Yun Seong Song

This study examines human arm kinematics during overground physical human-robot interaction (pHRI). Previous work showed humans adjust arm stiffness with changing trajectory uncertainty, but the roles of arm kinematics and muscle activation remained unclear. Building on a preliminary study, we analyzed arm movements with more participants (10 individuals) to achieve more reliable findings and examined two potential influences that arose in the preliminary study: the robot’s perturbation effect (a brief hand push) and left-to-right trunk sway. Using a linear mixed-effects model, we evaluated the effects of participant, block, and trajectory condition on arm angles. Results showed minimal kinematic contribution to stiffness modulation, with inconsistent significance levels in the measured metrics. Perturbation presence also had no significant impact on voluntary posture, with the exception of the posture differences before and after the perturbation. Trunk sway was strongly correlated with elbow angle, with a mean correlation ( R ² ) of 0.65 and a standard deviation of 0.24. Most variability arose from individual differences rather than experimental conditions. These findings might potentially allow for more flexible mechanical design in assistive and rehabilitation robots.

The vision of advancing robots capable of direct physical engagement with humans begins with an understanding of how humans interact with each other. Even rudimentary interactive tasks, such as shaking hands, provide tremendous challenges for modern robotics, and current research into the biomechanics of such interactions is limited. Thus, further analysis of physical human–human interaction (HHI) will bolster efforts to enhance collaborative robot capabilities. To this end, a custom force perturbation handle (FPH) was developed, including the requisite mechanical, electrical, and software systems, to investigate the unique role of arm stiffness modulation in haptic communication between humans. The novel device records force and torque values generated by an interacting dyad and administers controlled force perturbations for estimating arm impedance metrics while maintaining a compact configuration that facilitates natural manipulation during pHHI tasks. To evaluate the capabilities of the FPH, a pilot experiment was conducted where subjects were instructed to exhibit specified levels of arm stiffness to which the calculated values from the FPH were compared. The results from four unbalanced two-way analysis of variance (ANOVA) analyses indicated a strong correspondence between the subjects' stiffness instruction and the estimated values from the FPH. The data were further validated through a qualitative analysis of arm displacement and actuation length. Considerations were addressed for deploying the FPH in future dynamic pHHI tasks to acquire valuable insight into motor communication strategies between humans and its applications for future interactive robotics.

Industry 4.0 has created a growing need for effective human-robot collaboration (HRC). As robots and humans work more closely together, efficient communication becomes essential for coordinating their actions seamlessly. While speech may seem like the obvious choice for communication, noisy factory environments can render it impractical. Additionally, workers often have their hands occupied with assembly tasks, making hand-controlled interfaces less practical for controlling robots. To address these challenges, this paper presents a novel, hands-free method for robot control using electrooculography (EOG) signals–specifically, eye movements and blinks–with unmanned aerial vehicles (UAVs) used as the demonstration platform. We developed a real-time system that captures EOG signals through a graphical user interface (GUI), allowing users to direct their gaze toward on-screen commands and confirm selections with blinks. An LSTM (Long Short-Term Memory) model was trained to detect gaze coordinates and blink events from the EOG data. For performance benchmarking, we implemented a vision-based video-oculography (VOG) system as a camera-based baseline and compared both approaches under various conditions, including low light, wind disturbances, and deployment on embedded hardware as a resource-constrained device. Our comparative analysis showed the EOG-based method achieved 97.6% accuracy in command detection under normal conditions and maintained strong performance across challenging environments where the vision-based VOG system experienced significant degradation. The proposed EOG method also operated in real time at 47.97 FPS on a high-performance workstation and 38.87 FPS on the resource-constrained NVIDIA Jetson Orin Nano. On this platform, it ran 13.9× faster and used 319× less physical memory than the VOG-based alternative. These results underscore the advantages of EOG-based control in delivering reliable, real-time performance on both high performance workstations and resource-constrained platforms, outperforming vision-based alternatives in efficiency and adaptability.

