Integrating computer vision in exosuits for adaptive support and reduced muscle strain in industrial environments - Missiroli, Francesco; Mazzoni, Pietro; Lotti, Nicola; Tricomi, Enrica; Braghin, Francesco; Roveda, Loris; Masia, Lorenzo (IEEE Robotics and automation letters, 2024)
Exosuits are wearable technologies that improve physical capabilities and mobility providing support during various activities. Although primarily intended for medical rehabilitation, there is growing interest in utilizing exosuits in industrial environments to prevent work-related musculoskeletal disorders (WMSDs) by ensuring continuous joints support. However, achieving synchronization between the exosuit and human motion, as well as effectively controlling interactions with the surroundings, presents ongoing challenges. The integration of computer vision techniques, particularly object recognition algorithms, can greatly assist exosuits in understanding the user's environment and adapting their behaviour accordingly. To address this issue, we have developed a control strategy for a soft exosuit that employs computer vision to collaboratively offer tailored assistance to the elbow, alleviating joint stress during interactions with objects of various natures and weights. We conducted a study to assess the effectiveness of the integrated system, which merges object recognition and gravity compensation within a built-in structure of the robotic exosuit. The findings confirmed that the suggested solution notably minimized muscle strain during dynamic activities, exhibiting a consistent correlation with the mass of the object being lifted, namely reducing by 45% and 54% respectively the Biceps activity while lifting the MW and HW compared to the 32% of the “Dynamic Arm”. The intention of this contribution is to pave the way for incorporating the vision algorithm, thus enabling a more efficient interaction between the user and the exosuit itself. This includes adapting the control strategy to account for variations in environmental dynamics.
Veröffentlicht: 29. November 2023
Gesehen am 04.04.2024