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Fatigue is defined as “a loss of force-generating capacity” in a muscle that can intensify tremor. Tremor quantification can facilitate early detection of fatigue onset so that preventative or corrective controls can be taken to minimize work-related injuries and improve the performance of tasks that require high-levels of accuracy. We focused on developing a system that recognizes and classifies voluntary effort and detects phases of fatigue. The experiment was designed to extract and evaluate hand-tremor data during the performance of both rest and effort tasks. The data were collected from the wrist and finger of the participant’s dominant hand. To investigate tremor, time, frequency domain features were extracted from the accelerometer signal for segments of 45 and 90 samples/window. Analysis using advanced signal processing and machine-learning techniques such as decision tree, k-nearest neighbor, support vector machine, and ensemble classifiers were applied to discover models to classify rest and effort tasks and the phases of fatigue. Evaluation of the classifier’s performance was assessed based on various metrics using 5-fold cross-validation. The recognition of rest and effort tasks using an ensemble classifier based on the random subspace and window length of 45 samples was deemed to be the most accurate (96.1%). The highest accuracy (~98%) that distinguished between early and late fatigue phases was achieved using the same classifier and window length.
Lilia Aljihmani; Oussama Kerdjidj; Yibo Zhu; Ranjana K. Mehta; Madhav Erraguntla; Farzan Sasangohar; Khalid Qaraqe. Classification of Fatigue Phases in Healthy and Diabetic Adults Using Wearable Sensor. Sensors 2020, 20, 6897 .
AMA StyleLilia Aljihmani, Oussama Kerdjidj, Yibo Zhu, Ranjana K. Mehta, Madhav Erraguntla, Farzan Sasangohar, Khalid Qaraqe. Classification of Fatigue Phases in Healthy and Diabetic Adults Using Wearable Sensor. Sensors. 2020; 20 (23):6897.
Chicago/Turabian StyleLilia Aljihmani; Oussama Kerdjidj; Yibo Zhu; Ranjana K. Mehta; Madhav Erraguntla; Farzan Sasangohar; Khalid Qaraqe. 2020. "Classification of Fatigue Phases in Healthy and Diabetic Adults Using Wearable Sensor." Sensors 20, no. 23: 6897.
Many materials proposed as bioresorbable. However, in the clinical cardiology practice, they are not often used. This study evaluates the mechanical and corrosion properties of magnesium-based bioresorbable materials and identifies barriers to their implementation in clinical practice. The Embase, Scopus, Springer Link, and Science Direct databases searched up to April 4th, 2018. The magnesium-based materials were classified according to the compound materials used for enrichment. We have summarized the mechanical and corrosion properties separately. Of the 4194 potentially relevant publications, 101 reported systematic reviews. Of these studies, we included 37 in our review of reviews. In 51% of reviews, the authors reported mechanical properties and in 40% corrosion properties.
Lilia Aljihmani; Lejla Alic; Younes Boudjemline; Ziyad M. Hijazi; Bilal Mansoor; Erchin Serpedin; Khalid Qaraqe. Magnesium-Based Bioresorbable Stent Materials: Review of Reviews. Journal of Bio- and Tribo-Corrosion 2019, 5, 26 .
AMA StyleLilia Aljihmani, Lejla Alic, Younes Boudjemline, Ziyad M. Hijazi, Bilal Mansoor, Erchin Serpedin, Khalid Qaraqe. Magnesium-Based Bioresorbable Stent Materials: Review of Reviews. Journal of Bio- and Tribo-Corrosion. 2019; 5 (1):26.
Chicago/Turabian StyleLilia Aljihmani; Lejla Alic; Younes Boudjemline; Ziyad M. Hijazi; Bilal Mansoor; Erchin Serpedin; Khalid Qaraqe. 2019. "Magnesium-Based Bioresorbable Stent Materials: Review of Reviews." Journal of Bio- and Tribo-Corrosion 5, no. 1: 26.