Kevin Lynch

After a stroke, individuals often experience mobility impairments because of weakness and loss of independent joint control in the lower limbs. As a result, gait recovery becomes a primary goal of physical rehabilitation, traditionally achieved through high-intensity therapist-led training. However, conventional therapist-led approaches involving manual assistance or resistance can be physically demanding and limit interaction at multiple joints simultaneously. Robotic exoskeletons have emerged as a promising solution, enabling multijoint support, reducing therapist strain, and offering objective performance feedback. However, typical exoskeleton control strategies limit the physical therapist's involvement and adaptability to the patient's needs, which may hinder clinical adoption and outcomes. In this study, we introduce a gait rehabilitation paradigm based on physical human-robot-human interaction that we call therapist-exoskeleton-patient interaction (TEPI), in which a therapist and a patient with stroke are each equipped with a lower-limb exoskeleton virtually connected at the hips and knees via spring-damper elements. This connection enables bidirectional physical interaction, allowing the therapist to guide the patient's movement while receiving real-time haptic feedback. We evaluated this approach with eight patients with chronic stroke using a within-subject design, comparing TEPI training with conventional therapist-guided mobilization during treadmill walking. Results showed that, compared with conventional therapy, TEPI led to greater joint range of motion, increased step length and height, similar muscle activation, and high self-reported motivation and enjoyment. These findings suggest that TEPI can integrate robotic precision with therapist intuition, offering a framework for enhancing gait rehabilitation outcomes in populations recovering from stroke.

BACKGROUND: Lower-limb exoskeletons are a useful tool in rehabilitation settings as they can provide customized assistance to individuals during functional exercises. These approaches typically rely on state-machine-based control with impedance controllers tailored to different locomotion phases, ensuring appropriate assistance across various activities and environments. However, these methods necessitate lengthy calibration procedures, as many impedance parameters need to be fine-tuned to provide appropriate assistance for various activities (e.g., overground walking, ramps, and stairs). METHODS: This study presents three contributions: (1) a state-machine-based control strategy for partial assistance lower-limb exoskeletons, (2) a computational method to extract reference trajectories from a benchmark dataset (Camargo et al. in J Biomech 119:110320, 2021), enabling the identification of state-machine controller parameters and simplifying calibration procedures and (3) a dataset of 19 healthy individuals walking in five walking conditions (overground walking, upstairs, downstairs, up ramps, and down ramps) using either the state-machine approach or a transparent controller. RESULTS: The state-machine controller produced in average more negative interaction power ([Formula: see text] W/kg) compared to transparent control ([Formula: see text] W/kg), indicating greater user assistance. Preferred walking speed was notably faster with the state-machine controller, particularly on level ground, ramps and stairs ascent (25–32% increase). Kinematic analysis revealed closer alignment to able-bodied gait patterns with the state-machine controller, suggesting improved gait quality. At the same time, the dataset of the collected locomotion activities ( dataset link ) will constitute a new benchmark dataset for locomotion. CONCLUSIONS: In this work, we presented and evaluated a novel state-machine-based control strategy for partial-assistance lower-limb exoskeletons. In this approach, reference trajectories are extracted from a benchmark dataset, simplifying calibration procedures. Additionally, we provide a dataset of 19 healthy individuals using two exoskeleton controllers. The proposed controller will be applied to patient populations, while the dataset will serve as a valuable resource for advancing robust and effective control mechanisms through machine learning techniques.

For high-performance autonomous manipulation of a payload by a mobile manipulator team, or for collaborative manipulation with the human, robots should be able to discover where other robots are attached to the payload, as well as the payload's mass and inertial properties. In this paper, we describe a method for the robots to autonomously discover this information. The robots cooperatively manipulate the payload, and the twist, twist derivative, and wrench data at their grasp frames are used to estimate the transformation matrices between the grasp frames, the location of the payload's center of mass, and the payload's inertia matrix. The method is validated experimentally with a team of three mobile cobots, or mocobots.

This essay was originally printed in the 1955 issue of the Journal of Architectural Education, 10:1, 31-33The city is a very ancient kind of human environment. It has a history of five or six thous...

