Background Wearable assistive devices have demonstrated the potential to improve mobility outcomes for individuals with disabilities, and to augment healthy human performance; however, these benefits depend on how effectively power is transmitted from the device to the human user. Quantifying and understanding this power transmission is challenging due to complex human-device interface dynamics that occur as biological tissues and physical interface materials deform and displace under load, absorbing and returning power.
Methods Here we introduce a new methodology for quickly estimating interface power dynamics during movement tasks using common motion capture and force measurements, and then apply this method to quantify how a soft robotic ankle exosuit interacts with and transfers power to the human body during walking. We partition exosuit end-effector power (i.e., power output from the device) into power that augments ankle plantarflexion (termed augmentation power) vs. power that goes into deformation and motion of interface materials and underlying soft tissues (termed interface power).
Results We provide empirical evidence of how human-exosuit interfaces absorb and return energy, reshaping exosuit-to-human power flow and resulting in three key consequences: (i) During exosuit loading (as applied forces increased), about 55% of exosuit end-effector power was absorbed into the interfaces. (ii) However, during subsequent exosuit unloading (as applied forces decreased) most of the absorbed interface power was returned viscoelastically. Consequently, the majority (about 75%) of exosuit end-effector work over each stride contributed to augmenting ankle plantarflexion. (iii) Ankle augmentation power (and work) was delayed relative to exosuit end-effector power, due to these interface energy absorption and return dynamics.
Conclusions Our findings elucidate the complexities of human-exosuit interface dynamics during transmission of power from assistive devices to the human body, and provide insight into improving the design and control of wearable robots. We conclude that in order to optimize the performance of wearable assistive devices it is important, throughout design and evaluation phases, to account for human-device interface dynamics that affect power transmission and thus human augmentation benefits.
We introduce an implantable intracardiac soft robotic right ventricular ejection device (RVED) for dynamic approximation of the right ventricular (RV) free wall and the interventricular septum (IVS) in synchrony with the cardiac cycle to augment blood ejection in right heart failure (RHF). The RVED is designed for safe and effective intracardiac operation and consists of an anchoring system deployed across the IVS, an RV free wall anchor, and a pneumatic artificial muscle linear actuator that spans the RV chamber between the two anchors. Using a ventricular simulator and a custom controller, we characterized ventricular volume ejection, linear approximation against different loads and the effect of varying device actuation periods on volume ejection. The RVED was then tested in vivo in adult pigs (n = 5). First, we successfully deployed the device into the beating heart under 3D echocardiography guidance (n = 4). Next, we performed a feasibility study to evaluate the device's ability to augment RV ejection in an experimental model of RHF (n = 1). RVED actuation augmented RV ejection during RHF; while further chronic animal studies will provide details about the efficacy of this support device. These results demonstrate successful design and implementation of the RVED and its deployment into the beating heart. This soft robotic ejection device has potential to serve as a rapidly deployable system for mechanical circulatory assistance in RHF.
When defining requirements for any wearable robot for walking assistance, it is important to maximize the user’s metabolic benefit resulting from the exosuit assistance while limiting the metabolic penalty of carrying the system’s mass. Thus, the aim of this study was to isolate and characterize the relationship between assistance magnitude and the metabolic cost of walking while also examining changes to the wearer’s underlying gait mechanics. The study was performed with a tethered multiarticular soft exosuit during normal walking, where assistance was directly applied at the ankle joint and indirectly at the hip due to a textile architecture. The exosuit controller was designed such that the delivered torque profile at the ankle joint approximated that of the biological torque during normal walking. Seven participants walked on a treadmill at 1.5 meters per second under one unpowered and four powered conditions, where the peak moment applied at the ankle joint was varied from about 10 to 38% of biological ankle moment (equivalent to an applied force of 18.7 to 75.0% of body weight). Results showed that, with increasing exosuit assistance, net metabolic rate continually decreased within the tested range. When maximum assistance was applied, the metabolic rate of walking was reduced by 22.83 ± 3.17% relative to the powered-off condition (mean ± SEM).
