Walking efficiently with smart springs

Abstract

Numerous assistive exoskeletons have been developed in recent years to assist walking in individuals with and without motor disorders. A standard metric to measure the efficacy of assistance is the change in the metabolic energy cost between unassisted and assisted conditions. Various experimental methods, such as human-in-the-loop optimization, have been developed to find the optimal exoskeleton control to minimize metabolic energy. Such an approach is powerful yet time- and resource-intensive. In this regard, computational methods might complement state-of-the-art experimental approaches. Developing accurate models of the musculoskeletal system and neuromuscular commands could accelerate the development of exoskeletons and improve our understanding of human-exoskeleton interaction. The aims of the thesis were to model and simulate muscle-tendon mechanics and energetics of walking across speeds in unassisted conditions and with the support of ideal exoskeleton assistance with two modes of assistance: motor-based and spring-based actuators. 

The first three studies examined multiple musculoskeletal models, calibration methods of the muscle-tendon architecture, performance criteria for solving the muscle redundancy, and metabolic energy models to accurately estimate muscle excitations, fiber lengths, and metabolic energy cost compared to available experimental data. The musculoskeletal model proposed by Rajagopal et al. with calibrated muscle passive fiber-length curves and personalized Achilles and patellar tendon stiffness provided good agreement with muscle excitations and fiber lengths obtained from electromyographic signal and ultrasound imaging, respectively. Also, among multiple metabolic energy models in the literature, the model proposed by Bhargava et al. best estimated the average metabolic rates of the whole body compared to experimental measurements computed from spiroergometry. With the best estimations of muscle-tendon mechanics and energetics, the relative cost of the stance phase was predicted to increase significantly with walking speeds, and the metabolic cost of ankle plantarflexors was the highest among muscle groups and increased with walking speeds. The fourth study examined the optimal assistance to reduce muscle activations using motor-based and spring-based assistance of ankle plantarflexor, knee extensor, hip flexor, and hip abductor muscle groups. The largest reduction of muscle activation compared to unassisted conditions was obtained with hip flexor assistance with both actuation systems at high walking speeds. The reduction of metabolic rates compared to unassisted conditions was greater with walking speed with motor-based ankle plantarflexor assistance. In contrast, assisting this muscle group with spring-based actuation resulted in lower metabolic cost compared to unassisted conditions as walking speed increased. Interestingly, the decrease in muscle activations did not necessarily imply a reduction of metabolic energy cost compared to unassisted conditions, for instance with spring-based hip flexor and abductor assistance at some walking speeds. Metabolic energy rates during specific periods of the gait cycle were larger than in unassisted conditions due to increased muscle positive work, which is associated with high metabolic cost. 

The computational methods in the thesis might inspire future studies in the field. The software to calibrate muscle-tendon parameters, such as tendon compliance based on electromyography and muscle passive force-length curves based on ultrasound imaging, and to simulate exoskeleton assistance, are available in public repositories and can be adapted to integrate more experimental observations and simulate other motions than walking. Future studies will validate the predicted muscle-tendon mechanics with exoskeleton assistance.

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