Bio-inspired control strategies in wearable robotics: A comprehensive review of CPGs and DMPs

After millions of years of evolution, humans can achieve locomotion tasks in complex environments with versatile, robust and efficient bipedal gaits. Understanding human locomotion control systems can help us develop novel bio-inspired based methods for improving the current legged robots (e.g. humanoids) and wearable devices (e.g. prostheses, exoskeletons). This thesis systematically explores the bio-inspired approaches from concepts to applications for further understanding human locomotion. It includes three main parts: biomechanical studies on human experiments, hardware implementations of bio-inspired concepts, and modeling of human locomotion. The biomechanical studies provide insights on the human locomotor control systems. Human locomotion control can be separated into three locomotor subfunctions which are stance (axial leg function), swing (rotational leg function), and balance (posture control). We investigated how these subfunctions interact with each other by analyzing the contribution of stance and swing leg movements to the walking dynamics. The results reveal a coupling mechanism and synergistic interactions between the subfunctions. Further analyses on the human gait initiation (from standing to walking) experimental data demonstrate that the swing leg and stance leg functions are emerged during the first stride of the stance limb. And we find a strong correlation between the control of the frontal plane and the sagittal plane joints. All these results indicate that the support of one subfunction can provide benefits for the others. Inspired by the findings from the previous biomechanical studies, we implemented bio-inspired balance control strategies on a lower-limb exoskeleton for human walking. The hardware implementations are used to validate and demonstrate the benefits of bio-inspired control concepts. The results show that the bio-inspired balance controller can not only support the swing and stance leg function but also reduce the metabolic costs and assist human walking. The results also support the prior biomechanical studies which suggest synergistic interactions between the subfunctions. In addition, we also implemented a bio-inspired neuromuscular reflex based controller for a hopping robot to investigate the potential benefits of the muscle properties for the stance (rebounding) leg function. The results demonstrate that the robot can achieve stable and robust hopping with the bio-inspired controller. Further analyses show that the neuromuscular properties play an important role in stabilizing the motion. These results indicate that gait models which include the muscle properties and reflex-like control could better reproduce human locomotion. The modeling of human locomotion help us test the bio-inspired concepts in the simulation and reveal the key components of human locomotion control. Here, based on the previous findings, we developed a complex neuromuscular gait model to produce subject specific walking behaviors. Deep reinforcement learning methods were used to generate the control policy (sensor-motor mappings) which has similar functionality as human spinal cord neural circuitries. The results show that the model can achieve robust walking and closely reproduce human joint kinematics and muscle activations. In addition, we also found that the neuromuscular dynamics can facilitate the learning. In future, the proposed gait model can be used to identify optimal control schemes for wearable robots (e.g. prostheses, exoskeletons). In summary, this thesis presents a systematic approach of investigating bio-inspired concepts for human locomotion by experimental studies of human gait, simulations, and hardware implementations. The main contribution of this work is demonstrating how the bio-inspired concepts are extracted from the human experimental data, tested with the simulation models, and implemented and validated with the hardware systems. The outcomes of this thesis can be used as a framework to develop novel bio-inspired controllers for improving the performance of legged robots (e.g. humanoids) and wearable robots (e.g. prostheses, exoskeletons).

Background: Stroke causes weak functional mobility in survivors and affects the ability to perform activities of daily living. Wearable ankle robots are a potential intervention for gait rehabilitation post-stroke.Objective: The aim of this study is to provide a systematic review of wearable ankle robots, focusing on the overview, classification and comparison of actuators, gait event detection, control strategies, and performance evaluation.Method: Only English-language studies published from December 1995 to July 2018 were searched in the following databases: PubMed, EMBASE, Web of Science, Scopus, IEEE Xplore, Science Direct, SAGE journals.Result: A total of 48 articles were selected and 97 stroke survivors participated in these trials. Findings showed that few comparative trials were conducted among different actuators or control strategies. Moreover, mixed sensing technology which combines kinematic with kinetic information was effective in detecting motion intention of stroke survivors. Furthermore, all the selected clinical studies showed an improvement in the peak dorsiflexion degree of the swing phase, propulsion on the paretic side during push-off, and further enhanced walking speed after a period of robot-assisted ankle rehabilitation training.Conclusions: Preliminary findings suggest that wearable ankle robots have certain clinical benefits for the treatment of hemiplegic gait post-stroke. In the near future, a multicenter randomized controlled clinical trial is extremely necessary to enhance the clinical effectiveness of wearable ankle robots.

Reports on the market for wearable robotics and discusses applications for its use. Among the new frontiers of research, wearable robotics is increasingly prominent. Robots and humans have often represented two entities that exist without sharing common spaces (as in the industrial realm where humans and robots are separated by safety fences), but the road taken envisions a future where artificial and biological systems aim at a truly symbiotic interaction. Wearable robotics has been expanding in several areas, embracing the civilian and industrial domains. The adoption of smart spring-loaded mechanisms and new materials comprising polymers, carbon fibers, and textiles, complemented with novel smart sensing- actuation and human-in-the-loop control strategies, is opening a wide scenario where the exoskeletons and exosuits market is expected to grow to US$5.2 billion by 2025.

