Effect of Fixed and sEMG-Based Adaptive Shared Steering Control on Distracted Driver Behavior

Driver distraction is a well-known cause for traffic collisions worldwide. Studies have indicated that shared steering control, which actively provides haptic guidance torque on the steering wheel, effectively improves the performance of distracted drivers. Recently, adaptive shared steering control based on the forearm muscle activity of the driver has been developed, although its effect on distracted driver behavior remains unclear. To this end, a high-fidelity driving simulator experiment was conducted involving 18 participants performing double lane change tasks. The experimental conditions comprised two driver states: attentive and distracted. Under each condition, evaluations were performed on three types of haptic guidance: none (manual), fixed authority, and adaptive authority based on feedback from the forearm surface electromyography of the driver. Evaluation results indicated that, for both attentive and distracted drivers, haptic guidance with adaptive authority yielded lower driver workload and reduced lane departure risk than manual driving and fixed authority. Moreover, there was a tendency for distracted drivers to reduce grip strength on the steering wheel to follow the haptic guidance with fixed authority, resulting in a relatively shorter double lane change duration.

Omnidirectional Haptic Guidance for the Hearing Impaired to Track Sound Sources

We developed a hearing assistance system that enables hearing-impaired people to track the horizontal movement of a single sound source. The movement of the sound source is presented to the subject by vibrating vibrators on both shoulders according to the distance to and direction of the sound source, which are estimated from the acoustic signals detected by microphones attached to both ears. We presented the direction of and distance to the sound source to the subject by changing the ratio of the intensity of the two vibrators according to the direction and by increasing the intensity the closer the person got to the sound source. The subject could recognize the approaching sound source as a change in the vibration intensity by turning their face in the direction where the intensity of both vibrators was equal. The direction of the moving sound source can be tracked with an accuracy of less than 5° when an analog vibration pattern is added to indicate the direction of the sound source. By presenting the direction of the sound source with high accuracy, it is possible to show subjects the approach and departure of a sound source.

An Interactive Haptic Guidance System for Intuitive Programming CNC Machine Tool

The human-machine interfaces in modern CNC machine tools are not very intuitive and still based on archaic input systems, i.e., switches, handwheels, and buttons. This type of solution has two major drawbacks. The pushed button activates the movement only in one direction and is insensitive to the amount of the force exerted by the operator, which makes it difficult to move the machine axes at variable speeds. The paper proposes a novel and intuitive system of manual programming of a CNC machine tool based on a control lever with strain-gauge sensors. The presented idea of manual programming is aimed at eliminating the need to create a machining program and at making it possible to move the machine intuitively, eliminating mistakes in selecting directions and speeds. The article describes the concept of the system and the principle of operation of the control levers with force sensors. The final part of the work presents the experimental validation of the proposed system and a functionality comparison with the traditional CNC control.

Promoting Motor Variability During Robotic Assistance Enhances Motor Learning of Dynamic Tasks

Despite recent advances in robot-assisted training, the benefits of haptic guidance on motor (re)learning are still limited. While haptic guidance may increase task performance during training, it may also decrease participants' effort and interfere with the perception of the environment dynamics, hindering somatosensory information crucial for motor learning. Importantly, haptic guidance limits motor variability, a factor considered essential for learning. We propose that Model Predictive Controllers (MPC) might be good alternatives to haptic guidance since they minimize the assisting forces and promote motor variability during training. We conducted a study with 40 healthy participants to investigate the effectiveness of MPCs on learning a dynamic task. The task consisted of swinging a virtual pendulum to hit incoming targets with the pendulum ball. The environment was haptically rendered using a Delta robot. We designed two MPCs: the first MPC—end-effector MPC—applied the optimal assisting forces on the end-effector. A second MPC—ball MPC—applied its forces on the virtual pendulum ball to further reduce the assisting forces. The participants' performance during training and learning at short- and long-term retention tests were compared to a control group who trained without assistance, and a group that trained with conventional haptic guidance. We hypothesized that the end-effector MPC would promote motor variability and minimize the assisting forces during training, and thus, promote learning. Moreover, we hypothesized that the ball MPC would enhance the performance and motivation during training but limit the motor variability and sense of agency (i.e., the feeling of having control over their movements), and therefore, limit learning. We found that the MPCs reduce the assisting forces compared to haptic guidance. Training with the end-effector MPC increases the movement variability and does not hinder the pendulum swing variability during training, ultimately enhancing the learning of the task dynamics compared to the other groups. Finally, we observed that increases in the sense of agency seemed to be associated with learning when training with the end-effector MPC. In conclusion, training with MPCs enhances motor learning of tasks with complex dynamics and are promising strategies to improve robotic training outcomes in neurological patients.