Fatigue Evaluation through Machine Learning and a Global Fatigue Descriptor

[Human muscle fatigue monitoring method and its application for exoskeleton interactive control]

Aiming at the human-computer interaction problem during the movement of the rehabilitation exoskeleton robot, this paper proposes an adaptive human-computer interaction control method based on real-time monitoring of human muscle state. Considering the efficiency of patient health monitoring and rehabilitation training, a new fatigue assessment algorithm was proposed. The method fully combined the human neuromuscular model, and used the relationship between the model parameter changes and the muscle state to achieve the classification of muscle fatigue state on the premise of ensuring the accuracy of the fatigue trend. In order to ensure the safety of human-computer interaction, a variable impedance control algorithm with this algorithm as the supervision link was proposed. On the basis of not adding redundant sensors, the evaluation algorithm was used as the perceptual decision-making link of the control system to monitor the muscle state in real time and carry out the robot control of fault-tolerant mechanism decision-making, so as to achieve the purpose of improving wearing comfort and improving the efficiency of rehabilitation training. Experiments show that the proposed human-computer interaction control method is effective and universal, and has broad application prospects.

A Recognition Method for Road Hypnosis Based on Physiological Characteristics

Road hypnosis is a state which is easy to appear frequently in monotonous scenes and has a great influence on traffic safety. The effective detection for road hypnosis can improve the intelligent vehicle. In this paper, the simulated experiment and vehicle experiment are designed and carried out to obtain the physiological characteristics data of road hypnosis. A road hypnosis recognition model based on physiological characteristics is proposed. Higher-order spectra are used to preprocess the electrocardiogram (ECG) and electromyography (EMG) data, which can be further fused by principal component analysis (PCA). The Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), and K-Nearest Neighbor (KNN) models are constructed to identify road hypnosis. The proposed model has good identification performance on road hypnosis. It provides more alternative methods and technical support for real-time and accurate identification of road hypnosis. It is of great significance to improve the intelligence and active safety of intelligent vehicles.

A Classification Method for Workers' Physical Risk

In Industry 4.0 scenarios, wearable sensing allows the development of monitoring solutions for workers' risk prevention. Current approaches aim to identify the presence of a risky event, such as falls, when it has already occurred. However, there is a need to develop methods capable of identifying the presence of a risk condition in order to prevent the occurrence of the damage itself. The measurement of vital and non-vital physiological parameters enables the worker's complex state estimation to identify risk conditions preventing falls, slips and fainting, as a result of physical overexertion and heat stress exposure. This paper aims at investigating classification approaches to identify risk conditions with respect to normal physical activity by exploiting physiological measurements in different conditions of physical exertion and heat stress. Moreover, the role played in the risk identification by specific sensors and features was investigated. The obtained results evidenced that k-Nearest Neighbors is the best performing algorithm in all the experimental conditions exploiting only information coming from cardiorespiratory monitoring (mean accuracy 88.7±7.3% for the model trained with max(HR), std(RR) and std(HR)).

Effects of Knee Flexion Angles on the Joint Force and Muscle Force during Bridging Exercise: A Musculoskeletal Model Simulation

Bridging exercise is commonly used to increase the strength of the hip extensor and trunk muscles in physical therapy practice. However, the effect of lower limb positioning on the joint and muscle forces during the bridging exercise has not been analyzed. The purpose of this study was to use a musculoskeletal model simulation to examine joint and muscle forces during bridging at three different knee joint angle positions. Fifteen healthy young males (average age: 23.5 ± 2.2 years) participated in this study. Muscle and joint forces of the lumbar spine and hip joint during the bridging exercise were estimated at knee flexion angles of 60°, 90°, and 120° utilizing motion capture data. The lumbar joint force and erector spinae muscle force decreased significantly as the angle of the knee joint increased. The resultant joint forces were 200.0 ± 23.2% of body weight (%BW), 174.6 ± 18.6% BW, and 150.5 ± 15.8% BW at 60°, 90°, and 120° knee flexion angles, respectively. On the other hand, the hip joint force, muscle force of the gluteus maxims, and adductor magnus tended to increase as the angle of the knee joint increased. The resultant joint forces were 274.4 ± 63.7% BW, 303.9 ± 85.8% BW, and 341.1 ± 85.7% BW at a knee flexion angle of 60°, 90°, and 120°, respectively. The muscle force of the biceps femoris decreased significantly with increased knee flexion during the bridging exercise. In conclusion, the knee flexion position during bridging exercise has different effects on the joint and muscle forces around the hip joint and lumbar spine. These findings would help clinicians prescribe an effective bridging exercise that includes optimal lower limb positioning for patients who require training of back and hip extensor muscles.

