Tracking Physiological Fatigue in Prolonged Load Carriage Walking Using Phase Space Warping and Smooth Orthogonal Decomposition

Segala, David B.; Chelidze, David; Adams, Albert A.; Schiffman, Jeffrey M.; Hasselquist, Leif

doi:10.1115/imece2008-67329

Cited by 4 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Inertial measurement units (IMUs) are reliable and cheap sensors that are used to capture a person’s acceleration and motion data in real scenarios without the use of external sources or devices [ 58 , 59 , 60 ], allowing to assess the activity performance through estimations of the kinematic and spatiotemporal parameters [ 61 ], and motion analysis [ 62 ]. Although it is possible to identify the person’s fatigue level using these sensors, considering that the kinematic study in fatigue is still an early topic, the use of other physiological parameters like blood lactate [ 63 ], EMG [ 64 ], or even perceived level of fatigue [ 65 ] are widely used to corroborate the results. A motion analysis system is widely used in fatigue estimation due to its high accuracy and robustness in the measurement of the kinematic parameters.…”

Section: Related Workmentioning

confidence: 99%

A Data-Driven Approach to Physical Fatigue Management Using Wearable Sensors to Classify Four Diagnostic Fatigue States

Pinto-Bernal

Cifuentes

Perdomo

et al. 2021

Sensors

View full text Add to dashboard Cite

Physical exercise contributes to the success of rehabilitation programs and rehabilitation processes assisted through social robots. However, the amount and intensity of exercise needed to obtain positive results are unknown. Several considerations must be kept in mind for its implementation in rehabilitation, as monitoring of patients’ intensity, which is essential to avoid extreme fatigue conditions, may cause physical and physiological complications. The use of machine learning models has been implemented in fatigue management, but is limited in practice due to the lack of understanding of how an individual’s performance deteriorates with fatigue; this can vary based on physical exercise, environment, and the individual’s characteristics. As a first step, this paper lays the foundation for a data analytic approach to managing fatigue in walking tasks. The proposed framework establishes the criteria for a feature and machine learning algorithm selection for fatigue management, classifying four fatigue diagnoses states. Based on the proposed framework and the classifier implemented, the random forest model presented the best performance with an average accuracy of ≥98% and F-score of ≥93%. This model was comprised of ≤16 features. In addition, the prediction performance was analyzed by limiting the sensors used from four IMUs to two or even one IMU with an overall performance of ≥88%.

show abstract

Section: Related Workmentioning

confidence: 99%

A Data-Driven Approach to Physical Fatigue Management Using Wearable Sensors to Classify Four Diagnostic Fatigue States

Pinto-Bernal

Cifuentes

Perdomo

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Besides, a multidimensional damage identification approach has been developed based on this concept and validated using numerical model simulation [2] . In addition, David B. Segala, Albert Adams and et.al applied the theory to track and predict the onset of physiologic fatigue in prolonged load carriage walking [3] .…”

Section: Introductionmentioning

confidence: 99%

Application of Phase Space Warping on Damage Tracking for Bearing Fault

Fan

et al. 2012

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Nowadays, the significance of keeping equipment function properly each time is obvious. If equipment fails during its use, it may have disastrous consequences. Estimating remaining useful life (RUL) of equipment is a key to prevent such calamities, improve its reliability, provide security and reduce unnecessary maintenance and operational cost. The evolution and tracking of damage is the foundation of RUL predicting, and also is one of the most important content of mechanical fault diagnosis. Slow-time variable process of mechanical damage would lead the phase space reconstructed by fast-time variable vibrate signals warping. Search the dynamics characteristic law of damage evolution analysis in the phase space, and build the relationship between fast-time variable signals and slow-time variable damage, and then damage evolution tracking is possible. To validate the theory, simulation model of bearing damage evolution is built, the outer-race fault evolution signals is obtained, and the trend of evolution of degradation of bearing fault is described with Phase Space Warping (PSW) theory and Smooth Orthogonal Decomposition (SOD). The results proved the feasibility of the methodology of PSW in damage evolution tracking.

show abstract

Nonlinear Smooth Orthogonal Decomposition of Kinematic Features of Sawing Reconstructs Muscle Fatigue Evolution as Indicated by Electromyography

Segala

Gates

Dingwell

et al. 2011

Journal of Biomechanical Engineering

Self Cite

View full text Add to dashboard Cite

Tracking or predicting physiological fatigue is important for developing more robust training protocols and better energy supplements and/or reducing muscle injuries. Current methodologies are usually impractical and/or invasive and may not be realizable outside of laboratory settings. It was recently demonstrated that smooth orthogonal decomposition (SOD) of phase space warping (PSW) features of motion kinematics can identify fatigue in individual muscle groups. We hypothesize that a nonlinear extension of SOD will identify more optimal fatigue coordinates and provide a lower-dimensional reconstruction of local fatigue dynamics than the linear SOD. Both linear and nonlinear SODs were applied to PSW features estimated from measured kinematics to reconstruct muscle fatigue dynamics in subjects performing a sawing motion. Ten healthy young right-handed subjects pushed a weighted handle back and forth until voluntary exhaustion. Three sets of joint kinematic angles were measured from the right upper extremity in addition to surface electromyography (EMG) recordings. The SOD coordinates of kinematic PSW features were compared against independently measured fatigue markers (i.e., mean and median EMG spectrum frequencies of individual muscle groups). This comparison was based on a least-squares linear fit of a fixed number of the dominant SOD coordinates to the appropriate local fatigue markers. Between subject variability showed that at most four to five nonlinear SOD coordinates were needed to reconstruct fatigue in local muscle groups, while on average 15 coordinates were needed for the linear SOD. Thus, the nonlinear coordinates provided a one-order-of-magnitude improvement over the linear ones.

show abstract

Tracking Physiological Fatigue in Prolonged Load Carriage Walking Using Phase Space Warping and Smooth Orthogonal Decomposition

Cited by 4 publications

References 0 publications

A Data-Driven Approach to Physical Fatigue Management Using Wearable Sensors to Classify Four Diagnostic Fatigue States

A Data-Driven Approach to Physical Fatigue Management Using Wearable Sensors to Classify Four Diagnostic Fatigue States

Application of Phase Space Warping on Damage Tracking for Bearing Fault

Nonlinear Smooth Orthogonal Decomposition of Kinematic Features of Sawing Reconstructs Muscle Fatigue Evolution as Indicated by Electromyography

Contact Info

Product

Resources

About