For high-performance autonomous manipulation of a payload by a mobile manipulator team, or for collaborative manipulation with the human, robots should be able to discover where other robots are attached to the payload, as well as the payload's mass and inertial properties. In this paper, we describe a method for the robots to autonomously discover this information. The robots cooperatively manipulate the payload, and the twist, twist derivative, and wrench data at their grasp frames are used to estimate the transformation matrices between the grasp frames, the location of the payload's center of mass, and the payload's inertia matrix. The method is validated experimentally with a team of three mobile cobots, or mocobots.

Haptic human-robot-human interaction allows users to feel and respond to one another's forces while interfacing with separate robotic devices, providing customizable infrastructure for studying physical interaction during motor tasks (e.g., physical rehabilitation). For upper- and lower-limb tracking tasks, previous work has shown that virtual interactions with a partner can improve motor performance depending on the skill level of each partner. However, whether the mechanism explaining these improvements is identical in the upper and lower limbs is an open question. In this work, we investigate the effects of haptic interaction between healthy individuals during a trajectory tracking task involving single-joint movements at the wrist and ankle. We compare tracking performance and muscle activation during haptic conditions where pairs of participants were uni- and bidirectionally connected to investigate the contribution of real-time responses from a partner during the interaction. Findings showed similar improvements in tracking performance during bidirectional interaction for both the wrist and ankle. This was observed despite distinct strategies in muscle co-contraction between joints, as co-contraction was dependent on partner ability for the wrist but not the ankle. For each joint, bidirectional and unidirectional interaction resulted in similar improvements for the worse partner in the dyad. For the better partner, bidirectional interaction resulted in greater improvements than unidirectional interaction. While these results suggest that unidirectional interaction is sufficient for error correction of less skilled individuals during simple motor tasks, they also highlight the mutual benefits of bidirectional interaction which are consistent across the upper and lower limbs.

Legged robot locomotion is hindered by a mismatch between applications where legs can outperform wheels or treads, most of which feature deformable substrates, and existing tools for planning and control, most of which assume flat, rigid substrates. In this study, we focus on the ramifications of plastic terrain deformation on the hop-to-hop energy dynamics of a spring-legged monopedal hopping robot animated by a switched-compliance energy injection controller. From this deliberately simple robot-terrain template, we derive a hop-to-hop energy return map, and we use physical experiments and simulations to validate the hop-to-hop energy map for a real robot hopping on a real deformable substrate. The dynamical properties (fixed points, eigenvalues, basins of attraction) of this map provide insights into efficient, responsive, and robust locomotion on deformable terrain. Specifically, we identify constant-fixed-point surfaces in a controller parameter space that suggest it is possible to tune control parameters for efficiency or responsiveness while targeting a desired gait energy level. We also identify conditions under which fixed points of the energy map are globally stable, and we further characterize the basins of attraction of fixed points when these conditions are not satisfied. We conclude by discussing the implications of this hop-to-hop energy map for planning, control, and estimation for efficient, agile, and robust legged locomotion on deformable terrain.

Abstract Haptic human-robot-human interaction allows users to feel and respond to one another’s forces while interfacing with separate robotic devices, providing customizable infrastructure for studying physical interaction during motor tasks (i.e., physical rehabilitation). For both upper- and lower-limb tasks, previous work has shown that virtual interactions with a partner can improve motor performance and enhance individual learning. However, whether the mechanism of these improvements generalizes across different human systems is an open question. In this work, we investigate the effects of haptic interaction between healthy individuals during a trajectory tracking task involving single-joint movements at the wrist and ankle. We compare tracking performance and muscle activation during haptic conditions where pairs of participants were uni- and bidirectionally connected, in order to investigate the contribution of real-time responses from a partner during the interaction. Findings indicate similar improvements in tracking performance during the bidirectional interaction for both the wrist and ankle, despite significant differences in how individuals modulated co-contraction. For each joint, bidirectional and unidirectional interaction resulted in similar improvements for the worse partner in the dyad. For the better partner, bidirectional interaction outperformed unidirectional interaction, likely due to changes in movement planning that were not observed in the unidirectional condition. While these results suggest that unidirectional interaction is sufficient for error correction of less skilled individuals during simple motor tasks, they also highlight the mutual benefits of bidirectional interaction which are consistent across the upper and lower limbs.