Many anticipated physical human-robot interaction (pHRI) applications in the near future are overground tasks such as walking assistance. For investigating the biomechanics of human movement during pHRI, this work presents Ophrie, a novel interactive robot dedicated for physical interaction tasks with a human in overground settings. Unique design requirements for pHRI were considered in implementing the one-arm mobile robot, such as the low output impedance and the ability to apply small interaction forces. The robot can measure the human arm stiffness, an important physical quantity that can reveal human biomechanics during overground pHRI, while the human walks alongside the robot. This robot is anticipated to enable novel pHRI experiments and advance our understanding of intuitive and effective overground pHRI.

Humans can physically interact with other humans adeptly. Some overground interaction tasks, such as guiding a partner across a room, occur without visual and verbal communication, which suggests that the information exchanges occur through sensing movements and forces. To understand the process of motor communication during overground physical interaction, we hypothesized that humans modulate the mechanical properties of their arms for increased awareness and sensitivity to ongoing interaction. For this, we used an overground interactive robot to guide a human partner across one of three randomly chosen paths while occasionally providing force perturbations to measure the arm stiffness. We observed that the arm stiffness was lower at instants when the robot’s upcoming trajectory was unknown compared to instants when it was predicable - the first evidence of arm stiffness modulation for better motor communication during overground physical interaction.

Understanding the human motor control strategy during physical interaction tasks is crucial for developing future robots for physical human–robot interaction (pHRI). In physical human–human interaction (pHHI), small interaction forces are known to convey their intent between the partners for effective motor communication. The aim of this work is to investigate what affects the human’s sensitivity to the externally applied interaction forces. The hypothesis is that one way the small interaction forces are sensed is through the movement of the arm and the resulting proprioceptive signals. A pHRI setup was used to provide small interaction forces to the hand of seated participants in one of four directions, while the participants were asked to identify the direction of the push while blindfolded. The result shows that participants’ ability to correctly report the direction of the interaction force was lower with low interaction force as well as with high muscle contraction. The sensitivity to the interaction force direction increased with the radial displacement of the participant’s hand from the initial position: the further they moved the more correct their responses were. It was also observed that the estimated stiffness of the arm varies with the level of muscle contraction and robot interaction force.

Designing robots for physical interaction with humans pose unique design challenges. One such challenge is for the robots to be able to provide a wide range of mechanical impedance. In particular, for the design of an interactive robot for measuring human arm impedance during overground tasks, it is crucial for the robot output impedance to be low. Even though, with the help of

We present a new design method that is tailored for designing a physical interactive robotic arm for overground physical interaction. Designing such robotic arms present various unique requirements that differ from existing robotic arms, which are used for general manipulation, such as being able to generate required forces at every point inside the workspace and/or having low intrinsic mechanical impedance. Our design method identifies these requirements and categorizes them into kinematic and dynamic characteristics of the robot and then ensures that these unique considerations are satisfied in the early design phase. The robot’s capability for use in such tasks is analyzed using mathematical simulations of the designed robot, and discussion of its dynamic characteristics is presented. With our proposed method, the robot arm is ensured to perform various overground interactive tasks with a human.

Light Touch (LT) has been shown to reduce postural sway in a wide range of populations. While LT is believed to provide additional sensory information for balance modulation, the nature of this information and its specific effect on balance are yet unclear. In order to better understand LT and to potentially harness its advantages for a practical balance aid, we investigated the effect of LT as provided by a haptic robot. Postural sway during standing balance was reduced when the LT force (~ 1 N) applied to the high back area was dependent on the trunk velocity. Additional information on trunk position, provided through orthogonal vibrations, further reduced the sway position-metric of balance but did not further improve the velocity-metric of balance. Our results suggest that limited and noisy information on trunk velocity encoded in LT is sufficient to influence standing balance.