Heart Rate Variability-Based Subjective Physical Fatigue Assessment

Accurate assessment of physical fatigue is crucial to preventing physical injury caused by excessive exercise, overtraining during daily exercise and professional sports training. However, as a subjective feeling of an individual, physical fatigue is difficult for others to objectively evaluate. Heart rate variability (HRV), which is derived from electrocardiograms (ECG) and controlled by the autonomic nervous system, has been demonstrated to be a promising indicator for physical fatigue estimation. In this paper, we propose a novel method for the automatic and objective classification of physical fatigue based on HRV. First, a total of 24 HRV features were calculated. Then, a feature selection method was proposed to remove useless features that have a low correlation with physical fatigue and redundant features that have a high correlation with the selected features. After feature selection, the best 11 features were selected and were finally used for physical fatigue classifying. Four machine learning algorithms were trained to classify fatigue using the selected features. The experimental results indicate that the model trained using the selected 11 features could classify physical fatigue with high accuracy. More importantly, these selected features could provide important information regarding the identification of physical fatigue.

Enhanced cardiac vagal tone in mental fatigue: Analysis of heart rate variability in Time-on-Task, recovery, and reactivity

Heart Rate Variability (HRV) has been suggested as a useful tool to assess fatigue-sensitive psychological operations. The present study uses a between and within-subject design with a cognitively demanding task and a documentary viewing condition, to examine the temporal profile of HRV during reactivity, Time-on-Task (ToT), and recovery. In the cognitive task group, participants worked on a bimodal 2-back task with a game-like character (the Gatekeeper task) for about 1.5 hours, followed by a 12-minute break, and a post-break block of performance (about 18 min). In the other group, participants watched documentaries. We hypothesized an increasing vagal-mediated HRV as a function of Time spent on the Gatekeeper task and no HRV change in the documentary viewing group. We also analyzed the trial-based post-response cardiac activity as a physiological associate of task-related motivation. Relative to the documentary-viewing, ToT was associated with an elevated level of subjective fatigue, decreased heart rate, and increased HRV, particularly in the vagal-mediated components. Based on fatigued participants' post-error cardiac slowing, and post-error reaction time analyses, we found no evidence for motivation deficits. The present findings suggest that the parasympathetic branch of the autonomous nervous system functioning as a relaxation system tends to be activated under increasing mental fatigue. In addition, the study shows that many HRV indices also seem to change when individuals are engaged in a prolonged, less fatiguing activity (e.g. documentary viewing). This finding emphasizes the relevance of comparisons/control conditions in ToT experiments.

Chen B. A Data-Driven Approach to Predict Fatigue in Exercise Based on Motion Data from Wearable Sensors or Force Plate

Fatigue increases the risk of injury during sports training and rehabilitation. Early detection of fatigue during exercises would help adapt the training in order to prevent over-training and injury. This study lays the foundation for a data-driven model to automatically predict the onset of fatigue and quantify consequent fatigue changes using a force plate (FP) or inertial measurement units (IMUs). The force plate and body-worn IMUs were used to capture movements associated with exercises (squats, high knee jacks, and corkscrew toe-touch) to estimate participant-specific fatigue levels in a continuous fashion using random forest (RF) regression and convolutional neural network (CNN) based regression models. Analysis of unseen data showed high correlation (up to 89%, 93%, and 94% for the squat, jack, and corkscrew exercises, respectively) between the predicted fatigue levels and self-reported fatigue levels. Predictions using force plate data achieved similar performance as those with IMU data; the best results in both cases were achieved with a convolutional neural network. The displacement of the center of pressure (COP) was found to be correlated with fatigue compared to other commonly used features of the force plate. Bland-Altman analysis also confirmed that the predicted fatigue levels were close to the true values. These results contribute to the field of human motion recognition by proposing a deep neural network model that can detect fairly small changes of motion data in a continuous process and quantify the movement. Based on the successful findings with three different exercises, the general nature of the methodology is potentially applicable to a variety of other forms of exercises, thereby contributing to the future adaptation of exercise programs and prevention of over-training and injury as a result of excessive fatigue.