In the control of lower-limb exoskeletons with feet, the phase in the gait cycle can be identified by monitoring the weight distribution at the feet. This phase information can be used in the exoskeleton’s controller to compensate the dynamics of the exoskeleton and to assign impedance parameters. Typically the weight distribution is calculated using data from sensors such as treadmill force plates or insole force sensors. However, these solutions increase both the setup complexity and cost. For this reason, we propose a deep-learning approach that uses a short time window of joint kinematics to predict the weight distribution of an exoskeleton in real time. The model was trained on treadmill walking data from six users wearing a four-degree-of-freedom exoskeleton and tested in real time on three different users wearing the same device. This test set includes two users not present in the training set to demonstrate the model’s ability to generalize across individuals. Results show that the proposed method is able to fit the actual weight distribution with <inline-formula> <tex-math notation="LaTeX">$R^{2}=0.9$ </tex-math></inline-formula> and is suitable for real-time control with prediction times less than 1 ms. Experiments in closed-loop exoskeleton control show that deep-learning-based weight distribution estimation can be used to replace force sensors in overground and treadmill walking.

This work presents a novel rehabilitation framework designed for a therapist, wearing an inertial measurement unit (IMU) suit, to virtually interact with a lower-limb exoskeleton worn by a patient with motor impairments. This framework aims to harmonize the skills and knowledge of the therapist with the capabilities of the exoskeleton. The therapist can guide the patient's movements by moving their own joints and making real-time adjustments to meet the patient's needs, while reducing the physical effort of the therapist. This eliminates the need for a predefined trajectory for the patient to follow, as in conventional robotic gait training. For the virtual interaction medium between the therapist and patient, we propose an impedance profile that is stiff at low frequencies and less stiff at high frequencies, that can be tailored to individual patient needs and different stages of rehabilitation. The desired interaction torque from this medium is commanded to a whole-exoskeleton closed-loop compensation controller. The proposed virtual interaction framework was evaluated with a pair of unimpaired individuals in different teacher-student gait training exercises. Results show the proposed interaction control effectively transmits haptic cues, informing future applications in rehabilitation scenarios.

Human-swarm interaction is facilitated by a low-dimensional encoding of the swarm formation, independent of the (possibly large) number of robots. We propose using image moments to encode two-dimensional formations of robots. Each robot knows its pose and the desired formation moments, and simultaneously estimates the current moments of the entire swarm while controlling its motion to better achieve the desired group moments. The estimator is a distributed optimization, requiring no centralized processing, and self-healing, meaning that the process is robust to initialization errors, packet drops, and robots being added to or removed from the swarm. Our experimental results with a swarm of 50 robots, suffering nearly 50% packet loss, show that distributed estimation and control of image moments effectively achieves desired swarm formations.

Sit-to-Stand (StS) is a fundamental daily activity that can be challenging for stroke survivors due to strength, motor control, and proprioception deficits in their lower limbs. Existing therapies involve repetitive StS exercises, but these can be physically demanding for therapists while assistive devices may limit patient participation and hinder motor learning. To address these challenges, this work proposes the use of two lower-limb exoskeletons to mediate physical interaction between therapists and patients during a StS rehabilitative task. This approach offers several advantages, including improved therapist-patient interaction, safety enforcement, and performance quantification. The whole body control of the two exoskeletons transmits online feedback between the two users, but at the same time assists in movement and ensures balance, and thus helping subjects with greater difficulty. In this study we present the architecture of the framework, presenting and discussing some technical choices made in the design.

Medical device industry payments to healthcare organisations (HCOs) can create conflicts of interest which can undermine patient care. One way of addressing this concern is by enhancing transparency of industry financial support to HCOs. MedTech Europe, a medical device trade body, operate a system of disclosure of education payments to European HCOs. This study aimed to characterise payments reported in this database and to evaluate the disclosure system. An observational study of education-related payments to HCOs reported by the medical device industry in Europe was conducted. Data was manually extracted from transparentmedtech.eu. The primary outcome variable is the value of the payments, overall, and for each year, payment type, and country. The accessibility, availability and quality of the database was also analysed, using a proforma with 15 measures. Overall, 116 medical device companies reported education-related payments in 53 European and non-European countries, valuing over €425 million between 2017-2019, increasing in value between 2017-2019, from €93,798,419 to €175,414,302. Ten countries accounted for 94% of all payments and ten companies accounted for 80% of all payments. The accessibility, availability and quality of the database rated low for six measures, medium for six measures, and high for three measures. There is a large amount of education-related payments from medical device companies to European HCOs, creating substantial potential for conflicts of interest. MedTech Europe's disclosure system has many shortcomings. A European-wide publicly mandated disclosure system for both the medical device and pharmaceutical industries should be introduced. The medical device industry pay healthcare organisations (e.g. hospitals) large amounts of money. Industry states that this money is to help pay for healthcare professionals’ education. However, these payments can have a negative impact on healthcare professionals’ decision-making. This study sought to examine a website run by MedTech Europe, a representative body for the medical device industry, which outlines details of some of these payments (www.transparentmedtech.eu). Our analysis found that between 2017-2019 the medical device industry made ‘education’ payments valuing €425 million to healthcare organisations in Europe. We also assessed how comprehensive and user-friendly the database was and found a range of issues. For example, the database is not downloadable and some other important types of payments, such as payments for consultancy, are not included. We concluded that a mandatory database for both the medical device and pharmaceutical industry run by the European Union, would significantly improve transparency.

