The ability to accomplish a sit-to-stand (STS) motion is key to increase functional mobility and reduce rehospitalization risks. While raising aid (transfer) devices and partial bodyweight support (rehabilitation) devices exist, both are unable to adjust the STS training to different mobility levels. Therefore, We have developed an STS training device that allows various configurations of impedance and vertical/forward forces to adapt to many training needs while maintaining commercial raising aid transfer capabilities. Experiments with healthy adults (both men and women) of various heights and weights show that the device 1) has a low impact on the natural STS kinematics, 2) can provide precise weight unloading at the patient's center of mass and 3) can add a forward virtual spring to assist the transfer of the bodyweight to the feet for seat-off, at the start of the STS motion. Keywords: Rehabilitation robotics, Force control, Human-robot interaction, Patient transfer, Floor-lift1. INTRODUCTION For patients in movement rehabilitation, accomplishing functional tasks is key to increasing quality of life and reducing the risk of rehospitalization [1, 2]. Training sit-to-stands (STS) is particularly useful as it has a significant correlation with increasing patient muscle power and the balance required to perform standing and walking tasks [3, 4]. Frequent training is essential to prevent muscle atrophy. However, studies indicate that up to 65% of patients hospitalized in short-term care for seven days or longer develop muscle weakness due to prolonged immobility [5]. This is partly due to the current shortage of qualified clinical staff in hospital settings [6]. Clinical staff can use passive lifts to assist the patient's STS motion, such as the Guldmann GPT1 or the ARJO Sara Stedy, which hold the patient's and knees in a fixed position to reduce fall risks. However, using passive lifts for STS training can be exhaustive and lead to injuries for the clinical staff since it relies on them to move the patient's center of mass (CoM) [7, 8].

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Abstract--This study introduces a haptic shared control framework designed to teach human drivers advanced driving skills. In this context, shared control refers to a driving mode where the human driver collaborates with an autonomous driving system to control the steering of a vehicle simultaneously. Advanced driving skills are those necessary to safely push the vehicle to its handling limits in high-performance driving such as racing and emergency obstacle avoidance. Previous research has demonstrated the performance and safety benefits of shared control schemes using both subjective and objective evaluations. However, these schemes have not been assessed for their impact on skill acquisition on complex and demanding tasks. Prior research on long-term skill acquisition either applies haptic shared control to simple tasks or employs other feedback methods like visual and auditory aids. T o bridge this gap, this study creates a cyber racing coach framework based on the haptic shared control paradigm and evaluates its performance in helping human drivers acquire high-performance driving skills. The framework introduces (1) an autonomous driving system that is capable of cooperating with humans in a highly performant driving scenario; and (2) a haptic shared control mechanism along with a fading scheme to gradually reduce the steering assistance from autonomy based on the human driver's performance during training. Two benchmarks are considered: self-learning (no assistance) and full assistance during training. Results from a human subject study indicate that the proposed framework helps human drivers develop superior racing skills compared to the benchmarks, resulting in better performance and consistency. Advanced driving skills refer to a set of competencies that go beyond basic driving abilities in terms of situational awareness, hazard perception, risk management, and vehicle handling [1]. They are crucial in high-performance driving tasks such as racing, and can also improve safety in everyday driving [1], [2]. This work has been submitted to the IEEE for possible publication.

In physical Human-Robot Collaboration (pHRC), accurate human intent estimation and rational human-robot role allocation are crucial for safe and efficient assistance. Existing methods that rely on short-term motion data for intention estimation lack multi-step prediction capabilities, hindering their ability to sense intent changes and adjust human-robot assignments autonomously, resulting in potential discrepancies. To address these issues, we propose a Dual Transformer-based Robot Trajectron (DTRT) featuring a hierarchical architecture, which harnesses human-guided motion and force data to rapidly capture human intent changes, enabling accurate trajectory predictions and dynamic robot behavior adjustments for effective collaboration. Specifically, human intent estimation in DTRT uses two Transformer-based Conditional Variational Autoencoders (CVAEs), incorporating robot motion data in obstacle-free case with human-guided trajectory and force for obstacle avoidance. Additionally, Differential Cooperative Game Theory (DCGT) is employed to synthesize predictions based on human-applied forces, ensuring robot behavior align with human intention. Compared to state-of-the-art (SOTA) methods, DTRT incorporates human dynamics into long-term prediction, providing an accurate understanding of intention and enabling rational role allocation, achieving robot autonomy and maneuverability. Experiments demonstrate DTRT's accurate intent estimation and superior collaboration performance.