Haptic Feedback Perception and Learning With Cable-Driven Guidance in Exosuit Teleoperation of a Simulated Drone

Skin-interfaced multimodal sensing and tactile feedback system as enhanced human-machine interface for closed-loop drone control

Unmanned aerial vehicles have undergone substantial development and market growth recently. With research focusing on improving control strategies for better user experience, feedback systems, which are vital for operator awareness of surroundings and flight status, remain underdeveloped. Current bulky manipulators also hinder accuracy and usability. Here, we present an enhanced human-machine interface based on skin-integrated multimodal sensing and feedback devices for closed-loop drone control. This system captures hand gestures for intuitive, rapid, and precise control. An integrated tactile actuator array translates the drone's posture into two-dimensional tactile information, enhancing the operator's perception of the flight situation. Integrated obstacle detection and neuromuscular electrical stimulation-based force feedback system enable collision avoidance and flight path correction. This closed-loop system combines intuitive controls and multimodal feedback to reduce training time and cognitive load while improving flight stability, environmental awareness, and the drone's posture. The use of stretchable electronics also addresses wearability and bulkiness issues in traditional systems, advancing human-machine interface design.

Learning Effect in Joystick Tactile Guidance

Haptic feedback is a method to provide tactile guidance in scenarios requiring multiple senses and divided attention like aviation. Earlier tests on a flight simulator and an in-flight test using the proposed tactile guidance method have shown the need to study its learning process. In this study, twelve participants completed two tactile guidance tasks without visual feedback across twelve sessions to analyze the learning effect. The paper shows an improvement between sessions in guidance accuracy, response time, and self-assessed workload. On the other hand, reaction delay is not affected by the training. The percentage improvement between the initial and trained skills reached 30% in guidance accuracy performance.

Wearable Drone Controller: Machine Learning-Based Hand Gesture Recognition and Vibrotactile Feedback

We proposed a wearable drone controller with hand gesture recognition and vibrotactile feedback. The intended hand motions of the user are sensed by an inertial measurement unit (IMU) placed on the back of the hand, and the signals are analyzed and classified using machine learning models. The recognized hand gestures control the drone, and the obstacle information in the heading direction of the drone is fed back to the user by activating the vibration motor attached to the wrist. Simulation experiments for drone operation were performed, and the participants' subjective evaluations regarding the controller's convenience and effectiveness were investigated. Finally, experiments with a real drone were conducted and discussed to validate the proposed controller.

Haptics in Teleoperated Medical Interventions: Force Measurement, Haptic Interfaces and Their Influence on User's Performance

OBJECTIVES

Haptics in teleoperated medical interventions enables measurement and transfer of force information to the operator during robot-environment interaction. This paper provides an overview of the current research in this domain and guidelines for future investigations.

METHODS

We review current technologies in force measurement and haptic devices as well as their experimental evaluation and influence on user's performance.

RESULTS

Force sensing is moving away from the conventional proximal measurement methods to distal sensing and contact-less methods. Wearable devices that deliver haptic feedback on different body parts are increasingly playing an important role. Performance and accuracy improvement are the widely reported benefits of haptic feedback, while there is a debate on its effect on task completion time and exerted force.

CONCLUSION

With the surge of new ideas, there is a need for better and more systematic validation of the new sensing and feedback technology, through better user studies and novel methods like validated benchmarks and new taxonomies.

SIGNIFICANCE

This review investigates haptics from sensing to interfaces within the context of user's performance and the validation procedures to highlight salient advances. It provides guidelines to future developments and highlights the shortcomings in the field.