Data on education payments from medical device companies who are members of MedTech Europe. Data is aggregated by year/company/country. Source: transparentmedtech.eu

In the control of lower-limb exoskeletons with feet, the phase in the gait cycle can be identified by monitoring the weight distribution at the feet. This phase information can be used in the exoskeleton's controller to compensate the dynamics of the exoskeleton and to assign impedance parameters. Typically the weight distribution is calculated using data from sensors such as treadmill force plates or insole force sensors. However, these solutions increase both the setup complexity and cost. For this reason, we propose a deep-learning approach that uses a short time window of joint kinematics to predict the weight distribution of an exoskeleton in real time. The model was trained on treadmill walking data from six users wearing a four-degree-of-freedom exoskeleton and tested in real time on three different users wearing the same device. This test set includes two users not present in the training set to demonstrate the model's ability to generalize across individuals. Results show that the proposed method is able to fit the actual weight distribution with R2=0.9 and is suitable for real-time control with prediction times less than 1 ms. Experiments in closed-loop exoskeleton control show that deep-learning-based weight distribution estimation can be used to replace force sensors in overground and treadmill walking.

Controlling the interaction forces between a human and an exoskeleton is crucial for providing transparency or adjusting assistance or resistance levels. However, it is an open problem to control the interaction forces of lower-limb exoskeletons designed for unrestricted overground walking. For these types of exoskeletons, it is challenging to implement force/torque sensors at every contact between the user and the exoskeleton for direct force measurement. Moreover, it is important to compensate for the exoskeleton's whole-body gravitational and dynamical forces, especially for heavy lower-limb exoskeletons. Previous works either simplified the dynamic model by treating the legs as independent double pendulums, or they did not close the loop with interaction force feedback. The proposed whole-exoskeleton closed-loop compensation (WECC) method calculates the interaction torques during the complete gait cycle by using whole-body dynamics and joint torque measurements on a hip-knee exoskeleton. Furthermore, it uses a constrained optimization scheme to track desired interaction torques in a closed loop while considering physical and safety constraints. We evaluated the haptic transparency and dynamic interaction torque tracking of WECC control on three subjects. We also compared the performance of WECC with a controller based on a simplified dynamic model and a passive version of the exoskeleton. The WECC controller results in a consistently low absolute interaction torque error during the whole gait cycle for both zero and nonzero desired interaction torques. In contrast, the simplified controller yields poor performance in tracking desired interaction torques during the stance phase

Robot-mediated physical interaction between humans, or physical Human-Robot-Human Interaction (pHRHI for short) can transcend the physical constraints associated with direct human-human interaction such as constant interaction medium and the need for proximity. Control for pHRHI is related to bilateral teleoperation, but pHRHI must necessarily focus on human safety features on both sides of the interaction in a general setting. This paper presents a safety-aware framework for a robot mediated bilateral physical interaction between two humans in general setting while handling safety constraints with different priorities on two sides. The proposed framework also allows a user to haptically feel any number of constraints that are active on the other side, enabling a safe and intuitive interaction. Additionally, this approach is not dependent on the robot model and allows generalization across robots. We have formulated the safety-aware pHRHI problem as a hierarchical optimization framework with higher-priority safety tasks and lower-priority bilateral interaction and constraint communication tasks. We validated this framework by assessing the bilateral interaction, hierarchical constraint handling, and constraint communication performances on a dual human-robot setup. We also compared the performance with current approaches based on constrained optimization without any hierarchy or constraint communication feature. Results show that the proposed framework can handle prioritized physical/safety constraints, enabling bilateral interaction and providing a physical feeling of the other robot’s constraints.

