Selection of Muscle-Activity-Based Cost Function in Human-in-the-Loop Optimization of Multi-Gait Ankle Exoskeleton Assistance

Abstract

Using "human-in-the-loop" (HIL) optimization can obtain suitable exoskeleton assistance patterns to improve walking economy. However, there are differences in these patterns under different gait conditions, and currently most HIL optimizations use metabolic cost, which requires long periods to be estimated for each control law, as the physiological objective to minimize. We aimed to construct a muscle-activity-based cost function and to find the appropriate initial assistance patterns in HIL optimization of multi-gait ankle exoskeleton assistance. One healthy subject walked assisted by an ankle exoskeleton under nine gait conditions and each condition was the combination of different walking speeds, ground slopes and load weights. Ten assistance patterns were provided for the subject under each gait condition. Then we constructed a cost function based on surface electromyography signals of four lower leg muscles and select the muscle weight combination by using particle swarm optimization algorithm to compose the cost function with maximum differences between different assistance patterns. The mean weights of medial gastrocnemius, lateral gastrocnemius, soleus and tibialis anterior activity under all gait conditions are 0.153, 0.104, 0.953 and 0.145, respectively. Then we verified the effectiveness of this cost function by optimization and validation experiments conducted on four subjects. Our results are expected to guide the selection of muscle-activitybased cost functions and improve the time efficiency of HIL optimization.

Application of Wearable Sensors in Actuation and Control of Powered Ankle Exoskeletons: A Comprehensive Review

“…Phase-based [10,[37][38][39][40][41]43,49, Impedance-based [74,75,102,136,[161][162][163][164][165]180] Metabolic-rate-based [70,96,132,181,182] Reflex-model-based [68,[183][184][185][186][187][188] Proportional-myoelectric-based [11,49,68,89,[189][190][191][192][193][194][195][196][197][198][199][200][201] Adaptive gain proportional-myoelectric-based [133][134][135][202]…”

“…Cable/rope tension Tension sensor [53,182] force sensor [57] load cell [39,58,69,[108][109][110][122][123][124][125][126][127][128][129]180,183,204] strain gauge [72,113,181] Mechanical deflection Potentiometer [10,55,67,108,110,137,146,153,176] encoder [206,207,236] Pressure Pressure sensor [62,63,146,156,158,166,171,173,176,232] Pneumatic muscle force Load cell [11,38,49,50,…”

“…Metabolic-rate-based control [ 70 , 96 , 132 , 181 , 182 ] is perhaps the only variation of phase-based control schemes that takes a physiological input into account in fine-tuning its adaptive gains. Metabolic-rate-based control is a type of human-in-the-loop optimization that aims to select the optimal adaptive values for the control parameters that lead to a minimum steady-state metabolic energy cost during a particular gait mode [ 70 ].…”

Study on the Control Method of Knee Joint Human–Exoskeleton Interactive System

“…Some research has been carried out based on the combination of motion compensation and controller design. Some studies predefined the joint motion trajectory [ 14 ]. However, the gait of the human is not entirely consistent.…”

“…Therefore, the inaccurate estimation of friction affects the assisting effect. Zhang [ 14 ] adopted a force loop control frame based on the position of the inner loop to overcome friction and fixed trajectory interference. Proportional control and damping injection are used in the force loop.…”