECENT WORK AND ECONOMIC GROWTH
0
Research Products
11
SUSTAINABLE CITIES AND COMMUNITIES
0
Research Products
5
GENDER EQUALITY
0
Research Products
Documents
33
Citations
496
h-index
11
Documents
29
Citations
275
Scholarly Output
18
Articles
11
Views / Downloads
76/396
Supervised MSc Theses
1
Supervised PhD Theses
0
WoS Citation Count
181
Scopus Citation Count
261
WoS h-index
6
Scopus h-index
6
Patents
0
Projects
0
WoS Citations per Publication
10.06
Scopus Citations per Publication
14.50
Open Access Source
8
Supervised Theses
1
Google Analytics Visitor Traffic
| Journal | Count |
|---|---|
| IEEE Access | 2 |
| Artificial Intelligence in Medicine | 1 |
| Engineering Applications of Artificial Intelligence | 1 |
| Evolutionary Intelligence | 1 |
| IEEE International Conference on Robotics and Automation (ICRA) -- MAY 13-17, 2024 -- Yokohama, JAPAN | 1 |
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Scholarly Output Search Results
Now showing 1 - 10 of 18
Article Citation - WoS: 1Citation - Scopus: 1Gamifying Haptics User Studies: Comparison of Response Times From Smartphone Interfaces(IEEE Computer Soc, 2025) Kudsi, Bushra; Xu, Doris; Sen, Umit; Yoshida, Kyle T.; Stroppa, Fabio; Nunez, Cara M.; Sarac, MineHaptics user studies are often restricted to a set, physical location and use methods that do not captivate the user. Applying game design elements can create an entertaining environment and increase user engagement. Using ubiquitous tools, like smartphones, to conduct haptics user studies could allow researchers to access larger participant groups while a gamified approach could facilitate the data collection by making the experiment more enjoyable. To explore this concept, this work presents a gamified version of an existing psychophysical experiment that investigates response time to multisensory cues using a smartphone based on "Whac-A-Mole". We conducted a user study to compare our gamified interface with an existing psychophysical interface with thirteen participants exploring the response time from eighteen combinations of auditory, haptic, and visual stimuli at different levels of intensities and participant preferences for both interfaces. The results demonstrate that the gamified interface successfully captured similar trends in response times and significantly elevated participant enjoyment ($p < 0.003$), but did not result in equivalent response times to the original interface. This work shows the benefits and drawbacks of following a gamification approach when designing haptics user studies and discusses factors and trade-offs to consider when gamifying studies.Article Citation - WoS: 11Shared-Control Teleoperation Paradigms on a Soft-Growing Robot Manipulator(Springer, 2023) Stroppa, Fabio; Selvaggio, Mario; Agharese, Nathaniel; Luo, Ming; Blumenschein, Laura H.; Hawkes, Elliot W.; Okamura, Allison M.Semi-autonomous telerobotic systems allow both humans and robots to exploit their strengths while enabling personalized execution of a remote task. For soft robots with kinematic structures dissimilar to those of human operators, it is unknown how the allocation of control between the human and the robot changes the performance. This work presents a set of interaction paradigms between a human and a remote soft-growing robot manipulator, with demonstrations in both real and simulated scenarios. The soft robot can grow and retract by eversion and inversion of its tubular body, a property we exploit in the interaction paradigms. We implemented and tested six different human-robot interaction paradigms, with full teleoperation at one extreme and gradually adding autonomy to various aspects of the task execution. All paradigms are demonstrated by two experts and two naive operators. Results show that humans and the soft robot manipulator can effectively split their control along different degrees of freedom while acting simultaneously to accomplish a task. In the simple pick-and-place task studied in this work, performance improves as the control is gradually given to the robot's autonomy, especially when the robot can correct certain human errors. However, human engagement is maximized when the control over a task is at least partially shared. Finally, when the human operator is assisted by haptic guidance, which is computed based on soft robot tip position errors, we observed that the improvement in performance is dependent on the expertise of the human operator.Article Cooperative Mission Planning for Multiple Aerial and Ground Vehicles Based on Evolutionary Computation(IEEE-Inst Electrical Electronics Engineers Inc, 