Data Valuation Algorithm for Inertial Measurement Unit-Based Human Activity Recognition

Kim, Yeon-Wook; Lee, Sang‐Min

doi:10.3390/s23010184

Cited by 4 publications

(2 citation statements)

References 36 publications

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“…In this algorithm,

could utilize architectures such as convolutional neural networks (CNNs) for spatial feature extraction from the IMU data [ 50 , 51 , 52 ], followed by LSTMs or gated recurrent units (GRUs) to capture temporal patterns. The ensemble

would harness the predictive capabilities of multiple models to improve generalizability and reduce the likelihood of overfitting to the training data.…”

Section: Materials and Methodsmentioning

confidence: 99%

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

Biró,

Cuesta-Vargas,

Szilágyi

2023

Sensors

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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.

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“…In this algorithm,

would harness the predictive capabilities of multiple models to improve generalizability and reduce the likelihood of overfitting to the training data.…”

Section: Materials and Methodsmentioning

confidence: 99%

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

Biró,

Cuesta-Vargas,

Szilágyi

2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Recent results have shown that IMUs and machine learning classifiers are very effective in identifying activities in healthy controls [ 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ]. The accuracies often rely on the number of IMUs used and their location, with the wrist, chest, ankle, and thigh commonly used.…”

Section: Introductionmentioning

confidence: 99%

Measurement of Functional Use in Upper Extremity Prosthetic Devices Using Wearable Sensors and Machine Learning

Bochniewicz

Emmer

Dromerick

et al. 2023

Sensors

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Trials for therapies after an upper limb amputation (ULA) require a focus on the real-world use of the upper limb prosthesis. In this paper, we extend a novel method for identifying upper extremity functional and nonfunctional use to a new patient population: upper limb amputees. We videotaped five amputees and 10 controls performing a series of minimally structured activities while wearing sensors on both wrists that measured linear acceleration and angular velocity. The video data was annotated to provide ground truth for annotating the sensor data. Two different analysis methods were used: one that used fixed-size data chunks to create features to train a Random Forest classifier and one that used variable-size data chunks. For the amputees, the fixed-size data chunk method yielded good results, with 82.7% median accuracy (range of 79.3–85.8) on the 10-fold cross-validation intra-subject test and 69.8% in the leave-one-out inter-subject test (range of 61.4–72.8). The variable-size data method did not improve classifier accuracy compared to the fixed-size method. Our method shows promise for inexpensive and objective quantification of functional upper extremity (UE) use in amputees and furthers the case for use of this method in assessing the impact of UE rehabilitative treatments.

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A Systematic Survey of Data Value: Models, Metrics, Applications and Research Challenges

Bendechache,

Attard,

Ebiele

et al. 2023

IEEE Access

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Data Valuation Algorithm for Inertial Measurement Unit-Based Human Activity Recognition

Cited by 4 publications

References 36 publications

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

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

Measurement of Functional Use in Upper Extremity Prosthetic Devices Using Wearable Sensors and Machine Learning

A Systematic Survey of Data Value: Models, Metrics, Applications and Research Challenges

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