Abstract
Repetitive lifting of heavy loads increases the risk of back pain and even lumbar vertebral injuries to workers. Active exoskeletons can help workers lift loads by providing power assistance, and therefore reduce the moment and force applied on L5/S1 joint of human body when performing lifting tasks. However, most existing active exoskeletons for lifting assistance are unable to automatically detect user's lift movement, which limits the wide application of active exoskeletons in factories. In this paper, we propose a simple but effective lift detection strategy for exoskeleton control. This strategy uses only exoskeleton integrated sensors, without any extra sensors to capture human motion intentions. This makes the lift detection system more practical for applications in manufacturing environments. Seven healthy subjects participated in this research. Three different sessions were carried out, two for training and one for testing the algorithm. In the two training sessions, subjects were asked to wear a hip exoskeleton, controlled in transparent mode, and perform repetitive lifting and a locomotion circuit; lifting was executed with different techniques. The collected data were used to train the lift detection model. In the testing session, the exoskeleton was controlled in order to deliver torque to assist the lifting action, based on the lift detection made by the trained algorithm. The across-subject average accuracy of lift detection during online test was 97.97 ± 1.39% with subject-dependent model. Offline, the algorithm was trained with data acquired from all subjects to verify its performance for subject-independent detection, and an accuracy of 97.48 ± 1.53% was achieved. In addition, timeliness of the algorithm was quantitatively evaluated and the time delay was <160 ms across different lifting speeds. Surface electromyography was also measured to assess the efficacy of the exoskeleton in assisting subjects in performing load lifting tasks. These results validate the promise of applying the proposed lift detection strategy for exoskeleton control aiming at lift assistance.
Highlights
Repetitive lifting of heavy objects is one of the most common factors causing health problems such as low back pain (Punnett et al, 2005) and work-related musculoskeletal disorders
We proposed a simple rule-based lift detection strategy based on sensors embedded in an active pelvis orthosis (APO)
Intention detection is fundamental for the control of assistive exoskeletons, determining whether a user can receive appropriate power assistance from the exoskeleton at the optimal moment
Summary
Repetitive lifting of heavy objects is one of the most common factors causing health problems such as low back pain (Punnett et al, 2005) and work-related musculoskeletal disorders (da Costa and Vieira, 2010). Some passive exoskeletons have been developed and proved to be able to reduce the muscle activities at the low back when performing lifting tasks (Abdoli-e et al, 2006; AbdoliEramaki et al, 2007; Abdoli-e and Stevenson, 2008; Lotz et al, 2009; Wehner et al, 2009; Whitfield et al, 2014; Masood et al, 2016). These systems typically rely on spring-based mechanisms, designed so that the energy stored in the lowering phase is exerted back to the user in the lifting phase. Despite the positive results, such systems cannot generate high forces or torques, and are not versatile for use in tasks different from lifting; for example, when used in walking, they can hinder the movement and cause increased leg muscle activity, discomfort and muscle deconditioning (de Looze et al, 2016)
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