Classifying Lower Extremity Muscle Fatigue During Walking Using Machine Learning and Inertial Sensors

Fatigue in lower extremity musculature is associated with decline in postural stability, motor performance and alters normal walking patterns in human subjects. Automated recognition of lower extremity muscle fatigue condition may be advantageous in early detection of fall and injury risks. Supervised machine learning methods such as Support Vector Machines (SVM) have been previously used for classifying healthy and pathological gait patterns and also for separating old and young gait patterns. In this study we explore the classification potential of SVM in recognition of gait patterns utilizing an inertial measurement unit associated with lower extremity muscular fatigue. Both kinematic and kinetic gait patterns of 17 participants (29±11 years) were recorded and analyzed in normal and fatigued state of walking. Lower extremities were fatigued by performance of a squatting exercise until the participants reached 60% of their baseline maximal voluntary exertion level. Feature selection methods were used to classify fatigue and no-fatigue conditions based on temporal and frequency information of the signals. Additionally, influences of three different kernel schemes (i.e., linear, polynomial, and radial basis function) were investigated for SVM classification. The results indicated that lower extremity muscle fatigue condition influenced gait and loading responses. In terms of the SVM classification results, an accuracy of 96% was reached in distinguishing the two gait patterns (fatigue and no-fatigue) within the same subject using the kinematic, time and frequency domain features. It is also found that linear kernel and RBF kernel were equally good to identify intra-individual fatigue characteristics. These results suggest that intra-subject fatigue classification using gait patterns from an inertial sensor holds considerable potential in identifying “at-risk” gait due to muscle fatigue

Ann Biomed Eng

Fatigue in lower extremity musculature is associated with decline in postural stability, motor performance and alters normal walking patterns in human subjects. Automated recognition of lower extremity muscle fatigue condition may be advantageous in early detection of fall and injury risks. Supervised machine learning methods such as support vector machines (SVMs) have been previously used for classifying healthy and pathological gait patterns and also for separating old and young gait patterns. In this study we explore the classification potential of SVM in recognition of gait patterns utilizing an inertial measurement unit associated with lower extremity muscular fatigue. Both kinematic and kinetic gait patterns of 17 participants (29 \uc2\ub1 11 years) were recorded and analyzed in normal and fatigued state of walking. Lower extremities were fatigued by performance of a squatting exercise until the participants reached 60% of their baseline maximal voluntary exertion level. Feature selection methods were used to classify fatigue and no-fatigue conditions based on temporal and frequency information of the signals. Additionally, influences of three different kernel schemes (i.e., linear, polynomial, and radial basis function) were investigated for SVM classification. The results indicated that lower extremity muscle fatigue condition influenced gait and loading responses. In terms of the SVM classification results, an accuracy of 96% was reached in distinguishing the two gait patterns (fatigue and no-fatigue) within the same subject using the kinematic, time and frequency domain features. It is also found that linear kernel and RBF kernel were equally good to identify intra-individual fatigue characteristics. These results suggest that intra-subject fatigue classification using gait patterns from an inertial sensor holds considerable potential in identifying "at-risk" gait due to muscle fatigue.L30 AG022963/AG/NIA NIH HHS/United StatesL30-AG022963-04/AG/NIA NIH HHS/United StatesR01-OH009222/OH/NIOSH CDC HHS/United States2015-03-01T00:00:00Z24081829PMC394349

A Comparative Study of the Oxygen Uptake Between Nonmotorized and Motorized Treadmills

The purpose of this study was to determine the effects of nonmotorized treadmill walking and motorized treadmill walking on YO, results, measured in ml•kg·\u27·min·•, of males and females, ages 20-30 at Utah State University, Logan, Utah, USA. The participants were required to walk at a pace of 3 miles per hour and 13% grade for a total exercise time of 9 minutes. The exercise time was broken up with 3-minute recovery periods. Oxygen uptake was measured continuously using a metabolic measurement cart. The data obtained from the metabolic cart were correlated for each treadmill to determine the degree of relationship. A 1 test for correlated means was used to determine if there was a significant difference, alpha \u3c 0.05, when measuring YO, and metabolic (MET) results. A significantly low correlational coefficient was found when the Pro form Dual Motion Crosswalk Cross Trainer motorized treadmill (CW TM) V02 and MET results were compared with the Jane Fonda nonmotorized treadmill (Jane TM) and Voit 502 MD nonmotorized treadmill (Voit TM) YO, and MET results (r = 0.3, Q \u3c 0.0001). These results enabled the researchers to reject the null hypotheses, which stated there would be no significant difference and a high positive correlation between nonmotorized and motorized treadmill V02 and MET results. Standard mean difference effect sizes were calculated for the nonmotorized treadmills versus the motorized treadmill. An effect size of 1.62 was found when both nonmotorized treadmills were compared with the motorized treadmill. This, combined with the significant difference, Q \u3c 0.0001 , provided confidence that a Type I error was avoided. Therefore, the results of this research study show a significant difference in V02 and METs measured on a nonmotorized treadmill when compared with a motorized treadmill

PREDICTIVE SIMULATION OF HUMAN MOVEMENT AND APPLICATIONS TO ASSISTIVE DEVICE DESIGN AND CONTROL

Predictive simulation based on dynamic optimization using musculoskeletal models is a powerful approach for studying biomechanics of human gait. Predictive simulation can be used for a variety of applications from designing assistive devices to testing theories of motor controls. However, one of the challenges in formulating the predictive dynamic optimization problem is that the cost function, which represents the underlying goal of the walking task (e.g., minimal energy consumption) is generally unknown and is assumed a priori. While different studies used different cost functions, the qualities of the gaits with those cost functions were often not provided. Therefore, this dissertation evaluates and examines different cost function forms for dynamic simulation of human walking. The problem of the walking cost function determination was cast as a bilevel optimization, which was solved using a nested evolutionary approach. The results showed cost functions based on a weighted combination of muscle-based performance criteria (e.g., metabolic cost, muscle fatigue), gait smoothness, and gait stability led to better walking solutions compared to any cost functions only based on muscle performance criteria. Further evaluations of the walking cost functions were done in the simulation cases of human walking augmented with assistive devices such as prosthesis and exoskeleton. The simulations of augmented walking were comparable to the experimental results, which suggests the potential of using the simulation approach to address problems of finding assistive device design and control