Physical human-robot interfaces are a challenging aspect of exoskeleton design, mainly due to the fact that interfaces tend to migrate relatively to the body leading to discomfort and power losses. Therefore, the key is to develop interfaces that optimize attachment stiffness, i.e., reduce relative motion, without compromising comfort. To that end, we propose a method to obtain the optimal attachment pressure in terms of connection stiffness and comfort. The method is based on a soft robotic interface capable of actively controlling strapping pressure which is coupled to a cobot. Hereby the effects of strapping pressure on energetic losses, connection stiffness, and perceived comfort are analyzed. Results indicate a trade-off between connection stiffness and perceived comfort for this type of interface. An optimal strapping pressure was found in the 50 to 80 mmHg range. Connection stiffness was found to increase linearly over a pressure range from 0 mmHg (stiffness of 1139 N/m) to 100 mmHg (stiffness of 2232 N/m). And energetic losses were reduced by 42% by increasing connection stiffness. This research highlights the importance of strapping pressure when attaching an exoskeleton to a human and introduces a new adaptive interface to improve the coupling from an exoskeleton to an individual.

Exoskeletons have shown their ability to assist locomotion and augment human performances. However, the benefits of wearing these devices depend on how effectively power can be transmitted from the device to the user's biological structures. Recent studies have shown evidence of inefficient power transmission, with losses of up to 50%. The problem of power transmission can be mitigated by designing interfaces that increase contact stiffness and reduce relative motion between the limb and the robot. In this contribution, the design and development of physical interfaces for rigid lower limb exoskeletons is presented. The relative motion between the human and the developed interface is evaluated using a motion capture system and compared to the performances of a commercially available interface. Results indicate a clear reduction in relative motion between the user and the exoskeleton when the customized interface is worn.

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.

This work gives an overview of all types of constant torque mechanisms (CTMs) based on both function and structure. Based on their architecture, they can be divided into five distinct categories, which all have their specific behavior. It is also shown that these CTMs can be divided into two application types, namely, power assistance or torque stabilization. Constant force mechanisms (CFMs) are more prevalent in the literature; therefore, some discussion is proposed on how CFMs can be transformed into CTMs. It is also shown that some of these CTMs have a very high specific energy, which makes them potentially interesting for use as energy storage/power providers in novel fields like robotics.

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.

Measurements of pressure distribution on physical human-robot interfaces are crucial for ensuring the comfort and safety of human-exoskeleton interactions. To address this need, we propose a novel sensorized physical human robot interface with pressure distribution measurement based on Electrical Impedance Tomography (EIT). EIT sensors have the advantages of a simple structure and the ability to continuously measure pressure over a large area, making it a promising solution for wearable robots. A piezoresistive composite made of Carbon Black (CB) and flexible polymer was fabricated and embedded in a physical interface to compose the EIT sensor. To improve the spatial solution of the EIT inverse problem, a Convolutional Neural Network (CNN) enhanced Tikhonov regularization (referred to as CNN-TR) approach was adopted. The original compressed image is first reconstructed with Tikhonov regularization and then enhanced by the CNN model that trained with simulation data. Then a validation platform is built based on a MARK-10 force tester. The experimental results showed that developed EIT sensor with cNn-TR reconstruction method achieves accurate localization and size estimation of the compressed area, and is capable of distinguishing multiple compressed areas. Additionally, the EIT sensor is embedded into a physical interface to measure the compressed areas between the physical interface and human lower limb. The results validate that the proposed EIT pressure sensor is suitable for physical interfaces.