Wearable robots, such as bionic prostheses and exoskeletons, have been conventionally designed with low-torque, high-speed motors and high transmission ratios; however, recently designers are increasingly implementing high-torque, low-speed motors with lower transmission ratios. These motors were popularized by the drone industry and have transitioned to general use in robotics for their improved output impedance, efficiency, and lower audible noise. Due to the relative newness of these motors, there is a lack of information regarding how transmission dynamics affect the desired output impedance. In this study, we developed system identification techniques to characterize the output impedance (stiffness and damping) of these actuators operating without torque feedback, termed “open-loop” impedance control, a common control strategy employed in wearable robotics. Open-loop stiffness errors reached up to 42%, but could be reduced to 2.9% using a linear model based on our characterization. Second, we characterized the total efficiency across various power regimes, during both positive and negative work, and measured an average positive efficiency of 65%. With these characterization experiments, we are able to better compensate for transmission losses, render more accurate impedance control, and operate actuators more efficiently. This work provides performance benchmarks and context for existing wearable robotic systems that implement similar open-loop control strategies.

An adaptive control strategy for postural stability using a wearable robot

This article presents a systematic review on wearable robotic devices that use human-in-the-loop optimization (HILO) strategies to improve human-robot interaction. A total of 46 HILO studies were identified and divided into upper and lower limb robotic devices. The main aspects from HILO were identified, reviewed, and classified in four areas: 1) human-machine systems; 2) optimization methods; 3) control strategies; and 4) experimental protocols. A variety of objective functions (physiological, biomechanical, and subjective), optimization strategies, and optimized control parameters configurations used in different control strategies are presented and analyzed. An overview of experimental protocols is provided, including metrics, tasks, and conditions tested. Moreover, the relevance given to training or adaptation periods was explored. We outline an HILO framework that includes current wearable robots, optimization strategies, objective functions, control strategies, and experimental protocols. We conclude by highlighting current research gaps and defining future directions to improve the development of advanced HILO strategies in upper and lower limb wearable robots.

Wearable robots are envisioned to amplify the independence of people with movement impairments by providing daily physical assistance. For portable, comfortable, and safe devices, soft pneumatic-based robots are emerging as a potential solution. However, due to the inherent complexities, including compliance and nonlinear mechanical behavior, feedback control for facilitating human–robot interaction remains a challenge. Herein, we present the design, fabrication, and control architecture of a soft wearable robot that assists in supination and pronation of the forearm. The soft wearable robot integrates an antagonistic pair of pneumatic-based helical actuators to provide active pronation and supination torques. Our main contribution is a bio-inspired equilibrium-point control scheme for integrating proprioceptive feedback and exteroceptive input (e.g., the user’s muscle activation signals) directly with the on/off valve behavior of the soft pneumatic actuators. The proposed human–robot controller is directly inspired by the equilibrium-point hypothesis of motor control, which suggests that voluntary movements arise through shifts in the equilibrium state of the antagonistic muscle pair spanning a joint. We hypothesized that the proposed method would reduce the required effort during dynamic manipulation without affecting the error. In order to evaluate our proposed method, we recruited seven pediatric participants with movement disorders to perform two dynamic interaction tasks with a haptic manipulandum. Each task required the participant to track a sinusoidal trajectory while the haptic manipulandum behaved as a Spring-Dominate system or Inertia-Dominate system. Our results reveal that the soft wearable robot, when active, reduced user effort on average by 14%. This work demonstrates the practical implementation of an equilibrium-point volitional controller for wearable robots and provides a foundational path toward versatile, low-cost, and soft wearable robots.

Most of the wearable robots today assist their users by acting in parallel or in series to their natural limbs. We propose a different approach to wearable robotics, consisting of devices that provide users with additional, independent robotic limbs. We present a wearable robot prototype that can achieve these goals with an extremely light weight apparatus. In order to control additional robotic limbs as if they were part of the user's body, we need voluntary signals that are independent of natural limb motions and comfortable to measure. One suitable solution — explored in this study — is the use of muscle activation signals generated by the torso. We hypothesize that a human is competent to move the extra limbs voluntarily and independently without interfering with the natural arms and legs. We developed a wearable suit to measure these signals, and we tested three possible real-time control strategies linking torso muscle contraction to the motions of two simulated extra limbs. The experimental data show that the velocity control strategy yields the highest motion accuracy, minimum muscular effort, maximum independence from the natural limbs and the fastest learning rate. This control strategy has then been applied to the control of the physical robot prototype, worn by human subjects. All of the subjects achieved accurate (normalized tracking error < 0.5), independent (normalized natural arm motions < 0.15) control of the extra limbs.

The development of robotic systems capable of sharing with humans the load of heavy tasks has been one of the primary objectives in robotics research. At present, in order to fulfil such an objective, a strong interest in the robotics community is collected by the so-called wearable robots, a class of robotics systems that are worn and directly controlled by the human operator. Wearable robots, together with powered orthoses that exploit robotic components and control strategies, can represent an immediate resource also for allowing humans to restore manipulation and/or walking functionalities. The present chapter deals with wearable robotics systems capable of providing different levels of functional and/or operational augmentation to the human beings for specific functions or tasks. Prostheses, powered orthoses, and exoskeletons are described for upper limb, lower limb, and whole body structures. State-of-the-art devices together with their functionalities and main components are presented for each class of wearable system. Critical design issues and open research aspects are reported.