Human-swarm interaction is facilitated by a low-dimensional encoding of the swarm formation, independent of the (possibly large) number of robots. We propose using image moments to encode two-dimensional formations of robots. Each robot knows its pose and the desired formation moments, and simultaneously estimates the current moments of the entire swarm while controlling its motion to better achieve the desired group moments. The estimator is a distributed optimization, requiring no centralized processing, and self-healing, meaning that the process is robust to initialization errors, packet drops, and robots being added to or removed from the swarm. Our experimental results with a swarm of 50 robots, suffering nearly 50% packet loss, show that distributed estimation and control of image moments effectively achieves desired swarm formations.

Legged robot locomotion is hindered by a mismatch between applications where legs can outperform wheels or treads, most of which feature deformable substrates, and existing tools for planning and control, most of which assume flat, rigid substrates. In this study we focus on the ramifications of plastic terrain deformation on the hop-to-hop energy dynamics of a spring-legged monopedal hopping robot animated by a switched-compliance energy injection controller. From this deliberately simple robot-terrain template, we derive a hop-to-hop energy return map, and we use physical experiments and simulations to validate the hop-to-hop energy map for a real robot hopping on a real deformable substrate. The dynamical properties (fixed points, eigenvalues, basins of attraction) of this map provide insights into efficient, responsive, and robust locomotion on deformable terrain. Specifically, we identify constant-fixed-point surfaces in a controller parameter space that suggest it is possible to tune control parameters for efficiency or responsiveness while targeting a desired gait energy level. We also identify conditions under which fixed points of the energy map are globally stable, and we further characterize the basins of attraction of fixed points when these conditions are not satisfied. We conclude by discussing the implications of this hop-to-hop energy map for planning, control, and estimation for efficient, agile, and robust legged locomotion on deformable terrain.

Sit-to-Stand (StS) is a fundamental daily activity that can be challenging for stroke survivors due to strength, motor control, and proprioception deficits in their lower limbs. Existing therapies involve repetitive StS exercises, but these can be physically demanding for therapists while assistive devices may limit patient participation and hinder motor learning. To address these challenges, this work proposes the use of two lower-limb exoskeletons to mediate physical interaction between therapists and patients during a StS rehabilitative task. This approach offers several advantages, including improved therapist-patient interaction, safety enforcement, and performance quantification. The whole body control of the two exoskeletons transmits online feedback between the two users, but at the same time assists in movement and ensures balance, and thus helping subjects with greater difficulty. In this study we present the architecture of the framework, presenting and discussing some technical choices made in the design.

Physical interaction between individuals plays an important role in human motor learning and performance during shared tasks. Using robotic devices, researchers have studied the effects of dyadic haptic interaction mostly focusing on the upper-limb. Developing infrastructure that enables physical interactions between multiple individuals' lower limbs can extend the previous work and facilitate investigation of new dyadic lower-limb rehabilitation schemes. We designed a system to render haptic interactions between two users while they walk in multi-joint lower-limb exoskeletons. Specifically, we developed an infrastructure where desired interaction torques are commanded to the individual lower-limb exoskeletons based on the users' kinematics and the properties of the virtual coupling. In this pilot study, we demonstrated the capacity of the platform to render different haptic properties (e.g., soft and hard), different haptic connection types (e.g., bidirectional and unidirectional), and connections expressed in joint space and in task space. With haptic connection, dyads generated synchronized movement, and the difference between joint angles decreased as the virtual stiffness increased. This is the first study where multi-joint dyadic haptic interactions are created between lower-limb exoskeletons. This platform will be used to investigate effects of haptic interaction on motor learning and task performance during walking, a complex and meaningful task for gait rehabilitation.

We describe SH-SVL, a parameterized family of first-order distributed optimization algorithms that enable a network of agents to collaboratively calculate a decision variable that minimizes the sum of cost functions at each agent. These algorithms are self-healing in that their convergence to the correct optimizer can be guaranteed even if they are initialized randomly, agents join or leave the network, or local cost functions change. We also present simulation evidence that our algorithms are self-healing in the case of dropped communication packets. Our algorithms are the first single-Laplacian methods for distributed convex optimization to exhibit all of these characteristics. We achieve self-healing by sacrificing internal stability, a fundamental trade-off for single-Laplacian methods.

