Design and Development of an Affordable Haptic Robot with Force-Feedback and Compliant Actuation to Improve Therapy for Patients with Severe Hemiparesis

The study describes the design and development of a single degree-of-freedom haptic robot, Haptic Theradrive, for post-stroke arm rehabilitation for in-home and clinical use. The robot overcomes many of the weaknesses of its predecessor, the TheraDrive system, that used a Logitech steering wheel as the haptic interface for rehabilitation. Although the original TheraDrive system showed success in a pilot study, its wheel was not able to withstand the rigors of use. A new haptic robot was developed that functions as a drop-in replacement for the Logitech wheel. The new robot can apply larger forces in interacting with the patient, thereby extending the functionality of the system to accommodate low-functioning patients. A new software suite offers appreciably more options for tailored and tuned rehabilitation therapies. In addition to describing the design of the hardware and software, the paper presents the results of simulation and experimental case studies examining the system\u27s performance and usability

MPC-Based Haptic Shared Steering System: A Driver Modeling Approach for Symbiotic Driving

Advanced Driver Assistance Systems (ADAS) aim to increase safety and reduce mental workload. However, the gap in the understanding of the closed-loop driver-vehicle interaction often leads to reduced user acceptance. In this study, an optimal torque control law is calculated online in the Model Predictive Control (MPC) framework to guarantee continuous guidance during the steering task. The research contribution is in the integration of an extensive prediction model covering cognitive behaviour, neuromuscular dynamics, and the vehicle- steering dynamics, within the MPC-based haptic controller to enhance collaboration. The driver model is composed of a preview cognitive strategy based on a Linear-Quadratic-Gaussian, sensory organs, and neuromuscular dynamics, including muscle co-activation and reflex action. Moreover, an adaptive cost-function algorithm enables dynamic allocation of the control authority. Experiments were performed in a fixed-base driving simulator at Toyota Motor Europe involving 19 participants to evaluate the proposed controller with two different cost functions against a commercial Lane Keeping Assist (LKA) system as an industry benchmark. The results demonstrate the proposed controller fosters symbiotic driving and reduces driver-vehicle conflicts with respect to a state-of-the-art commercial system, both subjectively and objectively, while still improving path-tracking performance. Summarising, this study tackles the need to blend human and ADAS control, demonstrating the validity of the proposed strategy

A Robust Wheel Interface With A Novel Adaptive Controller For Computer/robot-Assisted Motivating Rehabilitation

TheraDrive is a low-cost robotic system for post-stroke upper extremity rehabilitation. This system uses off-the-shelf computer gaming wheels with force feedback to help reduce motor impairment and improve function in the arms of stroke survivors. Preliminary results show that the TheraDrive system lacks a robust mechanical linkage that can withstand the forces exerted by patients, lacks a patient-specific adaptive controller to deliver personalized therapy, and is not capable of delivering effective therapy to severely low-functioning patients. A new low-cost, high-force haptic robot with a single degree of freedom has been developed to address these concerns. The resulting TheraDrive consists of an actuated hand crank with a compliant transmission. Actuation is provided by a brushed DC motor, geared to output up to 50 lbf (223 N) at the end effector. To enable safe human-machine interaction, a special compliant element was developed to function also as a failsafe torque limiter. A load cell is used to determine the human-machine interaction forces for use by the robot\u27s impedance controller. The impedance controller renders a virtual spring that attracts or repels the end effector from a moving target that the human must track during therapy exercises. As exercises are performed, an adaptive controller monitors patient performance and adjusts the spring stiffness to ensure that exercises are difficult but doable, which is important for maintaining patient motivation. Experiments with a computer model of a human and robot show the adaptive controller\u27s ability to maintain difficulty of exercises after a period of initial calibration. Seven human subjects (3 normal, 4 stroke-impaired) were used to test this system alongside the original TheraDrive system in order to compare both systems. Data showed that the new system produced a larger change in normalized trajectory tracking error when assistance/resistance was added to exercises when compared to the original TheraDrive. Data also showed that adaptive control led subject performance to be closer to a desired level. Motivation surveys showed no significant difference in subject motivation between the two systems. When asked to choose a preferred system, stroke subjects unanimously chose the new robot

