Assistive devices are to be designed with the objective of use in daily-life as well as broad adoption by end users. In this context, it is necessary to tackle usability challenges by properly detecting and acting in accordance to user intents while minimizing the device installation complexity as well. In the case of physical assistive devices, using force/torque sensors is advantageous to detect user intent compared to EMG interfaces, but it remains difficult to correctly translate the detected intent into actuator motions. Focusing on upper-limb assistive robots, the user voluntary force is commonly used with a controller based on an admittance approach which leads to relatively poor reactivity and requires the user to develop force throughout the movement which can lead to fatigue, particularly for people with upper-limb impairments. This work proposes a Force-Triggered (FT) controller which can initiate and maintain movement only from short force impulses. The user voluntary forces are retrieved from total interaction forces by subtracting the passive component measured beforehand during a calibration phase. This paper presents the design of the proposed FT controller and its preliminary testing on pick-and-place tasks compared to an admittance strategy. This experiment was performed with one participant without impairment, equipped with an upper-limb exoskeleton prototype designed from recommendations of physical medicine therapists. This preliminary work highlights the potential of the proposed FT controller. Also, it provides directions for future work and clinical trials with end-users to assess the proposed FT approach usability while used alone or in the form of an hybrid controller between FT and admittance strategies.

Immersive environments provide opportunities to learn and transfer skills to real life. This opens up new areas of application, such as rehabilitation, where people with neurological disabilities can learn to drive a power wheelchair (PWC) through the development of immersive simulators. To expose these specific users to daily-life study interaction situations, it is important to ensure realistic interactions with the virtual humans that populate the simulated environment, as PWC users should learn to drive and navigate under everyday conditions. While non-verbal pedestrian-pedestrian interactions have been extensively studied, understanding pedestrian-PWC user interactions during locomotion is still an open research area. Our study aimed to investigate the regulation of interpersonal distance (i.e., proxemics) between a pedestrian and a PWC user in real and virtual situations. We designed 2 experiments in which 1) participants had to reach a goal by walking (respectively driving a PWC) and avoid a static PWC confederate (respectively a standing confederate) and 2) participants had to walk to a goal and avoid a static confederate seated on a PWC in real and virtual conditions. Our results showed that interpersonal distances were significantly different whether the pedestrian avoided the PWC user or vice versa. We also showed an influence of the orientation of the person to be avoided. We discuss these findings with respect to pedestrian-pedestrian interactions, as well as their implications for the design of virtual humans interacting with PWC users for rehabilitation applications. In particular, we proposed a proof of concept by adapting existing microscopic crowd simulation algorithms to consider the specificity of pedestrian-PWC user interactions.

This paper presents a generic power wheelchair dynamic model. As a first contribution, this paper proposes to use a generic model composed of a geometric model and a lumped model in order to be compliant with a wide range of existing commercially available wheelchairs. In this model, a set of essential parameters are enough to accurately replicate the dynamic behavior of a wheelchair. As a second contribution, this paper presents an identification method of a n-wheel type power wheelchair. The presented model is restricted to the sagittal plane only, which is sufficient to study the reliability of the identification and validation methods. Moreover, a Motion Cueing Algorithm based on the proposed model controls a simulator mechanical platform. The generic model has been then validated through a user study with 18 able-bodied participants evaluating the self-motion perception with our multisensory power wheelchair driving simulator. Results show that the simplified model is sufficient to provide accurate sensations to the user with respect to their experience while driving a power wheelchair.

Power wheelchairs are one of the main solutions for people with reduced mobility to maintain or regain autonomy and a comfortable and fulfilling life. However, driving a power wheelchair in a safe way is a difficult task that often requires training methods based on real-life situations. Although these methods are widely used in occupational therapy, they are often too complex to implement and unsuitable for some people with major difficulties. In this context, we collaborated with clinicians to develop a Virtual Reality based power wheelchair simulator. This simulator is an innovative training tool adapted to any type of situations and impairments. In this paper, we present a clinical study in which 29 power wheelchair regular users were asked to complete a clinically validated task designed by clinicians within two conditions: driving in a virtual environment with our simulator and driving in real conditions with a real power wheelchair. The objective of this study is to compare performances between the two conditions and to evaluate the Quality of Experience provided by our simulator in terms of Sense of Presence and Cybersickness. Results show that participants complete the tasks in a similar amount of time for both real and virtual conditions, using respectively a real power wheelchair and our simulator. Results also show that our simulator provides a high level of Sense of Presence and provokes only slight to moderate Cybersickness discomforts resulting in a valuable Quality of Experience.