Soft actuators are the components responsible for producing motion in soft robots. Although soft actuators have allowed for a variety of innovative applications, there is a need for design tools that can help to efficiently and systematically design actuators for particular functions. Mathematical modeling of soft actuators is an area that is still in its infancy but has the potential to provide quantitative insights into the response of the actuators. These insights can be used to guide actuator design, thus accelerating the design process. Here, we study fluid-powered fiber-reinforced actuators, because these have previously been shown to be capable of producing a wide range of motions. We present a design strategy that takes a kinematic trajectory as its input and uses analytical modeling based on nonlinear elasticity and optimization to identify the optimal design parameters for an actuator that will follow this trajectory upon pressurization. We experimentally verify our modeling approach, and finally we demonstrate how the strategy works, by designing actuators that replicate the motion of the index finger and thumb.
There is much interest in form-fitting, low-modulus, implantable devices or soft robots that can mimic or assist in complex biological functions such as the contraction of heart muscle. We present a soft robotic sleeve that is implanted around the heart and actively compresses and twists to act as a cardiac ventricular assist device. The sleeve does not contact blood, obviating the need for anticoagulation therapy or blood thinners, and reduces complications with current ventricular assist devices, such as clotting and infection. Our approach used a biologically inspired design to orient individual contracting elements or actuators in a layered helical and circumferential fashion, mimicking the orientation of the outer two muscle layers of the mammalian heart. The resulting implantable soft robot mimicked the form and function of the native heart, with a stiffness value of the same order of magnitude as that of the heart tissue. We demonstrated feasibility of this soft sleeve device for supporting heart function in a porcine model of acute heart failure. The soft robotic sleeve can be customized to patient-specific needs and may have the potential to act as a bridge to transplant for patients with heart failure.
The Soft Robotics Toolkit (SRT) is an open-access website containing detailed information about the design, fabrication, and characterization of soft-robotic components and systems (Figure 1). Soft robotics is a growing field of research concerned with the development of electromechanical technology composed of compliant materials or structures. The SRT website hosts design files, multimedia fabrication instructions, and software tutorials submitted by an international community of soft-robotics researchers and designers. In this article, we describe the development of the SRT and some challenges in developing widely disseminated robotic-hardware resources. Our attempts to overcome these challenges in the development of the toolkit are discussed by focusing on strategies that have been used to engage participants ranging from K-12 grade students to robotics research groups. A series of design competitions encouraged people to use and contribute to the toolkit. New fabrication methods requiring only low-cost and accessible materials were developed to lower the entry barriers to soft robotics and instructional materials and outreach activities were used to engage new audiences. We hope that our experiences in developing and scaling the toolkit may serve as guidance for other open robotic-hardware projects.
Background Recent advances in wearable robotic devices have demonstrated the ability to reduce the metabolic cost of walking by assisting the ankle joint. To achieve greater gains in the future it will be important to determine optimal actuation parameters and explore the effect of assisting other joints. The aim of the present work is to investigate how the timing of hip extension assistance affects the positive mechanical power delivered by an exosuit and its effect on biological joint power and metabolic cost during loaded walking. In this study, we evaluated 4 different hip assistive profiles with different actuation timings: early-start-early-peak (ESEP), early-start-late-peak (ESLP), late-start-early-peak (LSEP), late-start-late-peak (LSLP).
Methods Eight healthy participants walked on a treadmill at a constant speed of 1.5 m · s-1 while carrying a 23 kg backpack load. We tested five different conditions: four with the assistive profiles described above and one unpowered condition where no assistance was provided. We evaluated participants’ lower limb kinetics, kinematics, metabolic cost and muscle activation.
