AbstractWith product life-cycles getting shorter and limited availability of natural resources, the paradigm shift towards the circular economy is being impulsed. In this domain, the successful adoption of remanufacturing is key. However, its associated process efficiency is to date limited given high flexibility requirements for product disassembly. With the emergence of Industry 4.0, natural human-robot interaction is expected to provide numerous benefits in terms of (re)manufacturing efficiency and cost. In this regard, vision-based and wearable-based approaches are the most extended when it comes to establishing a gesture-based interaction interface. In this work, an experimental comparison of two different movement-estimation systems—(i) position data collected from Microsoft Kinect RGB-D cameras and (ii) acceleration data collected from inertial measurement units (IMUs)—is addressed. The results point to our IMU-based proposal, OperaBLE, having recognition accuracy rates up to 8.5 times higher than these of Microsoft Kinect, which proved to be dependent on the movement’s execution plane, subject’s posture, and focal distance.