Wearable robots have the potential to improve the lives of countless individuals; however, challenges associated with controlling these systems must be addressed before they can reach their full potential. Modern control strategies for wearable robots are predicated on activity-specific implementations, and testing is usually limited to a single, fixed activity within the laboratory (e.g., level ground walking). To accommodate various activities in real-world scenarios, control strategies must include the ability to safely and seamlessly transition between activity-specific controllers. One potential solution to this challenge is to the infer wearer's intent using pattern recognition of locomotion sensor data. To this end, we developed an intent recognition framework implementing convolutional neural networks with image encoding (i.e. spectrogram) that enables prediction of the upcoming locomotor activity of the wearer's next step. In this letter, we describe our intent recognition system, comprised of a mel-spectrogram and subsequent neural network architecture. In addition, we analyzed the effect of sensor locations and modalities on the recognition system, and compared our proposed system to state-of-the-art locomotor intent recognition strategies. We were able to attain high classification performance (error rate: 1.1%), which was comparable or better than previous systems.

This special issue collects recent works on the development and experimental validation of bioinspired control strategies for articulated robots. Selected articles cover a large range of robotics applications from humanoid robotics to animaloids and wearable assistive technologies, and all have a strong focus on the experimentation with real robotic systems. Overall, this special issue comprises seven articles, reporting on different control systems: three systems are for humanoid robots, one is for a snake-like robot, two are for legged robots, and one is for a wearable assistive robot.

Wearable exoskeleton robot is a kind of humanoid service robot to help the elderly and the patients with walking dysfunction, it is also an effective medical rehabilitation method to help patients who have walking disorders due to central neural system damage. This paper focuses on the flexible mechanism design of wearable lower limb exoskeleton robot. A wearable exoskeleton robot prototype was developed which can assist human walking. This paper analyzes the role of the major joints of walking human by experimental studies based on bionic design methods from human anatomy and bone surgery. We first analyze parameters of joint movements in a gait cycle, then we design a preliminary bionic model, finally we proposed a control strategy including a pair of electric crutches for the exoskeleton robot.

Back injuries are the most prevalent work-related musculoskeletal disorders and represent a major cause of disability. Although innovations in wearable robots aim to alleviate this hazard, the majority of existing exoskeletons are obtrusive because the rigid linkage design limits natural movement, thus causing ergonomic risk. Moreover, these existing systems are typically only suitable for one type of movement assistance, not ubiquitous for a wide variety of activities. To fill in this gap, this letter presents a new wearable robot design approach continuum soft exoskeleton. This spine-inspired wearable robot is unobtrusive and assists both squat and stoops while not impeding walking motion. To tackle the challenge of the unique anatomy of spine that is inappropriate to be simplified as a single degree of freedom joint, our robot is conformal to human anatomy and it can reduce multiple types of forces along the human spine such as the spinae muscle force, shear, and compression force of the lumbar vertebrae. We derived kinematics and kinetics models of this mechanism and established an analytical biomechanics model of human-robot interaction. Quantitative analysis of disc compression force, disc shear force and muscle force was performed in simulation. We further developed a virtual impedance control strategy to deliver force control and compensate hysteresis of Bowden cable transmission. The feasibility of the prototype was experimentally tested on three healthy subjects. The root mean square error of force tracking is 6.63 N (3.3% of the 200 N peak force) and it demonstrated that it can actively control the stiffness to the desired value. This continuum soft exoskeleton represents a feasible solution with the potential to reduce back pain for multiple activities and multiple forces along the human spine.

Wearable robots have become a prevalent method in the field of human augmentation and medical rehabilitation. Typical wearable robots mainly include exoskeletons and prostheses. However, their functions are limited due to dedicated design. In recent years, Supernumerary Robotic Limbs (SRLs) have become a hot spot in the field of wearable robots. Different from exoskeletons and prostheses, SRLs compensate and strengthen human abilities by providing extra limbs. This advantage allows SRLs to assist users in a novel way, rather than substituting missing limbs or enhancing existing limbs. However, finding a trade-off between wearability, efficiency, and usability of those SRLs is still an issue. This paper presents the state of the art in SRLs and discusses some open questions about SRLs' design and control for further research. This review covers the following areas: (1) Basic concepts and classifications of SRLs; (2) The literature retrieval methodology; (3) Design functions of different types of SRLs, including their positive and negative aspects; (4) Different control strategies of SRLs, including positive and negative aspects, and some improvement methods in applying SRLs; (5) The impact on human body schema while using SRLs; (6) Open challenges and suggestions for future development. This review will help researchers understand the current state of SRLs and provide comprehensive knowledge foundations for them.

Biomechanics, Sensing and Bio-inspired Control in Rehabilitation and Wearable Robotics