A Neuromechanical Model-Based Strategy to Estimate the Operator's Payload in Industrial Lifting Tasks.

One of the main technological barriers hindering the development of active industrial exoskeleton is today represented by the lack of suitable payload estimation algorithms characterized by high accuracy and low calibration time. The knowledge of the payload enables exoskeletons to dynamically provide the required assistance to the user. This work proposes a payload estimation methodology based on personalized Electromyography-driven musculoskeletal models (pEMS) combined with a payload estimation method we called "delta torque" that allows the decoupling of payload dynamical properties from human dynamical properties. The contribution of this work lies in the conceptualization of such methodology and its validation considering human operators during industrial lifting tasks. With respect to existing solutions often based on machine learning, our methodology requires smaller training datasets and can better generalize across different payloads and tasks. The proposed payload estimation methodology has been validated on lifting tasks with 0kg, 5kg, 10kg and 15kg, resulting in an average MAE of about 1.4 Kg. Even if 5kg and 10Kg lifting tasks were out of the training set, the MAE related to these tasks are 1.6 kg and 1.1 kg, respectively, demonstrating the generalizing property of the proposed methodology. To the best of the authors' knowledge, this is the first time that an EMG-driven model-based approach is proposed for human payload estimation.

Actuation Selection for Assistive Exoskeletons: Matching Capabilities to Task Requirements.

Selecting actuators for assistive exoskeletons involves decisions in which designers usually face contrasting requirements. While certain choices may depend on the application context or design philosophy, it is generally desirable to avoid oversizing actuators in order to obtain more lightweight and transparent systems, ultimately promoting the adoption of a given device. In many cases, the torque and power requirements can be relaxed by exploiting the contribution of an elastic element acting in mechanical parallel. This contribution considers one such case and introduces a methodology for the evaluation of different actuator choices resulting from the combination of different motors, reduction gears, and parallel stiffness profiles, helping to match actuator capabilities to the task requirements. Such methodology is based on a graphical tool showing how different design choices affect the actuator as a whole. To illustrate the approach, a back-support exoskeleton for lifting tasks is considered as a case study.