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

Latent Space Representation of Human Movement: Assessing the Effects of Fatigue

Fatigue plays a critical role in sports science, significantly affecting recovery, training effectiveness, and overall athletic performance. Understanding and predicting fatigue is essential to optimize training, prevent overtraining, and minimize the risk of injuries. The aim of this study is to leverage Human Activity Recognition (HAR) through deep learning methods for dimensionality reduction. The use of Adversarial AutoEncoders (AAEs) is explored to assess and visualize fatigue in a two-dimensional latent space, focusing on both semi-supervised and conditional approaches. By transforming complex time-series data into this latent space, the objective is to evaluate motor changes associated with fatigue within the participants' motor control by analyzing shifts in the distribution of data points and providing a visual representation of these effects. It is hypothesized that increased fatigue will cause significant changes in point distribution, which will be analyzed using clustering techniques to identify fatigue-related patterns. The data were collected using a Wii Balance Board and three Inertial Measurement Units, which were placed on the hip and both forearms (distal part, close to the wrist) to capture dynamic and kinematic information. The participants followed a fatigue-inducing protocol that involved repeating sets of 10 repetitions of four different exercises (Squat, Right Lunge, Left Lunge, and Plank Jump) until exhaustion. Our findings indicate that the AAE models are effective in reducing data dimensionality, allowing for the visualization of fatigue's impact within a 2D latent space. The latent space representation provides insights into motor control variations, revealing patterns that can be used to monitor fatigue levels and optimize training or rehabilitation programs.

Deep learning-based detection of affected body parts in Parkinson's disease and freezing of gait using time-series imaging

We proposed a deep learning method using a convolutional neural network on time-series (TS) images to detect and differentiate affected body parts in people with Parkinson's disease (PD) and freezing of gait (FOG) during 360° turning tasks. The 360° turning task was performed by 90 participants (60 people with PD [30 freezers and 30 nonfreezers] and 30 age-matched older adults (controls) at their preferred speed. The position and acceleration underwent preprocessing. The analysis was expanded from temporal to visual data using TS imaging methods. According to the PD vs. controls classification, the right lower third of the lateral shank (RTIB) on the least affected side (LAS) and the right calcaneus (RHEE) on the LAS were the most relevant body segments in the position and acceleration TS images. The RHEE marker exhibited the highest accuracy in the acceleration TS images. The identified markers for the classification of freezers vs. nonfreezers vs. controls were the left lateral humeral epicondyle (LELB) on the more affected side and the left posterior superior iliac spine (LPSI). The LPSI marker in the acceleration TS images displayed the highest accuracy. This approach could be a useful supplementary tool for determining PD severity and FOG.

Modified Squat Test for Predicting Knee Muscle Strength in Older Adults

BACKGROUND

Methods for evaluating the strength of the knee extensor muscles play a vital role in determining the functionality of the lower limbs and monitoring any alterations that occur over time in older individuals. This study assessed the validity of the Modified Squat Test (MST) in predicting knee extensor muscle strength in older adults.

METHODS

This study included a total of 110 older adults. We collected demographic information such as sex, age, body weight, height, and thigh circumference. Muscle strength was assessed by measuring the maximum voluntary isometric contraction of the knee extensors, and by performing the MST (5 and 10 repetitions) and single-leg standing balance test. Stepwise multiple linear regression analysis was used to investigate multiple factors impacting the prediction of knee extensor strength.

RESULTS

Factors such as age, sex, thigh circumference, performance on the single-leg standing eye-open (SSEO) task, and the time required to complete the 10 MST repetitions together explained 77.8% of the variation in knee extensor muscle strength among older adults. We further developed a predictive equation to calculate strength as follows: strength = 36.78 - 0.24 (age) + 6.16 (sex) + 0.19 (thigh circumference) + 0.05 (SSEO) - 0.54 (time required to complete 10 MST repetitions) ± 5.51 kg.

CONCLUSION

The 10-repetition MST is an invaluable instrument for establishing an equation to accurately predict lower limb muscle strength.

Assessment and indicators of kinematic behavior and perceived fatigability

OBJECTIVE

The objective of this study was to investigate the relationship between upper limb kinetics and perceived fatigability in elderly individuals during an upper limb position sustained isometric task.

METHODS

A total of 31 elderly participants, 16 men (72.94±4.49 years) and 15 women (72.27±6.05 years), performed a upper limb position sustained isometric task. Upper-limb acceleration was measured using an inertial measurement unit. Perceived fatigability was measured using the Borg CR10 scale.

