Robotically Aided Method to Characterise the Soft Tissue Interaction with Wearable Robots.

Wearable robots are widely used to enhance, support or assist humans in different tasks. To accomplish this scope, the interaction between the human body and the device should be comfortable, smooth, high-efficient to transfer forces, and safe for the user. Nevertheless, the pressure and shear stress related to these goals have been overlooked or partially analysed. In this sense, it is crucial to understand the soft tissue response through the in-vivo characterisation of multiple areas of the human body. In fact, soft tissue characterisation plays an essential role in calculating the pressure distribution and shear stress. However, current approaches to estimating soft tissue properties are unsuitable for deployment with multiple human body areas. Hence, this work presents a novel methodology to ease the characterisation of soft tissues using a robotic arm and a 3D superficial scanner. First, the robotic arm is validated by comparing the tensile and compression tests to the indentation tests done by the robot, estimating a 10,4% error. The preliminary experimental tests present the hyperelastic model which fit two adjacent zones of the forearm. This analysis can be extended in several ways, such as: calculating the shear stress, the energy losses or deformations caused by the interaction, and investigating the pressure distribution of different types of physical interfaces.

Deep learning for biosignal control: insights from basic to real-time methods with recommendations.

Objective.Biosignal control is an interaction modality that allows users to interact with electronic devices by decoding the biological signals emanating from the movements or thoughts of the user. This manner of interaction with devices can enhance the sense of agency for users and enable persons suffering from a paralyzing condition to interact with everyday devices that would otherwise be challenging for them to use. It can also improve control of prosthetic devices and exoskeletons by making the interaction feel more natural and intuitive. However, with the current state of the art, several issues still need to be addressed to reliably decode user intent from biosignals and provide an improved user experience over other interaction modalities. One solution is to leverage advances in deep learning (DL) methods to provide more reliable decoding at the expense of added computational complexity. This scoping review introduces the basic concepts of DL and assists readers in deploying DL methods to a real-time control system that should operate under real-world conditions.Approach.The scope of this review covers any electronic device, but with an emphasis on robotic devices, as this is the most active area of research in biosignal control. We review the literature pertaining to the implementation and evaluation of control systems that incorporate DL to identify the main gaps and issues in the field, and formulate suggestions on how to mitigate them.Main results.The results highlight the main challenges in biosignal control with DL methods. Additionally, we were able to formulate guidelines on the best approach to designing, implementing and evaluating research prototypes that use DL in their biosignal control systems.Significance.This review should assist researchers that are new to the fields of biosignal control and DL in successfully deploying a full biosignal control system. Experts in their respective fields can use this article to identify possible avenues of research that would further advance the development of biosignal control with DL methods.

Human Musculoskeletal and Energetic Adaptations to Unilateral Robotic Knee Gait Assistance.

OBJECTIVE: This paper aims to analyse the human musculoskeletal and energetic adaptation mechanisms when physically interacting with a unilateral knee orthosis during treadmill walking. METHODS: Test subjects participated in two walking trials, whereby the orthosis was controlled to deliver five predefined torque profiles of different duration (as % of a gait cycle). The adaptations to assistive torques of different duration were analysed in terms of gait parameters, metabolic effort, and muscle activity. RESULTS: Orthotic assistance's kinematic effects remain local to the assisted leg and joint, unlike the muscles spanning the knee joint, which engage in a balancing-out action to retain stability. Duration of assistive torque significantly affects only the timing of the knee joint's peak flexion angle in the stance phase, while the observed joint kinematics and muscle activity demonstrate different recovery times upon changing robotic support (washout effects). CONCLUSION: Human body adaptations to external robotic knee joint assistance during walking take place on multiple levels and to a different extent in a joint effort to keep the gait stable. SIGNIFICANCE: This paper provides important insights into the human body's multiple adaptation mechanisms in the presence of external robotic assistance.

Improved Motion Classification With an Integrated Multimodal Exoskeleton Interface.

Human motion intention detection is an essential part of the control of upper-body exoskeletons. While surface electromyography (sEMG)-based systems may be able to provide anticipatory control, they typically require exact placement of the electrodes on the muscle bodies which limits the practical use and donning of the technology. In this study, we propose a novel physical interface for exoskeletons with integrated sEMG- and pressure sensors. The sensors are 3D-printed with flexible, conductive materials and allow multi-modal information to be obtained during operation. A K-Nearest Neighbours classifier is implemented in an off-line manner to detect reaching movements and lifting tasks that represent daily activities of industrial workers. The performance of the classifier is validated through repeated experiments and compared to a unimodal EMG-based classifier. The results indicate that excellent prediction performance can be obtained, even with a minimal amount of sEMG electrodes and without specific placement of the electrode.

Integration of 3D Printed Flexible Pressure Sensors into Physical Interfaces for Wearable Robots.

