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.
Recent advances in medical robotics have initiated a transition from rigid serial manipulators to flexible or continuum robots capable of navigating to confined anatomy within the body. A desire for further procedure minimization is a key accelerator for the development of these flexible systems where the end goal is to provide access to the previously inaccessible anatomical workspaces and enable new minimally invasive surgical (MIS) procedures. While sophisticated navigation and control capabilities have been demonstrated for such systems, existing manufacturing approaches have limited the capabilities of millimeter-scale end-effectors for these flexible systems to date and, to achieve next generation highly functional end-effectors for surgical robots, advanced manufacturing approaches are required. We address this challenge by utilizing a disruptive 2D layer-by-layer precision fabrication process (inspired by printed circuit board manufacturing) that can create functional 3D mechanisms by folding 2D layers of materials which may be structural, flexible, adhesive, or conductive. Such an approach enables actuation, sensing, and circuitry to be directly integrated with the articulating features by selecting the appropriate materials during the layer-by-layer manufacturing process. To demonstrate the efficacy of this technology, we use it to fabricate three modular robotic components at the millimeter-scale: (1) sensors, (2) mechanisms, and (3) actuators. These modules could potentially be implemented into transendoscopic systems, enabling bilateral grasping, retraction and cutting, and could potentially mitigate challenging MIS interventions performed via endoscopy or flexible means. This research lays the ground work for new mechanism, sensor and actuation technologies that can be readily integrated via new millimeter-scale layer-by-layer manufacturing approaches.
Carrying load alters normal walking, imposes additional stress to the musculoskeletal system, and results in an increase in energy consumption and a consequent earlier onset of fatigue. This phenomenon is largely due to increased work requirements in lower extremity joints, in turn requiring higher muscle activation. The aim of this work was to assess the biomechanical and physiological effects of a multi-joint soft exosuit that applies assistive torques to the biological hip and ankle joints during loaded walking.
Severe skeletal muscle injuries are common and can lead to extensive fibrosis, scarring, and loss of function. Clinically, no therapeutic intervention exists that allows for a full functional restoration. As a result, both drug and cellular therapies are being widely investigated for treatment of muscle injury. Because muscle is known to respond to mechanical loading, we investigated instead whether a material system capable of massage-like compressions could promote regeneration. Magnetic actuation of biphasic ferrogel scaffolds implanted at the site of muscle injury resulted in uniform cyclic compressions that led to reduced fibrous capsule formation around the implant, as well as reduced fibrosis and inflammation in the injured muscle. In contrast, no significant effect of ferrogel actuation on muscle vascularization or perfusion was found. Strikingly, ferrogel-driven mechanical compressions led to enhanced muscle regeneration and a ∼threefold increase in maximum contractile force of the treated muscle at 2 wk compared with no-treatment controls. Although this study focuses on the repair of severely injured skeletal muscle, magnetically stimulated bioagent-free ferrogels may find broad utility in the field of regenerative medicine.
The innovation in surgical robotics has seen a shift toward flexible systems that can access remote locations inside the body. However, a general reliance on the conventional fabrication techniques ultimately limits the complexity and the sophistication of the distal implementations of such systems, and poses a barrier to further innovation and widespread adoption. In this paper, we present a novel, self-assembling force sensor manufactured using a composite lamination fabrication process, wherein linkages pre-machined in the laminate provide the required degrees-of-freedom and fold patterns to facilitate self-assembly. Using the purely 2-D fabrication techniques, the energy contained within a planar elastic biasing element directly integrated into the laminate is released post-fabrication, allowing the sensor to self-assemble into its final 3-D shape. The sensors are batch-fabricated, further driving down the production costs. The transduction mechanism relies on the principle of light intensity modulation, which allows the sensor to detect axial forces with millinewton-level resolution. The geometry of the sensor was selected based on the size constraints inherent in minimally invasive surgery, as well as with a specific focus on optimizing the sensor's linearity. The sensor is unique from the fiber-based force sensors in that the emitter and the detector are encapsulated within the sensor itself. The bare sensor operates over a force range of 0-200 mN, with a sensitivity of 5 V/N and a resolution of 0.8 mN. The experimental results show that the sensor's stiffness can be tuned using a thicker material for the spring layer and/or encapsulation/integration with soft materials. The empirical validation shows that the sensor has the sensitivity and the resolution necessary to discern the biologically relevant forces in a simulated cannulation task.