An active lower-extremity exoskeleton for synchronous mobility assistance in lifting and carrying manual handling task / Sado Fatai - Sado , Fatai (2019)
Lifting and carrying manual handling (MH) works are among the leading causes of occupational injuries worldwide. These works involve frequent postural and dynamic movements like repetitive squatting, lowering, and walking (under load) which are risk factors for the pathogenesis and progression of several cumulative trauma or repetitive motion/strain disorders of the lower extremities and lumbar spine such as knee/hip osteoarthritis, meniscal knee damage, and low back pain. These disorders are degenerative, affect workers productivity, and can lead to lifelong immobility for affected workers. Currently, therapies to address this problem have not been sufficient. Active exoskeletons for human performance augmentation (EHPA) are promising alternative intervention which have potentials to augment workers efficiency and capacity to overcome the causal biomechanical risk factors (e.g. fatigue, weak muscle, etc.) of injuries in lifting/carrying works. However, several stumbling blocks pertaining to design and control technologies currently limit a practical adoption. One prominent challenge has to do with how to effectively control the EHPA to achieve synchronous smooth movement assistance/augmentation for its pilot to have adequate assistive benefit, given diversity in movement biomechanics (i.e. squatting/walking) and movement transitions (i.e. squatting-to-standing-to-walking, etc.) in actual lifting/carrying work situation. In this study, a new 12-DOF lower extremity EHPA called UMExoLEA (Universiti Malaya Exoskeleton for Lower Extremity Augmentation) with 4-active bi-directional brushless DC motor actuation and a novel synchronous mobility control algorithm are developed to provide smooth lower-extremity mobility augmentation in lifting/carrying MH works. The control system can assist wearers’ movement by a new synergy of three controller algorithms: a dual unscented Kalman filter (DUKF) for trajectory estimation and model update, an impedance controller for generation of assistive torque, and a new supervisory control algorithm for pilot movement detection and synchronization with the exoskeleton. Experiments are conducted on 15 participants recruited to perform two industrial MH tasks: a mimicked repetitive lifting of 9.5kg box of loads and repetitive lifting and carrying of the same load in two different modes: with and without exoskeleton assistance. EMG signals taken at three muscles: Vastus Medialis, Rectus Femoris, and Gastrocnemius of the right leg, as well as feedback questionnaires and kinetic/kinematic data were used to evaluate the performance of the exoskeleton. Overall, participants muscular effort and maximum exerted force were reduced from 30% to 60% in both tasks. Participants’ perceived assistance from UMExoLEA was 73.1% (mean score) which also corresponds to their perceived effort reduction. The mean rating of subjective fatigue (on the lower extremity and low back) was significantly higher between 13.6% (SD: 15.0%) to 20.4% (SD: 17.8%) without exoskeleton assistance, whereas with assistance, participants’ rating was virtually zero. Furthermore, movement detection and synchronization received more than 99% success rate in all the repetitive motion tasks.