2025) Eker, A. Harun; Oznigolyan, Masis; Karaagacli, Kemal Faruk; Gokalp, Dogukan; Bickici, Yigit; Stroppa, FabioThe limited flight endurance of unmanned aerial vehicles (UAVs) necessitates multiple battery replacements to complete long-duration missions. In this paper, we address the cooperative mission planning of multiple UAVs and unmanned ground vehicles (UGVs), where UAVs are tasked with visiting a predetermined set of waypoints, and UGVs act as mobile battery replenishment platforms. The objective is to determine a cooperative mission plan that minimizes the overall mission completion time while respecting UAV flight time constraints. To this end, the mission is decomposed into a set of flight-and-recharge segments, and a genetic algorithm is applied to solve the resulting optimization problem. The proposed approach is evaluated using real-world datasets from three different operational areas. Extensive experiments are conducted to analyze parameter settings and validate the robustness of the method. Simulation results show that the algorithm adapts to variations in mission layout and can efficiently plan large-scale missions with thousands of waypoints, involving multiple UAVs and UGVs. A comparative study demonstrates that the proposed method achieves mission times very close to optimal solutions with a single-robot pair and remains competitive with a theoretical lower bound in multi-robot scenarios.Conference Object Citation - Scopus: 3Optimizing Real-Time Decision-Making in Sensor Networks(Institute of Electrical and Electronics Engineers Inc., 2023) Yiǧitbaşi,Y.; Stroppa,F.; Badia,L.The rapid integration of digital technologies into physical systems has given rise to cyber-physical systems, where the interaction between the computational and physical components plays a crucial role. This study explores optimal decision-making in event detection and transmission scheduling within cyber-physical systems, emphasizing the crucial aspect of efficient decision-making. We consider the problem of monitoring and reporting about a single event taking place within a finite time window achieving a reward related to the timeliness of the status update. Thus, the objective corresponds to minimizing the age of information between the instant of the event x and the status update time t, with a further penalty for a missed event. The monitoring apparatus decides when to perform the status update without knowing the value of x, but only knowing its statistical distribution. We assume a triangular probability density function for the instant of the event taking place, with a variable average. We provide an analytical derivation of the optimal choice of the status update, highlighting interesting trends, such as the saturation in the value of t as x grows close to the limit of the observation window. This proposed problem and its analytical formalization may serve as a further foundation for the general analysis of optimal monitoring of cyber-physical systems. © 2023 IEEE.Conference Object Citation - WoS: 3Citation - Scopus: 4The Impact of Evolutionary Computation on Robotic Design: a Case Study With an Underactuated Hand Exoskeleton(Ieee, 2024) Akbas, Baris; Yuksel, Huseyin Taner; Soylemez, Aleyna; Zyada, Mazhar Eid; Sarac, Mine; Stroppa, FabioRobotic exoskeletons can enhance human strength and aid people with physical disabilities. However, designing them to ensure safety and optimal performance presents significant challenges. Developing exoskeletons should incorporate specific optimization algorithms to find the best design. This study investigates the potential of Evolutionary Computation (EC) methods in robotic design optimization, with an underactuated hand exoskeleton (U-HEx) used as a case study. We propose improving the performance and usability of the U-HEx design, which was initially optimized using a naive brute-force approach, by integrating EC techniques such as Genetic Algorithm and Big Bang-Big Crunch Algorithm. Comparative analysis revealed that EC methods consistently yield more precise and optimal solutions than brute force in a significantly shorter time. This allowed us to improve the optimization by increasing the number of variables in the design, which was impossible with naive methods. The results show significant improvements in terms of the torque magnitude the device transfers to the user, enhancing its efficiency. These findings underline the importance of performing proper optimization while designing exoskeletons, as well as providing a significant improvement to this specific robotic design.Review Citation - WoS: 7Citation - Scopus: 8Optimizing soft robot design and tracking with and without evolutionary computation: an intensive survey(Cambridge Univ Press, 2024) Stroppa, Fabio; Majeed, Fatimah Jabbar; Batiya, Jana; Baran, Eray; Sarac, MineSoft robotic devices are designed for applications such as exploration, manipulation, search and rescue, medical surgery, rehabilitation, and assistance. Due to their complex kinematics, various and often hard-to-define degrees of freedom, and nonlinear properties of their material, designing and operating these devices can be quite challenging. Using tools such as optimization methods can improve the efficiency of these devices and help roboticists manufacture the robots they need. In this work, we present an extensive and systematic literature search on the optimization methods used for the mechanical design of soft robots, particularly focusing on literature exploiting evolutionary computation (EC). We completed the search in the IEEE, ACM, Springer, SAGE, Elsevier, MDPI, Scholar, and Scopus databases between 2009 and 2024 using the keywords "soft robot," "design," and "optimization." We categorized our findings in terms of the type of soft robot (i.e., bio-inspired, cable-driven, continuum, fluid-driven, gripper, manipulator, modular), its application (exploration, manipulation, surgery), the optimization metrics (topology, force, locomotion, kinematics, sensors, and energy), and the optimization method (categorized as EC or non-EC methods). After providing a road map of our findings in the state of the art, we offer our observations concerning the implementation of the optimization methods and their advantages. We then conclude our paper with suggestions for future research.Article Citation - Scopus: 13Shared-Control Teleoperation Paradigms on a Soft-Growing Robot Manipulator(Institute for Ionics, 2023) Stroppa, F.; Selvaggio, M.; Agharese, N.; Luo, M.; Blumenschein, L.H.; Hawkes, E.W.; Okamura, A.M.Semi-autonomous telerobotic systems allow both humans and robots to exploit their strengths while enabling personalized execution of a remote task. For soft robots with kinematic structures dissimilar to those of human operators, it is unknown how the allocation of control between the human and the robot changes the performance. This work presents a set of interaction paradigms between a human and a remote soft-growing robot manipulator, with demonstrations in both real and simulated scenarios. The soft robot can grow and retract by eversion and inversion of its tubular body, a property we exploit in the interaction paradigms. We implemented and tested six different human-robot interaction paradigms, with full teleoperation at one extreme and gradually adding autonomy to various aspects of the task execution. All paradigms are demonstrated by two experts and two naive operators. Results show that humans and the soft robot manipulator can effectively split their control along different degrees of freedom while acting simultaneously to accomplish a task. In the simple pick-and-place task studied in this work, performance improves as the control is gradually given to the robot’s autonomy, especially when the robot can correct certain human errors. However, human engagement is maximized when the control over a task is at least partially shared. Finally, when the human operator is assisted by haptic guidance, which is computed based on soft robot tip position errors, we observed that the improvement in performance is dependent on the expertise of the human operator. © 2023, The Author(s), under exclusive licence to Springer Nature B.V.Conference Object Citation - WoS: 1Citation - Scopus: 1Large Monitors Reduce Tracking Error in Robot-Assisted Visual-Motor Tasks(IEEE, 2022) Stroppa, Fabio; Amirabdollahian, Farshid; Frisoli, AntonioRobot-assisted rehabilitation often makes use of virtual environments to present the therapy tasks. Virtual reality has the ability of providing valuable visual feedback and enjoyable interaction to the patients; therefore, the way they are displayed to users becomes crucial. Is the monitor size an important feature that influences how the task is perceived and thus affects patients' performance? This study on healthy participants investigates the influence of displays in perceiving haptic effects. The participants performed an experiment using an end-effector robot, where they followed a moving target around a trajectory while disturbed by a simulated perturbation and assisted by an adaptive algorithm. The experiment was presented on two different monitors to assess whether a