Reducing the metabolic rate of walking and running with a versatile, portable exosuit - Summary

Reducing the metabolic rate of walking and running with a versatile, portable exosuit. Jinsoo Kim et al. Science  Aug 16 2019: Vol. 365, Issue 6454, pp. 668-672. DOI: 10.1126/science.aav7536

Lowering locomotion's metabolic cost

Walking and running require different gaits, with each type of motion putting a greater bias on different muscles and joints. Kim et al. developed a soft, fully portable, lightweight exosuit that is able to reduce the metabolic rate for both running and walking by assisting each motion via the hip extension (see the Perspective by Pons). A waist belt holds most of the mass, thus reducing the cost of carrying the suit. By tracking the motion of the user, the suit is able to switch modes between the two types of motion automatically.

Abstract: Walking and running have fundamentally different biomechanics, which makes developing devices that assist both gaits challenging. We show that a portable exosuit that assists hip extension can reduce the metabolic rate of treadmill walking at 1.5 meters per second by 9.3% and that of running at 2.5 meters per second by 4.0% compared with locomotion without the exosuit. These reduction magnitudes are comparable to the effects of taking off 7.4 and 5.7 kilograms during walking and running, respectively, and are in a range that has shown meaningful athletic performance changes. The exosuit automatically switches between actuation profiles for both gaits, on the basis of estimated potential energy fluctuations of the wearer’s center of mass. Single-participant experiments show that it is possible to reduce metabolic rates of different running speeds and uphill walking, further demonstrating the exosuit’s versatility.

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Humans can walk and run to attain a wider speed range. At low speeds, the metabolic rate of walking is lower than that of running, but this tendency is reversed at higher speeds, such that at high speeds the metabolic rate of running is lower than that of walking. The ability to switch between walking and running allows humans to adopt the gait with the lowest metabolic rate at each speed (fig. S1A) (1, 2). Development of robotic assistive devices that can provide benefits for both walking and running is challenging because of the fundamentally different biomechanics of these gaits (3). In walking, the legs function like inverted pendulums to move the center of mass (CoM), and the gravitational potential energy and kinetic energy fluctuate out of phase (4). Running, meanwhile, can be modeled as a spring–mass system (5–7) with in-phase gravitational potential and kinetic energy fluctuations (4). In walking, the greatest internal joint moments occur at the ankle, and the hip and ankle perform approximately the same amount of positive work. In running, the greatest internal joint moments occur at the knee and ankle, and the ankle performs the largest amount of positive work, followed by the hip (8, 9).

Because of these differences, most research laboratories have developed separate assistive devices for walking (10–12) and running (3, 13–15). Robotic assistive devices have been shown to reduce the metabolic rate of walking below normal biological levels by 7 to 21% by assisting the ankle joint and/or the hip joint (11, 12, 16, 17). Early efforts at reducing the metabolic rate of running have shown increases of 27 to 58% compared with running without an exoskeleton (13, 14). These increases occur in part because the metabolic cost of carrying mass (e.g., a robotic assistive device) during running (18) is greater than that during walking (19, 20), and the penalty for carrying mass on the limbs is further amplified due to increased limb acceleration (21, 22). Nasiri et al. developed an unpowered exoskeleton that reduced the metabolic rate of running by 8% by applying an elastic torque at the hip as a function of interthigh angle (23). However, those authors noted that this design may not be effective during walking because it could disrupt the swing phase.

We hypothesize that assisting walking and running requires customized actuation profiles via an interface with low distal mass and minimal restriction of motion during the unassisted portions of the gait cycle. To achieve these design criteria, we use functional apparel to attach the device to the wearer, with cables that generate moments in concert with the combined moment that results from different biological muscles. We previously developed such an exosuit that reduces the metabolic rate of walking by 14.9% by assisting the ankle and hip (16). In the current study, we aimed to develop and test a lightweight, portable exosuit that assists with hip extension and can switch automatically between actuation profiles for walking and running. We chose to assist hip extension because it is important for both gaits (8, 9, 24) and does not require added mass to distal leg segments.

The textile components of the device consist of a waist belt and two thigh wraps (Fig. 1A, fig. S2, and data S1). Subjective testing of the maximum range of motion shows that the exosuit does not restrict the movements required for walking and running (Fig. 1B). Two electrical motors connected to cables via pulleys apply a tensile force between the waist belt and the thigh wraps to generate an external extension moment around the hip joint (movie S1 and data S2) (3). The entire exosuit weighs 5.0 kg, with 91% of the total system mass carried at the waist (table S1). This design approach minimizes the additional metabolic rate penalty when mass is added distally during walking (25) and running (22) (Fig. 1C). We programmed two separate actuation force profiles for walking and running. The timings of the walking profile and the running profile were selected on the basis of the profiles with the highest reduction in metabolic rate for walking (26) and running (27) in prior studies that used nonportable, tethered hip exosuits. The profile from the walking study was originally designed to approximate the biological hip extension moment, whereas the profile from the running study was designed to approximate the optimal profile from a muscle-driven simulation (24). Using these profiles as starting points, they were then slightly tailored to improve controller robustness and comfort through pilot tests. To allow the wearer to switch seamlessly between walking and running, we used an online classification algorithm that functions on the basis of potential energy fluctuations measured by inertial measurement units (IMUs) (Fig. 2, movie S1, and data S3) (3, 28).

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