RESULTS

Higher mean acceleration in the x-axis throughout the activity was associated with higher final perceived fatigability scores. Moderate correlations were observed between perceived fatigability variation and mean acceleration cutoffs in all axes during the second half of the activity. In women, significant correlations were found between all perceived fatigability cutoffs and mean acceleration in the y- and x-axes. However, in men, the relationships between perceived fatigability variation and mean acceleration were more extensive and stronger.

CONCLUSION

The acceleration pattern of the upper limb is linked to perceived fatigability scores and variation, with differences between sexes. Monitoring upper limb acceleration using a single inertial measurement unit can be a useful and straightforward method for identifying individuals who may be at risk of experiencing high perceived fatigability or task failure.

AI-Assisted Fatigue and Stamina Control for Performance Sports on IMU-Generated Multivariate Times Series Datasets

BACKGROUND

Optimal sports performance requires a balance between intensive training and adequate rest. IMUs provide objective, quantifiable data to analyze performance dynamics, despite the challenges in quantifying athlete training loads. The ability of AI to analyze complex datasets brings innovation to the monitoring and optimization of athlete training cycles. Traditional techniques rely on subjective assessments to prevent overtraining, which can lead to injury and underperformance. IMUs provide objective, quantitative data on athletes' physical status during action. AI and machine learning can turn these data into useful insights, enabling data-driven athlete performance management. With IMU-generated multivariate time series data, this paper uses AI to construct a robust model for predicting fatigue and stamina.

MATERIALS AND METHODS

IMUs linked to 19 athletes recorded triaxial acceleration, angular velocity, and magnetic orientation throughout repeated sessions. Standardized training included steady-pace runs and fatigue-inducing techniques. The raw time series data were used to train a supervised ML model based on frequency and time-domain characteristics. The performances of Random Forest, Gradient Boosting Machines, and LSTM networks were compared. A feedback loop adjusted the model in real time based on prediction error and bias estimation.

RESULTS

The AI model demonstrated high predictive accuracy for fatigue, showing significant correlations between predicted fatigue levels and observed declines in performance. Stamina predictions enabled individualized training adjustments that were in sync with athletes' physiological thresholds. Bias correction mechanisms proved effective in minimizing systematic prediction errors. Moreover, real-time adaptations of the model led to enhanced training periodization strategies, reducing the risk of overtraining and improving overall athletic performance.

CONCLUSIONS

In sports performance analytics, the AI-assisted model using IMU multivariate time series data is effective. Training can be tailored and constantly altered because the model accurately predicts fatigue and stamina. AI models can effectively forecast the beginning of weariness before any physical symptoms appear. This allows for timely interventions to prevent overtraining and potential accidents. The model shows an exceptional ability to customize training programs according to the physiological reactions of each athlete and enhance the overall training effectiveness. In addition, the study demonstrated the model's efficacy in real-time monitoring performance, improving the decision-making abilities of both coaches and athletes. The approach enables ongoing and thorough data analysis, supporting strategic planning for training and competition, resulting in optimized performance outcomes. These findings highlight the revolutionary capability of AI in sports science, offering a future where data-driven methods greatly enhance athlete training and performance management.

Identification of runner fatigue stages based on inertial sensors and deep learning

Introduction: Running is one of the most popular sports in the world, but it also increases the risk of injury. The purpose of this study was to establish a modeling approach for IMU-based subdivided action pattern evaluation and to investigate the classification performance of different deep models for predicting running fatigue. Methods: Nineteen healthy male runners were recruited for this study, and the raw time series data were recorded during the pre-fatigue, mid-fatigue, and post-fatigue states during running to construct a running fatigue dataset based on multiple IMUs. In addition to the IMU time series data, each participant's training level was monitored as an indicator of their level of physical fatigue. Results: The dataset was examined using single-layer LSTM (S_LSTM), CNN, dual-layer LSTM (D_LSTM), single-layer LSTM plus attention model (LSTM + Attention), CNN, and LSTM hybrid model (LSTM + CNN) to classify running fatigue and fatigue levels. Discussion: Based on this dataset, this study proposes a deep learning model with constant length interception of the raw IMU data as input. The use of deep learning models can achieve good classification results for runner fatigue recognition. Both CNN and LSTM can effectively complete the classification of fatigue IMU data, the attention mechanism can effectively improve the processing efficiency of LSTM on the raw IMU data, and the hybrid model of CNN and LSTM is superior to the independent model, which can better extract the features of raw IMU data for fatigue classification. This study will provide some reference for many future action pattern studies based on deep learning.