Physical interaction between individuals plays an important role in human motor learning and performance during shared tasks. Using robotic devices, researchers have studied the effects of dyadic haptic interaction mostly focusing on the upper-limb. Developing infrastructure that enables physical interactions between multiple individuals' lower limbs can extend the previous work and facilitate investigation of new dyadic lower-limb rehabilitation schemes. We designed a system to render haptic interactions between two users while they walk in multi-joint lower-limb exoskeletons. Specifically, we developed an infrastructure where desired interaction torques are commanded to the individual lower-limb exoskeletons based on the users' kinematics and the properties of the virtual coupling. In this pilot study, we demonstrated the capacity of the platform to render different haptic properties (e.g., soft and hard), different haptic connection types (e.g., bidirectional and unidirectional), and connections expressed in joint space and in task space. With haptic connection, dyads generated synchronized movement, and the difference between joint angles decreased as the virtual stiffness increased. This is the first study where multi-joint dyadic haptic interactions are created between lower-limb exoskeletons. This platform will be used to investigate effects of haptic interaction on motor learning and task performance during walking, a complex and meaningful task for gait rehabilitation.

&lt;p&gt;While treating sensorimotor impairments, a therapist may provide physical assistance by guiding their patient's limb to teach a desired movement. In this scenario, a key aspect is the compliance of the interaction, as the therapist can provide subtle cues or impose a movement as demonstration. One approach to studying these interactions involves haptically connecting two individuals through robotic interfaces. Upper-limb studies have shown that pairs of connected individuals estimate one another's goals during tracking tasks by exchanging haptic information, resulting in improved performance dependent on the ability of one's partner and the stiffness of the virtual connection. In this study, our goal was to investigate whether these findings generalize to the lower-limb during an ankle tracking task. Pairs of healthy participants (i.e., dyads) independently tracked target trajectories with and without connections rendered between two ankle robots. We tested the effects of connection stiffness as well as visual noise to manipulate the correlation of tracking errors between partners. In our analysis, we compared changes in task performance across conditions while tracking with and without the connection. We found that tracking improvements while connected increased with connection stiffness, favoring the worse partner in the dyad during hard connections. We modeled the interaction as three springs in series, considering the stiffness of the connection and each partners' ankle, to show that improvements were likely due to a cancellation of random tracking errors between partners. These results suggest a simplified mechanism of improvements compared to what has been reported during upper-limb dyadic tracking.&lt;/p&gt;

While treating sensorimotor impairments, a therapist may provide physical assistance by guiding their patient’s limb to teach a desired movement. In this scenario, a key aspect is the compliance of the interaction, as the therapist can provide subtle cues or impose a movement as demonstration. One approach to studying these interactions involves haptically connecting two individuals through robotic interfaces. Upper-limb studies have shown that pairs of connected individuals estimate one another’s goals during tracking tasks by exchanging haptic information, resulting in improved performance dependent on the ability of one’s partner and the stiffness of the virtual connection. In this study, our goal was to investigate whether these findings generalize to the lower-limb during an ankle tracking task. Pairs of healthy participants (i.e., dyads) independently tracked target trajectories with and without connections rendered between two ankle robots. We tested the effects of connection stiffness as well as visual noise to manipulate the correlation of tracking errors between partners. In our analysis, we compared changes in task performance across conditions while tracking with and without the connection. We found that tracking improvements while connected increased with connection stiffness, favoring the worse partner in the dyad during hard connections. We modeled the interaction as three springs in series, considering the stiffness of the connection and each partners’ ankle, to show that improvements were likely due to a cancellation of random tracking errors between partners. These results suggest a simplified mechanism of improvements compared to what has been reported during upper-limb dyadic tracking.

