Estimating Biomechanical Time-Series with Wearable Sensors: A Systematic Review of Machine Learning Techniques

Gurchiek, Reed D.; Cheney, Nicholas; McGinnis, Ryan S.

doi:10.20944/preprints201911.0006.v1

Cited by 14 publications

(1 citation statement)

References 91 publications

(161 reference statements)

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“…Since the 1930s, over 200 theoretically grounded estimating methods have been developed, with many still rooted in traditional theoretical approaches. Emerging estimating methods increasingly integrate machine learning algorithms [17]. Machine learning algorithms have been applied across various domains to address societal challenges.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Incremental Network for Estimating Chinese Musical Vogue Patterns of a Given Artist Over Time

Hou,

Zhang

2024

IEEE Access

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Estimating musical future trends is critical for the Chinese music industry, as it assists artists, producers, record labels, and investors in discerning public musical preferences. This knowledge facilitates market strategy optimization, recommendation system improvement, inventory management, and provides informed support for advertising and sponsorships. The benefits extend beyond enhancing user experience and contributing to cultural studies, as they are also invaluable for the discovery of emerging artists and the sustainable growth of the entire Chinese music ecosystem. Nonetheless, existing models for prolonged musical future trend estimation have been challenged by the severe decay of past information. Inspired by the concept of incremental learning, we propose the Dynamic Incremental Network (DINet). DINet capitalizes on high-dimensional sequential information embedded in the memory states of trained sequence models. It dynamically integrates information from both previous and current time steps when constructing the model to estimate the next time step's input sequence, enabling the incremental transmission of past information and addressing the decay problem in prolonged musical future trend estimation. Leveraging an open-source dataset from a musical pattern estimation contest, ablation experiments and comparative analyses have shown that DINet not only surpasses traditional and baseline models but also outperforms existing musical future trend estimation models, demonstrating its superior performance. In the future, we intend to employ the proposed Dynamic Incremental Method (DIM)an Occam's razor (simple yet effective) mechanism-to further enhance the performance of state-of-the-art musical and other sequential estimation tasks.

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Section: Introductionmentioning

confidence: 99%

Dynamic Incremental Network for Estimating Chinese Musical Vogue Patterns of a Given Artist Over Time

Hou,

Zhang

2024

IEEE Access

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Workout Detection by Wearable Device Data Using Machine Learning

Yoshida

Yuda

2023

Applied Sciences

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There are many reports that workouts relieve daily stress and are effective in improving mental and physical health. In recent years, there has been a demand for quick and easy methods to analyze and evaluate living organisms using biological information measured from wearable sensors. In this study, we attempted workout detection for one healthy female (40 years old) based on multiple types of biological information, such as the number of steps taken, activity level, and pulse, obtained from a wristband-type wearable sensor using machine learning. Data were recorded intermittently for approximately 64 days and 57 workouts were recorded. Workouts adopted for exercise were yoga and the workout duration was 1 h. We extracted 3416 min of biometric information for each of three categories: workout, awake activities (activities other than workouts), and sleep. Classification was performed using random forest (RF), SVM, and KNN. The detection accuracy of RF and SVM was high, and the recall, precision, and F-score values when using RF were 0.962, 0.963, and 0.963, respectively. The values for SVM were 0.961, 0.962, and 0.962, respectively. In addition, as a result of calculating the importance of the feature values used for detection, sleep state (39.8%), skin temperature (33.3%), and pulse rate (13.2%) accounted for approximately 86.3% of the total. By applying RF or SVM to the biological information obtained from the wearable wristband sensor, workouts could be detected every minute with high accuracy.

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Detecting Fatigue during Exoskeleton-Assisted Trunk Flexion Tasks: A Machine Learning Approach

Kuber,

Godbole,

Rashedi

2024

Applied Sciences

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Back-Support Industrial Exoskeletons (BSIEs) can be beneficial in reducing the risk of injury due to overexertion during trunk flexion tasks. Most real-world tasks include complex body movements, leading to mixed outcomes that necessitate field-based methods for detecting overall physical demands. Monitoring fatigue can be beneficial in this regard to ensure that benefits of BSIEs are translated to the real world. Our experiment included 14 participants, who performed 30 repetitions of 45° trunk-flexion while assisted by a BSIE, first without fatigue and then at medium-high back fatigue (7/10 in the Borg scale). We extracted 135 features from recorded muscle activity, trunk motion, and whole-body stability across bending, transition, and retraction portions of each trunk-flexion cycle. Four classification algorithms, namely Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and XGBoost (XGB), were implemented to assess fatigue prediction. XGB (Accuracy: 86.1%, Recall: 86%, Specificity: 86.3%) was effective in classifying fatigue with data obtained from a single EMG sensor located on the lower back (erector spinae) muscle. Meanwhile, stability measures showed high predictability with both RF (92.9%, 91.9%, 94.1%) and XGB (93.5, 94.1%, 93.1%). Findings demonstrate the success of force plates, and when replaced by pressure insoles, they can facilitate real-world fatigue detection during BSIE-assisted trunk-flexion tasks.

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Estimating Biomechanical Time-Series with Wearable Sensors: A Systematic Review of Machine Learning Techniques

Cited by 14 publications

References 91 publications

Dynamic Incremental Network for Estimating Chinese Musical Vogue Patterns of a Given Artist Over Time

Dynamic Incremental Network for Estimating Chinese Musical Vogue Patterns of a Given Artist Over Time

Workout Detection by Wearable Device Data Using Machine Learning

Detecting Fatigue during Exoskeleton-Assisted Trunk Flexion Tasks: A Machine Learning Approach

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