Impact of a passive upper-body exoskeleton on muscle activity, heart rate and discomfort during a carrying task

Abstract

Objective The goal of this study was to compare erector spinae muscle fatigue, upper limb muscle activity, body areas discomfort, and heart rate during a 10-min carrying task with and without a passive upper-body exoskeleton (CarrySuitⓇ) while considering sex influences. Background Passive exoskeletons are commercially available to assist lifting or carrying task. However, evidence of their impact on muscle activity, fatigue, heart rate and discomfort are scarce and/or do not concur during carrying tasks. Method Thirty participants (16 females and 14 male) performed a 10-min, 15kg load-carrying task with and without the exoskeleton in two non-consecutive days. Heart rate, and erector spinae, deltoid, biceps and brachioradialis muscle activity were recorded during the carrying tasks. In addition, erector spinae electromyography during an isometric hold test and discomfort ratings were measured before and after the task. Results While without the exoskeleton upper limb muscle activity increased or remained constant during the carrying task and showing high peak activation for both males and females, a significant activity reduction was observed with the exoskeleton. Low back peak activation, heart rate and discomfort were lower with than without the exoskeleton. In males muscle activation was significantly asymmetric without the exoskeleton and more symmetric with the exoskeleton. Conclusion The tested passive exoskeleton appears to alleviate the physical workload and impact of carrying heavy loads on the upper limbs and lower back for both males and females.

Effects of a passive upper-body exoskeleton on whole-body kinematics, leg muscle activity, and discomfort during a carrying task

“…These concerns underscore the importance of evaluating the biomechanical effects of exoskeletons on users’ leg muscle activity and overall body kinematics. Furthermore, understanding the effects of the CarrySuit Ⓡ on leg muscle activity and whole-body kinematics will complement the validated benefits of previous studies; see [ 10 , 15 ]. Previous studies have highlighted that electromyography and range of motion metrics are relevant objective variables in ergonomic evaluation of exoskeletons [ 17 ].…”

“…Several musculoskeletal disorders, such as at the knee, hip, back, neck, and arms, have been linked to carrying and moving large weights [ 6 – 8 ], highlighting the need for interventions for this activity. However, most passive exoskeletons studies have focused on the lifting part of the task [ 1 , 9 ] and less on carrying and walking with heavy objects [ 10 ].…”

“…To our knowledge, few studies have scientifically evaluated the CarrySuit Ⓡ . A recent publication by our team evaluated erector spinae muscle fatigue, upper limb muscle activity, body areas discomfort, and heart rate during a 10-min carrying task with and without the CarrySuit Ⓡ [ 10 ]. Other authors performed a single-session evaluation of the CarrySuit Ⓡ through shoulder, neck, and lower back muscle activation during 2-minute load carrying on a treadmill [ 15 ].…”

Machine Learning-Based Fatigue Level Prediction for Exoskeleton-Assisted Trunk Flexion Tasks Using Wearable Sensors

“…We investigated the sensitivity of hyperparameters on the metric of accuracy by varying the levels of three parameters: maximum depth (values: 5, 10, 15, 20, 25), learning rate (0.00001, 0.0001, 0.001, 0.01, 0.1), and subsample rate (0.1, 0.3, 0.5, 0.7, 0.9), as shown in Figure 6. Higher values of model accuracy were obtained with lower values of max We investigated the sensitivity of hyperparameters on the metric of accuracy by varying the levels of three parameters: maximum depth (values: 5,10,15,20,25), learning rate (0.00001, 0.0001, 0.001, 0.01, 0.1), and subsample rate (0.1, 0.3, 0.5, 0.7, 0.9), as shown in Figure 6. Higher values of model accuracy were obtained with lower values of max depth and higher values of learning rate and subsample rate.…”

“…Fatigue is measured by recording perceived exertion or by measuring decreases in physical ability, such as the inability to maintain force generation, and the cognitive capacity to perform tasks [4]. While localized fatigue, or muscle fatigue, is typically measured by measuring impacts on muscle activity (e.g., an increase in the peak amplitude of the signal) [5,6], perceived fatigue, or global fatigue, is usually measured using subjective approaches [7,8]. There is a growing need for developing quantitative approaches of measuring perceived fatigue.…”