Comparative Analysis of Supervised Classifiers for the Evaluation of Sarcopenia Using a sEMG-Based Platform

Leone, Alessandro; Rescio, Gabriele; Manni, Andrea; Siciliano, P.; Caroppo, Andrea

doi:10.3390/s22072721

Cited by 16 publications

(12 citation statements)

References 49 publications

(48 reference statements)

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“…Since the primary goal is to develop an early screening tool, high sensitivity should be prioritized when considering the balance between sensitivity and specificity. As such, the optimal operating point (OOP) of ROC was chosen by the rule that it has the highest sensitivity while specificity is above the 0.65 threshold [ 39 ].…”

Section: Resultsmentioning

confidence: 99%

Exploration of a machine learning approach for diagnosing sarcopenia among Chinese community-dwelling older adults using sEMG-based data

Li,

Ou,

et al. 2024

J NeuroEngineering Rehabil

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Background In the practical application of sarcopenia screening, there is a need for faster, time-saving, and community-friendly detection methods. The primary purpose of this study was to perform sarcopenia screening in community-dwelling older adults and investigate whether surface electromyogram (sEMG) from hand grip could potentially be used to detect sarcopenia using machine learning (ML) methods with reasonable features extracted from sEMG signals. The secondary aim was to provide the interpretability of the obtained ML models using a novel feature importance estimation method. Methods A total of 158 community-dwelling older residents (≥ 60 years old) were recruited. After screening through the diagnostic criteria of the Asian Working Group for Sarcopenia in 2019 (AWGS 2019) and data quality check, participants were assigned to the healthy group (n = 45) and the sarcopenic group (n = 48). sEMG signals from six forearm muscles were recorded during the hand grip task at 20% maximal voluntary contraction (MVC) and 50% MVC. After filtering recorded signals, nine representative features were extracted, including six time-domain features plus three time-frequency domain features. Then, a voting classifier ensembled by a support vector machine (SVM), a random forest (RF), and a gradient boosting machine (GBM) was implemented to classify healthy versus sarcopenic participants. Finally, the SHapley Additive exPlanations (SHAP) method was utilized to investigate feature importance during classification. Results Seven out of the nine features exhibited statistically significant differences between healthy and sarcopenic participants in both 20% and 50% MVC tests. Using these features, the voting classifier achieved 80% sensitivity and 73% accuracy through a five-fold cross-validation. Such performance was better than each of the SVM, RF, and GBM models alone. Lastly, SHAP results revealed that the wavelength (WL) and the kurtosis of continuous wavelet transform coefficients (CWT_kurtosis) had the highest feature impact scores. Conclusion This study proposed a method for community-based sarcopenia screening using sEMG signals of forearm muscles. Using a voting classifier with nine representative features, the accuracy exceeds 70% and the sensitivity exceeds 75%, indicating moderate classification performance. Interpretable results obtained from the SHAP model suggest that motor unit (MU) activation mode may be a key factor affecting sarcopenia.

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Section: Resultsmentioning

confidence: 99%

Exploration of a machine learning approach for diagnosing sarcopenia among Chinese community-dwelling older adults using sEMG-based data

Li,

Ou,

et al. 2024

J NeuroEngineering Rehabil

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“…SARC-F is the loss of muscle mass, being classified through scoring through a questionnaire answered by the volunteers. A score greater than four shows the probability of sarcopenia [ 34 ].…”

Section: Methodsmentioning

confidence: 99%

Development of a Biomechanical Device for Parameter Quantification Associated with the Sit-to-Stand Movement

Oliveira

Carvalho²,

Rasteiro

et al. 2023

Sensors

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The “sit-to-stand” (STS) movement is essential during activities of daily living (ADL). In individuals with physical-motor diseases, its execution and repetition increases activity levels, which is crucial for a good motor rehabilitation process and daily training. Interestingly, there are no sit-to-stand devices that allow a quantitative assessment of the key variables that happen during STS, and there is a need to come up with a new device. This work presents a developed biomechanical support device that measures the force of the upper limbs during the STS movement, aiming to motivate and encourage people undergoing physical therapy in the lower limbs. The device uses two instrumented beams and allows real-time visualization of both arms’ applied force and it records the data for post-processing. The device was tested with a well-defined protocol on a group of 34 healthy young volunteers and an elderly group of 16 volunteers from a continuing care unit. The system showed robust strength and stiffness, good usability, and a user interface that acquired and recorded data effectively, allowing one to observe force-time during the execution of the movement through the application interface developed and in recording data for post-processing. Asymmetries in the applied forces in the STS movement between the upper limbs were identified, particularly in volunteers of the continuing care unit. From the application and the registered data, it can be observed that volunteers with motor problems in the lower limbs performed more strength in their arms to compensate. As expected, the maximum average strength of the healthy volunteers (both arms: force = 105 Newton) was higher than that of the volunteers from the continuing care unit (right arm: force = 54 Newton; left arm: force = 56 Newton). Among others, moderate correlations were observed between weight-applied and height-applied forces and there was a moderately high correlation between the Sequential Clinical Assessment of Respiratory Function (SCAR-F score) and time to perform the movement. Based on the obtained results, the developed device can be a helpful tool for monitoring the evaluation of a patient with limitations in the upper and lower limbs. In addition, the developed system allows for easy evolution, such as including a barometric platform and implementing serious games that can stimulate the execution of the STS movement.

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“…Gait Phase Detection [ 116 ]. Evaluation of sarcopenia based on sEMG platform [ 117 ]. Time-frequency muscle synergy estimation based on sEMG [ 118 ].…”

Section: Figurementioning

confidence: 99%

A Review of EMG-, FMG-, and EIT-Based Biosensors and Relevant Human–Machine Interactivities and Biomedical Applications

Zheng

Zhao

et al. 2022

Biosensors

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Wearables developed for human body signal detection receive increasing attention in the current decade. Compared to implantable sensors, wearables are more focused on body motion detection, which can support human–machine interaction (HMI) and biomedical applications. In wearables, electromyography (EMG)-, force myography (FMG)-, and electrical impedance tomography (EIT)-based body information monitoring technologies are broadly presented. In the literature, all of them have been adopted for many similar application scenarios, which easily confuses researchers when they start to explore the area. Hence, in this article, we review the three technologies in detail, from basics including working principles, device architectures, interpretation algorithms, application examples, merits and drawbacks, to state-of-the-art works, challenges remaining to be solved and the outlook of the field. We believe the content in this paper could help readers create a whole image of designing and applying the three technologies in relevant scenarios.

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Comparative Analysis of Supervised Classifiers for the Evaluation of Sarcopenia Using a sEMG-Based Platform

Cited by 16 publications

References 49 publications

Exploration of a machine learning approach for diagnosing sarcopenia among Chinese community-dwelling older adults using sEMG-based data

Exploration of a machine learning approach for diagnosing sarcopenia among Chinese community-dwelling older adults using sEMG-based data

Development of a Biomechanical Device for Parameter Quantification Associated with the Sit-to-Stand Movement

A Review of EMG-, FMG-, and EIT-Based Biosensors and Relevant Human–Machine Interactivities and Biomedical Applications

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