Abstract Background Medical device industry payments to healthcare organisations (HCOs) can create conflicts of interest which can undermine patient care. One way of addressing this concern is by enhancing transparency of industry financial support to HCOs. MedTech Europe, a medical device trade body, operate a system of disclosure of education payments to European HCOs. This study aimed to characterise payments reported in this database and to evaluate the disclosure system. Methods An observational study of education-related payments to HCOs reported by the medical device industry in Europe was conducted. Data was manually extracted from transparentmedtech.eu. The primary outcome variable is the value of the payments, overall, and for each year, payment type, and country. The accessibility, availability and quality of the database was also analysed, using a proforma with 15 measures. Findings Overall, 116 medical device companies reported education-related payments in 53 countries, valuing over €420 million between 2017-2019, increasing in value between 2017-2019, from €91,289,672 to €175,414,302. Ten countries accounted for 94% of all payments and ten companies accounted for 80% of all payments. The accessibility, availability and quality of the database, rated low for six measures, medium for six measures and high for three measures. Interpretation There is a large amount of education-related payments from medical device companies to European HCOs, creating substantial potential for conflicts of interest. MedTech Europe’s disclosure system has many shortcomings. A European-wide publicly mandated disclosure system for both the medical device and pharmaceutical industries should be introduced. Funding Swedish Research Council (SM, PO)

Data on education payments from medical device companies who are members of MedTech Europe. Data is aggregated by year/company/country. Source: transparentmedtech.eu

In this article, we define “robotic contact juggling” to be the purposeful control of the motion of a 3-D smooth object as it rolls freely on a motion-controlled robot manipulator, or “hand.” While specific examples of robotic contact juggling have been studied before, in this article, we provide the first general formulation and solution method for the case of an arbitrary smooth object in a single-point rolling contact on an arbitrary smooth hand. Our formulation splits the problem into four subproblems: deriving the second-order rolling kinematics; deriving the 3-D rolling dynamics; planning rolling motions that satisfy the rolling dynamics and achieve the desired goal; and stabilization of planned rolling trajectories. The theoretical results are demonstrated in 3-D simulations and 2-D experiments using feedback from a high-speed vision system.

Controlling the interaction forces between a human and an exoskeleton is crucial for providing transparency or adjusting assistance or resistance levels. However, it is an open problem to control the interaction forces of lower-limb exoskeletons designed for unrestricted overground walking. For these types of exoskeletons, it is challenging to implement force/torque sensors at every contact between the user and the exoskeleton for direct force measurement. Moreover, it is important to compensate for the exoskeleton's whole-body gravitational and dynamical forces, especially for heavy lower-limb exoskeletons. Previous works either simplified the dynamic model by treating the legs as independent double pendulums, or they did not close the loop with interaction force feedback. The proposed whole-exoskeleton closed-loop compensation (WECC) method calculates the interaction torques during the complete gait cycle by using whole-body dynamics and joint torque measurements on a hip-knee exoskeleton. Furthermore, it uses a constrained optimization scheme to track desired interaction torques in a closed loop while considering physical and safety constraints. We evaluated the haptic transparency and dynamic interaction torque tracking of WECC control on three subjects. We also compared the performance of WECC with a controller based on a simplified dynamic model and a passive version of the exoskeleton. The WECC controller results in a consistently low absolute interaction torque error during the whole gait cycle for both zero and nonzero desired interaction torques. In contrast, the simplified controller yields poor performance in tracking desired interaction torques during the stance phase.

While treating sensorimotor impairments, a therapist may provide physical assistance by guiding their patient's limb to teach a desired movement. In this scenario, a key aspect is the compliance of the interaction, as the therapist can provide subtle cues or impose a movement as demonstration. One approach to studying these interactions involves haptically connecting two individuals through robotic interfaces. Upper-limb studies have shown that pairs of connected individuals estimate one another's goals during tracking tasks by exchanging haptic information, resulting in improved performance dependent on the ability of one's partner and the stiffness of the virtual connection. In this study, our goal was to investigate whether these findings generalize to the lower limb during an ankle tracking task. Pairs of healthy participants (i.e., dyads) independently tracked target trajectories with and without connections rendered between two ankle robots. We tested the effects of connection stiffness as well as visual noise to manipulate the correlation of tracking errors between partners. In our analysis, we compared changes in task performance across conditions while tracking with and without the connection. We found that tracking improvements while connected increased with connection stiffness, favoring the worse partner in the dyad during hard connections. We modeled the interaction as three springs in series, considering the stiffness of the connection and each partners' ankle, to show that improvements were likely due to a cancellation of random tracking errors between partners. These results suggest a simplified mechanism of improvements compared to what has been reported during upper-limb dyadic tracking.