Autonomy and the ability to maintain social activities can be challenging for people with disabilities experiencing reduced mobility. In the case of disabilities that impact mobility, power wheelchairs can help such people retain or regain autonomy. Nonetheless, driving a power wheelchair is a complex task that requires a combination of cognitive, visual and visuo-spatial abilities. In practice, people need to pass prior ability tests and driving training before being prescribed a power wheelchair by their therapist. Still, conventional training in occupational therapy can be insufficient for some people with severe cognitive and/or visuo-spatial functions. As such, these people are often prevented from obtaining a power wheelchair prescription from their therapist due to safety concerns. In this context, driving simulators might be efficient and promising tools to provide alternative, adaptive, flexible, and safe training. In previous work, we proposed a Virtual Reality (VR) driving simulator integrating vestibular feedback to simulate wheelchair motion sensations. The performance and acceptability of a VR simulator rely on satisfying user Quality of Experience (QoE). Therefore, our simulator is designed to give the user a high Sense of Presence (SoP) and low Cybersickness. This paper presents a pilot study assessing the impact of the vestibular feedback provied on user QoE. Participants were asked to perform a driving task whilst in the simulator under two conditions: with and without vestibular feedback. User QoE is assessed through subjective questionnaires measuring user SoP and cybersickness. The results show that vestibular feedback activation increases SoP and decreases cybersickness. This study constitutes a mandatory step before clinical trials and, as such, only enrolled people without disabilities.

Power wheelchairs allow people with motor disabilities to have more mobility and independence. However, driving safely such a vehicle is a daily challenge particularly in urban environments while navigating on sidewalks, negotiating curbs or dealing with uneven grounds. Indeed, differences of elevation have been reported to be one of the most challenging environmental barrier to negotiate, with tipping and falling being the most common accidents power wheelchair users encounter. It is thus our challenge to design assistive solutions for power wheelchair navigation in order to improve safety while navigating in such environments. To this aim, we propose a shared-control algorithm which provides assistance while navigating with a wheelchair in an environment consisting of negative obstacles. We designed a dedicated sensor-based control law allowing trajectory correction while approaching negative obstacles e.g. steps, curbs, descending slopes. This shared control proposed method takes into account the human-in-the loop factor. In this study, our solution the ability of our system to ensure a safe trajectory while navigating on a sidewalk is demonstrated through simulation, thus providing a proof-of-concept of our method.

Smart powered wheelchairs can increase mobility and independence for people with disability by providing navigation support. This support can be supplied in the form of autonomous or semi-autonomous obstacle avoidance systems. However, for rehabilitation or learning purposes, it would be of great benefit for wheelchair users to have a better understanding of the surrounding environment while driving. Therefore, another way of providing navigation support is to communicate information through a dedicated and adapted feedback interface. We here propose a framework in which feedback is provided by sending forces through the wheelchair controller as the user steers the wheelchair. This solution is based on a low complex optimization framework able to perform smooth trajectory correction and to provide obstacle avoidance. The impact of the proposed haptic guidance solution on user driving performance was assessed during this pilot study for validation purposes through an experiment with 4 able-bodied participants. They were asked to drive a power wheelchair on an obstacle course with and without activation of the force feedback. Results of this pilot study showed that the number of collisions significantly decreased while force feedback was activated, thus validating the proposed framework.

Motor or visual impairments may prevent a user from steering a wheelchair effectively in indoor environments. In such cases, joystick jerks arising from uncontrolled motions may lead to collisions with obstacles. We here propose a perceptive shared control system that progressively corrects the trajectory as a user manually drives the wheelchair, by means of a sensor-based shared control law capable of smoothly avoiding obstacles. This control law is based on a low complex optimization framework validated through simulations and extensive clinical trials. The provided model uses distance information. Therefore, for low-cost considerations, we use ultrasonic sensors to measure the distances around the wheelchair. The solution therefore provides an efficient assistive tool that does not alter the quality of experience perceived by the user, while ensuring his security in hazardous situations.