Abstract
AbstractWearable devices, such as exoskeletons, are becoming increasingly common and are being used mainly for improving motility and daily life autonomy, rehabilitation purposes, and as industrial aids. There are many variables that must be optimized to create an efficient, smoothly operating device. The selection of a suitable actuator is one of these variables, and the actuators are usually sized after studying the kinematic and dynamic characteristics of the target task, combining information from motion tracking, inverse dynamics, and force plates. While this may be a good method for approximate sizing of actuators, a more detailed approach is necessary to fully understand actuator performance, control algorithms or sensing strategies, and their impact on weight, dynamic performance, energy consumption, complexity, and cost. This work describes a learning-based evaluation method to provide this more detailed analysis of an actuation system for ourXoTrunkexoskeleton. The study includes: (a) a real-world experimental setup to gather kinematics and dynamics data; (b) simulation of the actuation system focusing on motor performance and control strategy; (c) experimental validation of the simulation; and (d) testing in real scenarios. This study creates a systematic framework to analyze actuator performance and control algorithms to improve operation in the real scenario by replicating the kinematics and dynamics of the human–robot interaction. Implementation of this approach shows substantial improvement in the task-related performance when applied on a back-support exoskeleton during a walking task.
Highlights
Results were recorded for mean absolute error, standard deviation and relative errors in angular displacement, speed, and acceleration, Table 1 The results presented in Table 1 show kinematic comparison data for the three tasks presented in “Experimental test protocol” in the two modalities
The complexity of human–robot interaction while wearing an exoskeleton means that safe, smooth, accurate, predictable motions, and high user comfort are paramount
Approximate controllers or poorly dimensioned actuators that would generate low performance and uncomfortable effects are not tolerable. To address these critical design issues, this paper explored a learning-based evaluation framework, taking into consideration the human–robot interaction, to support the design and analysis of mechanical, actuation, and control solutions
Summary
Exoskeletons and Applications The past few years have seen rapidly growing interest in exoskeletons and their applications (Ferris and Schlink, 2017; Young and Ferris, 2017). These are wearable devices that support physical activities by working in synchrony with one or more joints of the musculoskeletal structure. The most common field where exoskeletons are applied is physical/motor rehabilitation using systems such as Lokomat (Jezernik et al, 2003) and LOPES (Veneman et al, 2007), both of which are static/fixed structures. Mobile exoskeletons have the potential to be used outside clinical settings to restore some degree of motility to people with pathologies causing severe loss of mobility. Simpler devices targeted at assisting people with moderate to low impairments, such as the elderly, usually assist a single or double joint (Kong and Jeon, 2006; Ikehara et al, 2011)
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