Trust-Based Control of (Semi)Autonomous Mobile Robotic Systems

Despite great achievements made in (semi)autonomous robotic systems, human participa-tion is still an essential part, especially for decision-making about the autonomy allocation of robots in complex and uncertain environments. However, human decisions may not be optimal due to limited cognitive capacities and subjective human factors. In human-robot interaction (HRI), trust is a major factor that determines humans use of autonomy. Over/under trust may lead to dispro-portionate autonomy allocation, resulting in decreased task performance and/or increased human workload. In this work, we develop automated decision-making aids utilizing computational trust models to help human operators achieve a more effective and unbiased allocation. Our proposed decision aids resemble the way that humans make an autonomy allocation decision, however, are unbiased and aim to reduce human workload, improve the overall performance, and result in higher acceptance by a human. We consider two types of autonomy control schemes for (semi)autonomous mobile robotic systems. The first type is a two-level control scheme which includes switches between either manual or autonomous control modes. For this type, we propose automated decision aids via a computational trust and self-confidence model. We provide analytical tools to investigate the steady-state effects of the proposed autonomy allocation scheme on robot performance and human workload. We also develop an autonomous decision pattern correction algorithm using a nonlinear model predictive control to help the human gradually adapt to a better allocation pattern. The second type is a mixed-initiative bilateral teleoperation control scheme which requires mixing of autonomous and manual control. For this type, we utilize computational two-way trust models. Here, mixed-initiative is enabled by scaling the manual and autonomous control inputs with a function of computational human-to-robot trust. The haptic force feedback cue sent by the robot is dynamically scaled with a function of computational robot-to-human trust to reduce humans physical workload. Using the proposed control schemes, our human-in-the-loop tests show that the trust-based automated decision aids generally improve the overall robot performance and reduce the operator workload compared to a manual allocation scheme. The proposed decision aids are also generally preferred and trusted by the participants. Finally, the trust-based control schemes are extended to the single-operator-multi-robot applications. A theoretical control framework is developed for these applications and the stability and convergence issues under the switching scheme between different robots are addressed via passivity based measures

MPC-Based Haptic Shared Steering System: A Driver Modeling Approach for Symbiotic Driving

Advanced Driver Assistance Systems (ADAS) aim to increase safety and reduce mental workload. However, the gap in the understanding of the closed-loop driver-vehicle interaction often leads to reduced user acceptance. In this study, an optimal torque control law is calculated online in the Model Predictive Control (MPC) framework to guarantee continuous guidance during the steering task. The research contribution is in the integration of an extensive prediction model covering cognitive behaviour, neuromuscular dynamics, and the vehicle- steering dynamics, within the MPC-based haptic controller to enhance collaboration. The driver model is composed of a preview cognitive strategy based on a Linear-Quadratic-Gaussian, sensory organs, and neuromuscular dynamics, including muscle co-activation and reflex action. Moreover, an adaptive cost-function algorithm enables dynamic allocation of the control authority. Experiments were performed in a fixed-base driving simulator at Toyota Motor Europe involving 19 participants to evaluate the proposed controller with two different cost functions against a commercial Lane Keeping Assist (LKA) system as an industry benchmark. The results demonstrate the proposed controller fosters symbiotic driving and reduces driver-vehicle conflicts with respect to a state-of-the-art commercial system, both subjectively and objectively, while still improving path-tracking performance. Summarising, this study tackles the need to blend human and ADAS control, demonstrating the validity of the proposed strategy

Whole-Body Dynamic Telelocomotion: A Step-to-Step Dynamics Approach to Human Walking Reference Generation

Teleoperated humanoid robots hold significant potential as physical avatars for humans in hazardous and inaccessible environments, with the goal of channeling human intelligence and sensorimotor skills through these robotic counterparts. Precise coordination between humans and robots is crucial for accomplishing whole-body behaviors involving locomotion and manipulation. To progress successfully, dynamic synchronization between humans and humanoid robots must be achieved. This work enhances advancements in whole-body dynamic telelocomotion, addressing challenges in robustness. By embedding the hybrid and underactuated nature of bipedal walking into a virtual human walking interface, we achieve dynamically consistent walking gait generation. Additionally, we integrate a reactive robot controller into a whole-body dynamic telelocomotion framework. Thus, allowing the realization of telelocomotion behaviors on the full-body dynamics of a bipedal robot. Real-time telelocomotion simulation experiments validate the effectiveness of our methods, demonstrating that a trained human pilot can dynamically synchronize with a simulated bipedal robot, achieving sustained locomotion, controlling walking speeds within the range of 0.0 m/s to 0.3 m/s, and enabling backward walking for distances of up to 2.0 m. This research contributes to advancing teleoperated humanoid robots and paves the way for future developments in synchronized locomotion between humans and bipedal robots.Comment: 8 pages, 8 figure

Outils basés simulation pour la conception d'une protection haptique sur l'axe de roulis pour hélicoptère

