Rationale, Implementation and Evaluation of Assistive Strategies for an Active Back-Support Exoskeleton

Toward real-world evaluations of trunk exoskeletons using inertial measurement units

Trunk exoskeletons are an emerging technology that could reduce spinal loading, guide trunk motion, and augment lifting ability. However, while they have achieved promising results in brief laboratory studies, they have not yet been tested in longer-term real-world studies - partially due to reliance on stationary sensors such as cameras. To enable future real-world evaluations of trunk exoskeletons, this paper describes two preliminary studies on using inertial measurement units (IMUs) to collect kinematic data from an exoskeleton wearer. In the first study, a participant performed three activities (walking, sit-to-stand, box lifting) while trunk flexion angle was measured with both IMUs and reference cameras. The mean absolute difference in flexion angle between the two methods was 1.4° during walking, 3.6° during sit-to-stand and 5.2° during box lifting, showing that IMUs can measure trunk flexion with a reasonable accuracy. In the second study, six participants performed five activities (standing, sitting straight, slouching, 'good' lifting, 'bad' lifting), and a naïve Bayes classifier was used to automatically classify the activity from IMU data. The classification accuracy was 92.2%, indicating the feasibility of automated activity classification using IMUs. The IMUs will next be used to obtain longer-term recordings of different activities performed both with and without a trunk exoskeleton to determine how the exoskeleton affects a person's posture and behavior.

Acceleration-based Assistive Strategy to Control a Back-support Exoskeleton for Load Handling: Preliminary Evaluation

Industrial active exoskeletons have recently achieved considerable interest, due to their intrinsic versatility compared to passive devices. To achieve this versatility, an important open challenge is the design of appropriate control strategies to automatically modulate the physical assistance according to the activity the user is performing.This work focuses on active back-support exoskeletons. To improve the assistance provided in dynamic situations with respect to state-of-the-art methods, a new strategy making use of the angular acceleration of the user's trunk is presented.The feasibility and effectiveness of the proposed strategy were tested experimentally on a prototype in a load handling task. The main advantages in terms of assistive torque profiles emerge during the transition phases of the movement (i.e. beginning and end of lowering and lifting) indicating an appropriate adaptation to the dynamics of the execution.In this preliminary evaluation, the data on peak muscular activity at the spine show promising trends, encouraging further developments and a more detailed evaluation.

Assistive Arm-Exoskeleton Control Based on Human Muscular Manipulability

This paper introduces a novel control framework for an arm exoskeleton that takes into account force of the human arm. In contrast to the conventional exoskeleton controllers where the assistance is provided without considering the human arm biomechanical force manipulability properties, we propose a control approach based on the arm muscular manipulability. The proposed control framework essentially reshapes the anisotropic force manipulability into the endpoint force manipulability that is invariant with respect to the direction in the entire workspace of the arm. This allows users of the exoskeleton to perform tasks effectively in the whole range of the workspace, even in areas that are normally unsuitable due to the low force manipulability of the human arm. We evaluated the proposed control framework with real robot experiments where subjects wearing an arm exoskeleton were asked to move a weight between several locations. The results show that the proposed control framework does not affect the normal movement behavior of the users while effectively reduces user effort in the area of low manipulability. Particularly, the proposed approach augments the human arm force manipulability to execute tasks equally well in the entire workspace of the arm.