Researchers at the College of Engineering at Carnegie Mellon University have developed a novel design approach for exoskeletons and prosthetic limbs that incorporates direct feedback from the human body. The findings were published this week in Science.
This technique, called human-in-the-loop optimization, customizes walking assistance for individuals and significantly improves energy economy during walking. The algorithm that enables this optimization represents a landmark achievement in the field of biomechatronics.
“Existing exoskeleton devices, despite their potential, have not improved walking performance as much as we think they should,” said Steven Collins, an associate professor of Mechanical Engineering. “We’ve seen improvements related to computing, hardware, and sensors, but the biggest challenge has remained the human element—we just haven’t been able to guess how they will respond to new devices.”
The software algorithm is combined with versatile emulator hardware that automatically identifies optimal assistance strategies for individuals.
Human-in-the-loop optimization incorporates direct feedback from the human body.
During experiments, each user received a unique pattern of assistance from an exoskeleton worn on one ankle. The algorithm tested their responses to 32 different patterns over the course of an hour, making adjustments based on measurements of their energy use with each pattern. The optimized assistance pattern produced larger benefits than any exoskeleton to date, including devices acting at all joints on both legs.
“When we walk, we naturally optimize coordination patterns for energy efficiency,” said Collins. “Human-in-the-loop optimization acts in a similar way to optimize the assistance provided by wearable devices. We are really excited about this approach, because we think it will dramatically improve energy economy, speed, and balance for millions of people, especially those with disabilities.”
Optimum human input exoskeleton
Exoskeletons can be used to augment human abilities—for example, to lift very heavy loads or to provide greater endurance. For each user, though, a device will need to be adjusted for optimum effect, which can be time-consuming. Zhang et al. show that the human can be included in the optimization process, with real-time adaptation of an ankle exoskeleton (see the Perspective by Malcolm et al.). By using indirect calorimetry to measure metabolic rates, the authors were able to adjust the torque provided by the device while users were walking, running, and carrying a load.
Exoskeletons and active prostheses promise to enhance human mobility, but few have succeeded. Optimizing device characteristics on the basis of measured human performance could lead to improved designs. We have developed a method for identifying the exoskeleton assistance that minimizes human energy cost during walking. Optimized torque patterns from an exoskeleton worn on one ankle reduced metabolic energy consumption by 24.2 ± 7.4% compared to no torque. The approach was effective with exoskeletons worn on one or both ankles, during a variety of walking conditions, during running, and when optimizing muscle activity. Finding a good generic assistance pattern, customizing it to individual needs, and helping users learn to take advantage of the device all contributed to improved economy. Optimization methods with these features can substantially improve performance.
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