Benchmarks have long been used to verify and compare the readiness level of different technologies in many application domains. In the field of wearable robots, the lack of a recognized benchmarking methodology is one important impediment that may hamper the efficient translation of research prototypes into actual products. At the same time, an exponentially growing number of research studies are addressing the problem of quantifying the performance of robotic exoskeletons, resulting in a rich and highly heterogeneous picture of methods, variables and protocols. This review aims to organize this information, and identify the most promising performance indicators that can be converted into practical benchmarks. We focus our analysis on lower limb functions, including a wide spectrum of motor skills and performance indicators. We found that, in general, the evaluation of lower limb exoskeletons is still largely focused on straight walking, with poor coverage of most of the basic motor skills that make up the activities of daily life. Our analysis also reveals a clear bias towards generic kinematics and kinetic indicators, in spite of the metrics of human-robot interaction. Based on these results, we identify and discuss a number of promising research directions that may help the community to attain a comprehensive benchmarking methodology for robotassisted locomotion more efficiently.

We present simulated monopedal and bipedal robots that are capable of open-loop stable periodic running motions without any feedback even though they have no statically stable standing positions. Running as opposed to walking involves flight phases which makes stability a particularly difficult issue. The concept of open-loop stability implies that the actuators receive purely periodic torque or force inputs that are never altered by any feedback in order to prevent the robot from falling. The design of these robots and the choice of model parameter values leading to stable motions is a difficult task that has been accomplished using newly developed stability optimization methods.

This paper introduces a method for the robust estimation of foot clearance during walking, using a single inertial measurement unit (IMU) placed on the subject’s foot. The proposed solution is based on double integration and drift cancellation of foot acceleration signals. The method is insensitive to misalignment of IMU axes with respect to foot axes. Details are provided regarding calibration and signal processing procedures. Experimental validation was performed on 10 healthy subjects under three walking conditions: normal, fast and with obstacles. Foot clearance estimation results were compared to measurements from an optical motion capture system. The mean error between them is significantly less than 15 % under the various walking conditions.

The advancement and development of human modeling have greatly benefited from principles used in robotics, for instance, multibody dynamics laid the foundations for physics engines of human movement simulation, and the robotics and control theory were used to contextualize human sensorimotor control. There are many common interests and interconnections between the fields of human modeling and robotics. In recent years, as robots have become safer and smarter, they actively participate in our lives and help us in various scenarios. Roboticists need tools and data from human modeling to build next-generation robots that better assist humans. In this survey, we focus on the connections between physical human-robot interaction and human modeling. On one hand, human neuromusculoskeletal and sensorimotor control models provide novel insights into the human response that robots can utilize to improve human performance. On the other hand, robots are becoming instrumental in quantifying the performance of the (neuro)musculoskeletal system. Thus, the combined use of human modeling and robotic methods in physical human-robot interaction can lead to both improved human understanding and functional assistance.