Results The variation of timing in the hip extension assistance resulted in a different amount of mechanical power delivered to the wearer across conditions; with the ESLP condition providing a significantly higher amount of positive mechanical power (0.219 ± 0.006 W · kg-1) with respect to the other powered conditions. Biological joint power was significantly reduced at the hip (ESEP and ESLP) and at the knee (ESEP, ESLP and LSEP) with respect to the unpowered condition. Further, all assistive profiles significantly reduced the metabolic cost of walking compared to the unpowered condition by 5.7 ± 1.5 %, 8.5 ± 0.9 %, 6.3 ± 1.4 % and 7.1 ± 1.9 % (mean ± SE for ESEP, ESLP, LSEP, LSLP, respectively).
Conclusions The highest positive mechanical power delivered by the soft exosuit was reported in the ESLP condition, which showed also a significant reduction in both biological hip and knee joint power. Further, the ESLP condition had the highest average metabolic reduction among the powered conditions. Future work on autonomous hip exoskeletons may incorporate these considerations when designing effective control strategies.
We present a simple fabrication approach for anisotropically conductive stretchable composites, towards novel flexible pressure transducers. Flexible electronic systems have gained great interest in recent years, and within this space, anisotropic conducting materials have been explored for enhanced sensing performance. However, current methods for producing these materials are complex or are limited to small fabrication areas. Our method uses film applicator coating to render commercially available conductive RTVs anisotropically conductive. A ratio of in-plane surface resistance to through-thickness resistance of 1010 was achieved using our method. Furthermore, we show that when a normal pressure is applied to such films, the in-plane resistance can be reduced by seven orders of magnitude for an applied pressure of 10 kPa. Hence these materials show great promise for the development of novel, robust pressure transducers.
Soft bending actuators are inherently compliant, compact, and lightweight. They are preferable candidates over rigid actuators for robotic applications ranging from physical human interaction to delicate object manipulation. However, characterizing and predicting their behaviors are challenging due to the material nonlinearities and the complex motions they can produce. This paper investigates a soft bending actuator design that uses a single air chamber and fiber reinforcements. Additionally, the actuator design incorporates a sensing layer to enable real-time bending angle measurement for analysis and control. In order to study the bending and force exertion characteristics when interacting with the environment, a quasistatic analytical model is developed based on the bending moments generated from the applied internal pressure and stretches of the soft materials. Comparatively, a finite-element method model is created for the same actuator design. Both the analytical model and the finite-element model are used in the fiber reinforcement analysis and the validation experiments with fabricated actuators. The experimental results demonstrate that the analytical model captures the relationships of supplied air pressure, actuator bending angle, and interaction force at the actuator tip. Moreover, it is shown that an off-the-shelf bend angle sensor integrated to the actuator in this study could provide real-time force estimation, thus eliminating the need for a force sensor.
Light-Intensity Modulated (LIM) force sensors are seeing increasing interest in the field of surgical robotics and flexible systems in particular. However, such sensing modalities are notoriously susceptible to ambient effects such as temperature and environmental irradiance which can register as false force readings. We explore machine learning techniques to dynamically compensate for environmental biases that plague multi-axis optoelectronic force sensors. In this work, we fabricate a multisensor: three-axis LIM force sensor with integrated temperature and ambient irradiance sensing manufactured via a monolithic, origami-inspired fabrication process called printed-circuit MEMS. We explore machine learning regression techniques to compensate for temperature and ambient light sensitivity using on-board environmental sensor data. We compare batch-based ridge regression, kernelized regression and support vector techniques to baseline ordinary least-squares estimates to show that on-board environmental monitoring can substantially improve sensor force tracking performance and output stability under variable lighting and large (>100C) thermal gradients. By augmenting the least-squares estimate with nonlinear functions describing both environmental disturbances and cross-axis coupling effects, we can reduce the error in Fx, Fy and Fz by 10%, 33%, and 73%, respectively. We assess viability of each algorithm tested in terms of both prediction accuracy and computational overhead, and analyze kernel-based regression for prediction in the context of online force feedback and haptics applications in surgical robotics. Finally, we suggest future work for fast approximation and prediction using stochastic, sparse kernel techniques.