Sensing pressure at the physical interface between the robot and the human has important implications for wearable robots. On the one hand, monitoring pressure distribution can give valuable benefits on the aspects of comfortability and safety of such devices. Additionally, on the other hand, they can be used as a rich sensory input to high level interaction controllers. However, a problem is that the commercial availability of this technology is mostly limited to either low-cost solutions with poor performance or expensive options, limiting the possibilities for iterative designs. As an alternative, in this manuscript we present a three-dimensional (3D) printed flexible capacitive pressure sensor that allows seamless integration for wearable robotic applications. The sensors are manufactured using additive manufacturing techniques, which provides benefits in terms of versatility of design and implementation. In this study, a characterization of the 3D printed sensors in a test-bench is presented after which the sensors are integrated in an upper arm interface. A human-in-the-loop calibration of the sensors is then shown, allowing to estimate the external force and pressure distribution that is acting on the upper arm of seven human subjects while performing a dynamic task. The validation of the method is achieved by means of a collaborative robot for precise force interaction measurements. The results indicate that the proposed sensors are a potential solution for further implementation in human-robot interfaces.

Walking with a powered ankle-foot orthosis: the effects of actuation timing and stiffness level on healthy users.

BACKGROUND: In the last decades, several powered ankle-foot orthoses have been developed to assist the ankle joint of their users during walking. Recent studies have shown that the effects of the assistance provided by powered ankle-foot orthoses depend on the assistive profile. In compliant actuators, the stiffness level influences the actuator's performance. However, the effects of this parameter on the users has not been yet evaluated. The goal of this study is to assess the effects of the assistance provided by a variable stiffness ankle actuator on healthy young users. More specifically, the effect of different onset times of the push-off torque and different actuator's stiffness levels has been investigated. METHODS: Eight healthy subjects walked with a unilateral powered ankle-foot orthosis in several assisted walking trials. The powered orthosis was actuated in the sagittal plane by a variable stiffness actuator. During the assisted walking trials, three different onset times of the push-off assistance and three different actuator's stiffness levels were used. The metabolic cost of walking, lower limb muscles activation, joint kinematics, and gait parameters measured during different assisted walking trials were compared to the ones measured during normal walking and walking with the powered orthosis not providing assistance. RESULTS: This study found trends for more compliant settings of the ankle actuator resulting in bigger reductions of the metabolic cost of walking and soleus muscle activation in the stance phase during assisted walking as compared to the unassisted walking trial. In addition to this, the study found that, among the tested onset times, the earlier ones showed a trend for bigger reductions of the activation of the soleus muscle during stance, while the later ones led to a bigger reduction in the metabolic cost of walking in the assisted walking trials as compared to the unassisted condition. CONCLUSIONS: This study presents a first attempt to show that, together with the assistive torque profile, also the stiffness level of a compliant ankle actuator can influence the assistive performance of a powered ankle-foot orthosis.

Design and experimental evaluation of a lightweight, high-torque and compliant actuator for an active ankle foot orthosis.

The human ankle joint plays a crucial role during walking. At the push-off phase the ankle plantarflexors generate the highest torque among the lower limb joints during this activity. The potential of the ankle plantarflexors is affected by numerous pathologies and injuries, which cause a decrease in the ability of the subject to achieve a natural gait pattern. Active orthoses have shown to have potential in assisting these subjects. The design of such robots is very challenging due to the contrasting design requirements of wearability (light weight and compact) and high torques capacity. This paper presents the development of a high-torque ankle actuator to assist the ankle joint in both dorsiflexion and plantarflexion. The compliant actuator is a spindle-driven MACCEPA (Mechanically Adjustable Compliance and Controllable Equilibrium Position Actuator). The design of the actuator was made to keep its weight as low as possible, while being able to provide high torques. As a result of this novel design, the actuator weighs 1.18kg. Some static characterization tests were perfomed on the actuator and their results are shown in the paper.

BioMot exoskeleton - Towards a smart wearable robot for symbiotic human-robot interaction.

This paper presents design of a novel modular lower-limb gait exoskeleton built within the FP7 BioMot project. Exoskeleton employs a variable stiffness actuator in all 6 joints, a directional-flexibility structure and a novel physical humanrobot interfacing, which allows it to deliver the required output while minimally constraining user's gait by providing passive degrees of freedom. Due to modularity, the exoskeleton can be used as a full lower-limb orthosis, a single-joint orthosis in any of the three joints, and a two-joint orthosis in a combination of any of the two joints. By employing a simple torque control strategy, the exoskeleton can be used to deliver user-specific assistance, both in gait rehabilitation and in assisting people suffering musculoskeletal impairments. The result of the presented BioMot efforts is a low-footprint exoskeleton with powerful compliant actuators, simple, yet effective torque controller and easily adjustable flexible structure.