A Data-Driven Approach for Fatigue Detection during Running Using Pedobarographic Measurements

Background

Detecting fatigue at the early stages of a run could aid training programs in making adjustments, thereby reducing the heightened risk of injuries from overuse. The study aimed to investigate the effects of running fatigue on plantar force distribution in the dominant and nondominant feet of amateur runners.

Methods

Thirty amateur runners were recruited for this study. Bilateral time-series plantar forces were employed to facilitate automatic fatigue gait recognition using convolutional neural network (CNN) and CNN-based long short-term memory network (ConvLSTM) models. Plantar force data collection was conducted both before and after a running-induced fatigue protocol using a FootScan force plate. The Keras library in Python 3.8.8 was used to train and tune deep learning models.

Results

The results demonstrated that more mid-forefoot and heel force occurs during bilateral plantar and less midfoot fore force occurs in the dominant limb after fatigue (p < 0.001). The time of peak forces was significantly shortened at the midfoot and sum region of the nondominant foot, while it was delayed at the hallux region of the dominant foot (p < 0.001). In addition, the ConvLSTM model showed higher performance (Accuracy = 0.867, Sensitivity = 0.874, and Specificity = 0.859) in detecting fatigue gait than CNN (Accuracy = 0.800, Sensitivity = 0.874, and Specificity = 0.718).

Conclusions

The findings of this study could offer empirical data for evaluating risk factors linked to overuse injuries in a single limb, as well as facilitate early detection of fatigued gait.

Microfluidic Wearable Devices for Sports Applications

This study aimed to systematically review the application and research progress of flexible microfluidic wearable devices in the field of sports. The research team thoroughly investigated the use of life signal-monitoring technology for flexible wearable devices in the domain of sports. In addition, the classification of applications, the current status, and the developmental trends of similar products and equipment were evaluated. Scholars expect the provision of valuable references and guidance for related research and the development of the sports industry. The use of microfluidic detection for collecting biomarkers can mitigate the impact of sweat on movements that are common in sports and can also address the issue of discomfort after prolonged use. Flexible wearable gadgets are normally utilized to monitor athletic performance, rehabilitation, and training. Nevertheless, the research and development of such devices is limited, mostly catering to professional athletes. Devices for those who are inexperienced in sports and disabled populations are lacking. Conclusions: Upgrading microfluidic chip technology can lead to accurate and safe sports monitoring. Moreover, the development of multi-functional and multi-site devices can provide technical support to athletes during their training and competitions while also fostering technological innovation in the field of sports science.

Interoceptive and metacognitive facets of fatigue in multiple sclerosis

Numerous disorders are characterised by fatigue as a highly disabling symptom. Fatigue plays a particularly important clinical role in multiple sclerosis (MS) where it exerts a profound impact on quality of life. Recent concepts of fatigue grounded in computational theories of brain-body interactions emphasise the role of interoception and metacognition in the pathogenesis of fatigue. So far, however, for MS, empirical data on interoception and metacognition are scarce. This study examined interoception and (exteroceptive) metacognition in a sample of 71 persons with a diagnosis of MS. Interoception was assessed by prespecified subscales of a standard questionnaire (Multidimensional Assessment of Interoceptive Awareness [MAIA]), while metacognition was investigated with computational models of choice and confidence data from a visual discrimination paradigm. Additionally, autonomic function was examined by several physiological measurements. Several hypotheses were tested based on a preregistered analysis plan. In brief, we found the predicted association of interoceptive awareness with fatigue (but not with exteroceptive metacognition) and an association of autonomic function with exteroceptive metacognition (but not with fatigue). Furthermore, machine learning (elastic net regression) showed that individual fatigue scores could be predicted out-of-sample from our measurements, with questionnaire-based measures of interoceptive awareness and sleep quality as key predictors. Our results support theoretical concepts of interoception as an important factor for fatigue and demonstrate the general feasibility of predicting individual levels of fatigue from simple questionnaire-based measures of interoception and sleep.

Upper-Limb Kinematic Behavior and Performance Fatigability of Elderly Participants Performing an Isometric Task: A Quasi-Experimental Study

Upper-limb position-sustained tasks (ULPSIT) are involved in several activities of daily living and are associated with high metabolic and ventilatory demand and fatigue. In older people, this can be critical to the performance of daily living activities, even in the absence of a disability.

OBJECTIVES

To understand the ULPSIT effects on upper-limb (UL) kinetics and performance fatigability in the elderly.

METHODS

Thirty-one (31) elderly participants (72.61 ± 5.23 years) performed an ULPSIT. The UL average acceleration (AA) and performance fatigability were measured using an inertial measurement unit (IMU) and time-to-task failure (TTF).