different size affects their performance. Statistically significant results show that the performance achieved with the large monitor features lower error compared to the small monitor, implying that large monitors might be a better solution for rehabilitation with virtual tasks and assistive robots.Article Citation - WoS: 117Citation - Scopus: 185Opportunities and Challenges of Artificial Intelligence and Distributed Systems To Improve the Quality of Healthcare Service(Elsevier, 2024) Aminizadeh, Sarina; Heidari, Arash; Dehghan, Mahshid; Toumaj, Shiva; Rezaei, Mahsa; Navimipour, Nima Jafari; Unal, MehmetThe healthcare sector, characterized by vast datasets and many diseases, is pivotal in shaping community health and overall quality of life. Traditional healthcare methods, often characterized by limitations in disease prevention, predominantly react to illnesses after their onset rather than proactively averting them. The advent of Artificial Intelligence (AI) has ushered in a wave of transformative applications designed to enhance healthcare services, with Machine Learning (ML) as a noteworthy subset of AI. ML empowers computers to analyze extensive datasets, while Deep Learning (DL), a specific ML methodology, excels at extracting meaningful patterns from these data troves. Despite notable technological advancements in recent years, the full potential of these applications within medical contexts remains largely untapped, primarily due to the medical community's cautious stance toward novel technologies. The motivation of this paper lies in recognizing the pivotal role of the healthcare sector in community well-being and the necessity for a shift toward proactive healthcare approaches. To our knowledge, there is a notable absence of a comprehensive published review that delves into ML, DL and distributed systems, all aimed at elevating the Quality of Service (QoS) in healthcare. This study seeks to bridge this gap by presenting a systematic and organized review of prevailing ML, DL, and distributed system algorithms as applied in healthcare settings. Within our work, we outline key challenges that both current and future developers may encounter, with a particular focus on aspects such as approach, data utilization, strategy, and development processes. Our study findings reveal that the Internet of Things (IoT) stands out as the most frequently utilized platform (44.3 %), with disease diagnosis emerging as the predominant healthcare application (47.8 %). Notably, discussions center significantly on the prevention and identification of cardiovascular diseases (29.2 %). The studies under examination employ a diverse range of ML and DL methods, along with distributed systems, with Convolutional Neural Networks (CNNs) being the most commonly used (16.7 %), followed by Long Short -Term Memory (LSTM) networks (14.6 %) and shallow learning networks (12.5 %). In evaluating QoS, the predominant emphasis revolves around the accuracy parameter (80 %). This study highlights how ML, DL, and distributed systems reshape healthcare. It contributes to advancing healthcare quality, bridging the gap between technology and medical adoption, and benefiting practitioners and patients.Review Citation - WoS: 11Citation - Scopus: 14Optimizing Exoskeleton Design with Evolutionary Computation: An Intensive Survey(Mdpi, 2023) Stroppa, Fabio; Soylemez, Aleyna; Yuksel, Huseyin Taner; Akbas, Baris; Sarac, MineExoskeleton devices are designed for applications such as rehabilitation, assistance, and haptics. Due to the nature of physical human-machine interaction, designing and operating these devices is quite challenging. Optimization methods lessen the severity of these challenges and help designers develop the device they need. In this paper, we present an extensive and systematic literature search on the optimization methods used for the mechanical design of exoskeletons. We completed the search in the IEEE, ACM, and MDPI databases between 2017 and 2023 using the keywords exoskeleton, design, and optimization. We categorized our findings in terms of which limb (i.e., hand, wrist, arm, or leg) and application (assistive, rehabilitation, or haptic) the exoskeleton was designed for, the optimization metrics (force transmission, workspace, size, and adjustability/calibration), and the optimization method (categorized as evolutionary computation or non-evolutionary computation methods). We discuss our observations with respect to how the optimization methods have been implemented based on our findings. We conclude our paper with suggestions for future research.