International audienceThe latest evolution of pilot controllers, referred to as ASSU (Active Side Sticks Units) provides static and dynamic tactile force (or haptic) feedback to the pilot at the grip. Combined with FBW (fly-by-wire), this promising technology has enhanced safety levels compared to the original mechanical linkage systems they have started to replace, while offering vast improved benefits in terms of carefree handling and pilot situational awareness. In the framework of a PhD thesis, the Information Processing and Systems Department (DTIS) of ONERA and SAFRAN Electronics & Defense have started a cooperation to evaluate the interest and the different possibilities offered by the ASSU technology to improve safety and handling qualities of rotary wing aircraft. Up to now, the design and tuning of these functions were essentially performed thanks to numerous simulator sessions or flight tests with pilots. More than just providing a set of values for the required parameters defining the cueing function (hopefully an optimal set of parameters), it is expected that the approach presented here would reduce the number of piloted simulation tests and associated difficulties of the availability of pilots, the significant amount of time and material resources. The main objective of this work is to develop a design methodology based on the simulation of the entire helicopter control loop (also including the pilot in some form) and enabling the definition and parameterization of cueing functions. Moreover, some objective criteria will be defined and used to design the force feedback laws, bringing additional means of evaluation and validation than the classical subjective rating scales

The effect of haptic guidance, aging, and initial skill level on motor learning of a steering task

In a previous study, we found that haptic guidance from a robotic steering wheel can improve short-term learning of steering of a simulated vehicle, in contrast to several studies of other tasks that had found that the guidance either impairs or does not aid motor learning. In this study, we examined whether haptic guidance-as-needed can improve long-term retention (across 1 week) of the steering task, with age and initial skill level as independent variables. Training with guidance-as-needed allowed all participants to learn to steer without experiencing large errors. For young participants (age 18–30), training with guidance-as-needed produced better long-term retention of driving skill than did training without guidance. For older participants (age 65–92), training with guidance-as-needed improved long-term retention in tracking error, but not significantly. However, for a subset of less skilled, older subjects, training with guidance-as-needed significantly improved long-term retention. The benefits of guidance-based training were most evident as an improved ability to straighten the vehicle direction when coming out of turns. In general, older participants not only systematically performed worse at the task than younger subjects (errors ∼3 times greater), but also apparently learned more slowly, forgetting a greater percentage of the learned task during the 1 week layoffs between the experimental sessions. This study demonstrates that training with haptic guidance can benefit long-term retention of a driving skill for young and for some old drivers. Training with haptic guidance is more useful for people with less initial skill

Methods for Multiloop Identification of Visual and Neuromuscular Pilot Responses

In this paper, identification methods are proposed to estimate the neuromuscular and visual responses of a multiloop pilot model. A conventional and widely used technique for simultaneous identification of the neuromuscular and visual systems makes use of cross-spectral density estimates. This paper shows that this technique requires a specific noninterference hypothesis, often implicitly assumed, that may be difficult to meet during actual experimental designs. A mathematical justification of the necessity of the noninterference hypothesis is given. Furthermore, two methods are proposed that do not have the same limitations. The first method is based on autoregressive models with exogenous inputs, whereas the second one combines cross-spectral estimators with interpolation in the frequency domain. The two identification methods are validated by offline simulations and contrasted to the classic method. The results reveal that the classic method fails when the noninterference hypothesis is not fulfilled; on the contrary, the two proposed techniques give reliable estimates. Finally, the three identification methods are applied to experimental data from a closed-loop control task with pilots. The two proposed techniques give comparable estimates, different from those obtained by the classic method. The differences match those found with the simulations. Thus, the two identification methods provide a good alternative to the classic method and make it possible to simultaneously estimate human's neuromuscular and visual responses in cases where the classic method fails

Application of the H-Mode, a Design and Interaction Concept for Highly Automated Vehicles, to Aircraft

Driven by increased safety, efficiency, and airspace capacity, automation is playing an increasing role in aircraft operations. As aircraft become increasingly able to autonomously respond to a range of situations with performance surpassing human operators, we are compelled to look for new methods that help us understand their use and guide their design using new forms of automation and interaction. We propose a novel design metaphor to aid the conceptualization, design, and operation of highly-automated aircraft. Design metaphors transfer meaning from common experiences to less familiar applications or functions. A notable example is the "Desktop metaphor" for manipulating files on a computer. This paper describes a metaphor for highly automated vehicles known as the H-metaphor and a specific embodiment of the metaphor known as the H-mode as applied to aircraft. The fundamentals of the H-metaphor are reviewed followed by an overview of an exploratory usability study investigating human-automation interaction issues for a simple H-mode implementation. The envisioned application of the H-mode concept to aircraft is then described as are two planned evaluations