RESULTS

The findings showed significant changes in AA in the X- and Z-axes (p < 0.05). AA differences in women started earlier in the baseline cutoff in the X-axis, and in men, started earlier between cutoffs in the Z-axis. TTF was positively related to AA in men until 60% TTF.

CONCLUSIONS

ULPSIT produced changes in AA behavior, indicative of movement of the UL in the sagittal plane. AA behavior is sex related and suggests higher performance fatigability in women. Performance fatigability was positively related to AA only in men, where movement adjustments occurred in an early phase, though with increased activity time.

PERSIST: A Multimodal Dataset for the Prediction of Perceived Exertion during Resistance Training

Measuring and adjusting the training load is essential in resistance training, as training overload can increase the risk of injuries. At the same time, too little load does not deliver the desired training effects. Usually, external load is quantified using objective measurements, such as lifted weight distributed across sets and repetitions per exercise. Internal training load is usually assessed using questionnaires or ratings of perceived exertion (RPE). A standard RPE scale is the Borg scale, which ranges from 6 (no exertion) to 20 (the highest exertion ever experienced). Researchers have investigated predicting RPE for different sports using sensor modalities and machine learning methods, such as Support Vector Regression or Random Forests. This paper presents PERSIST, a novel dataset for predicting PERceived exertion during reSIStance Training. We recorded multiple sensor modalities simultaneously, including inertial measurement units (IMU), electrocardiography (ECG), and motion capture (MoCap). The MoCap data has been synchronized to the IMU and ECG data. We also provide heart rate variability (HRV) parameters obtained from the ECG signal. Our dataset contains data from twelve young and healthy male participants with at least one year of resistance training experience. Subjects performed twelve sets of squats on a Flywheel platform with twelve repetitions per set. After each set, subjects reported their current RPE. We chose the squat exercise as it involves the largest muscle group. This paper demonstrates how to access the dataset. We further present an exploratory data analysis and show how researchers can use IMU and ECG data to predict perceived exertion.

Real-time forecasting of exercise-induced fatigue from wearable sensors

Although a number of studies attempt to classify human fatigue, most models can only identify fatigue after fatigue has already occurred. In this paper, we propose a novel time series approach to forecasting wearable sensor data and associated fatigue progression during exercise. The proposed framework consists of spatio-temporal attention-based Transformer with an auxiliary critic and a fatigue classifier. The Transformer network is used to analyze the person-independent pattern underlying the past kinematic sequence obtained from wearable sensors and generate short term predictions of the human motion. Adversarial training is employed to regularize the Transformer and improve the time series forecasting performance. A fatigue classifier is used to estimate person-independent fatigue levels based on the forecasted wearable sensor data from the Transformer model. The proposed approach is validated with simulated and real squat datasets which were collected from young healthy participants. The proposed network can accurately forecast a time horizon of up to 80 timesteps for motion signal forecasting and fatigue classification. In terms of fatigue prediction, an accuracy of 83% and a Pearson correlation coefficient of 0.92 were achieved on forecasted motion data with unseen participant data. The experimental results show that our model can predict fatigue progression and outperforms other state-of-the-art techniques, achieving 95% correlation compared to 83% for the best performing baseline method. Successfully predicting fatigue progression can help a patient or athlete monitor and adjust their exercise session to prevent overexertion and fatigue-induced injury.

Detection of Horse Locomotion Modifications Due to Training with Inertial Measurement Units: A Proof-of-Concept

Detecting fatigue during training sessions would help riders and trainers to optimize their training. It has been shown that fatigue could affect movement patterns. Inertial measurement units (IMUs) are wearable sensors that measure linear accelerations and angular velocities, and can also provide orientation estimates. These sensors offer the possibility of a non-invasive and continuous monitoring of locomotion during training sessions. However, the indicators extracted from IMUs and their ability to show these locomotion changes are not known. The present study aims at defining which kinematic variables and indicators could highlight locomotion changes during a training session expected to be particularly demanding for the horses. Heart rate and lactatemia were measured to attest for the horse’s fatigue following the training session. Indicators derived from acceleration, angular velocities, and orientation estimates obtained from nine IMUs placed on 10 high-level dressage horses were compared before and after a training session using a non-parametric Wilcoxon paired test. These indicators were correlation coefficients (CC) and root mean square deviations (RMSD) comparing gait cycle kinematics measured before and after the training session and also movement smoothness estimates (SPARC, LDLJ). Heart rate and lactatemia measures did not attest to a significant physiological fatigue. However, the statistics show an effect of the training session (p < 0.05) on many CC and RMSD computed on the kinematic variables, indicating a change in the locomotion with the training session as well as on SPARCs indicators (p < 0.05), and revealing here a change in the movement smoothness both in canter and trot. IMUs seem then to be able to track locomotion pattern modifications due to training. Future research should be conducted to be able to fully attribute the modifications of these indicators to fatigue.

Accelerometer-Based Identification of Fatigue in the Lower Limbs during Cyclical Physical Exercise: A Systematic Review

Physical exercise (PE) is beneficial for both physical and psychological health aspects. However, excessive training can lead to physical fatigue and an increased risk of lower limb injuries. In order to tailor training loads and durations to the needs and capacities of an individual, physical fatigue must be estimated. Different measurement devices and techniques (i.e., ergospirometers, electromyography, and motion capture systems) can be used to identify physical fatigue. The field of biomechanics has succeeded in capturing changes in human movement with optical systems, as well as with accelerometers or inertial measurement units (IMUs), the latter being more user-friendly and adaptable to real-world scenarios due to its wearable nature. There is, however, still a lack of consensus regarding the possibility of using biomechanical parameters measured with accelerometers to identify physical fatigue states in PE. Nowadays, the field of biomechanics is beginning to open towards the possibility of identifying fatigue state using machine learning algorithms. Here, we selected and summarized accelerometer-based articles that either (a) performed analyses of biomechanical parameters that change due to fatigue in the lower limbs or (b) performed fatigue identification based on features including biomechanical parameters. We performed a systematic literature search and analysed 39 articles on running, jumping, walking, stair climbing, and other gym exercises. Peak tibial and sacral acceleration were the most common measured variables and were found to significantly increase with fatigue (respectively, in 6/13 running articles and 2/4 jumping articles). Fatigue classification was performed with an accuracy between 78% and 96% and Pearson's correlation with an RPE (rate of perceived exertion) between r = 0.79 and r = 0.95. We recommend future effort toward the standardization of fatigue protocols and methods across articles in order to generalize fatigue identification results and increase the use of accelerometers to quantify physical fatigue in PE.

Model-based data augmentation for user-independent fatigue estimation

OBJECTIVE

User-independent recognition of exercise-induced fatigue from wearable motion data is challenging, due to inter-participant variability. This study aims to develop algorithms that can accurately estimate fatigue during exercise.

METHODS

A novel approach for wearable sensor data augmentation was used to generate (via OpenSim) a large corpus of simulated wearable human motion data, based on a small corpus of human motion data measured using optical sensors. Simulated data is generated using detailed kinematic modelling with variations based on human anthropometry datasets. Using both the recorded and generated data, we trained three different neural networks (Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), DeepConvLSTM) to perform person-independent fatigue estimation from wearable motion data.

RESULTS

The estimation performance increased with the amount of simulated training data. Accuracy and correlation values were higher with the proposed data augmentation method as compared to other general time series augmentation methods (e.g, rotation, jettering, magnitude wrapping) with the same amount of training data. An accuracy of 87% and a Pearson correlation coefficient of 90% were achieved on unseen data when the DeepConvLSTM model was trained with the proposed augmented dataset.

CONCLUSION

The enlarged dataset significantly improves the prediction of inter-individual fatigue.

SIGNIFICANCE

Appropriate augmentation techniques for biomechanical data can improve model accuracy and reduce the need for expensive data collection.

Prediction of athlete movements using wearable sensors for sports person health monitoring application

Improvement in the data gathering to track the practise environments of the sports performance. Among these, the Internet of Things (IoT) technology with smartphones is increasingly evolving to help people with their health problems. In the world of athletics, wearable devices can provide real-time data to track athletes’ heart rhythms and help athletic activities. The players’ pulse rates change at various positions as they play sport and track their heartbeat, allowing them to understand their fitness and improve a person’s health. Therefore, the study proposes a wearable sensor-based athletic movement prediction (WS-AMP) model. The model uses the deep learning algorithm to effectively classify motions usually extracted from the interactive motion panels and determine how feasible it is to perform wearable sensor data classification. On 523 athletes with nine athletic motions, data on optical motion capture have been obtained. The research performs the deep neural network model’s training and validation, incorporating the convolutional neural network. The experimental study performs the prediction analysis and comparison with existing machine learning models. The experimental above analysis of wearable sensor-based IoT health monitoring of Sport person movements prediction are Abnormal Conditions ratio is 86.65%, Spectrum analysis of heart rate ratio is 87.12%, the Error rate of body maintenance ratio is 83.51%, Mental acuity ratio is 87.10% and finally overall accuracy, and F1 score ratio is 93.80%.