Real-Time Delayed Onset Muscle Soreness (DOMS) Detection in High Intensity Interval Training Using Artificial Neural Network

Setiawan, Rachmad; Hikmah, Nada Fitrieyatul; Agustina, Florence Fedora

doi:10.1109/isitia56226.2022.9855209

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…The main contribution of this study was to develop an algorithm to assess sleep quality by combining ECG and EMG signals using machine learning. In the field of biomedical engineering, machine learning is commonly utilized for classification in applications such as muscular fatigue during exercises [28], multimodal cardiac analysis [29], and classification for liver fibrosis prediction [30].…”

Section: Introductionmentioning

confidence: 99%

Sleep Quality Assessment from Robust Heart and Muscle Fatigue Estimation Using Supervised Machine Learning

2023

IJIES

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Poor sleep quality is a common sign of a variety of sleep and health problems. Thus, sleep quality assessment is necessary as it can be a first-step predictor of physical and mental health. Several studies were completed for this objective. However, no prior study in sleep quality assessment has explored a comprehensive heart rate variability (HRV) analysis by including feature extraction in the time and frequency domain, and nonlinear analysis. This study proposed a full evaluation of sleep quality, by incorporating multiple physiological signs in subjects to detect exhaustion after a period of sleep. The primary contribution was the development of an algorithm to estimate sleep quality based on the combination of electrocardiography (ECG) and electromyography (EMG) signals by using machine learning. HRV analysis of ECG signal included time domain, frequency domain, and non-linear analysis. Mean power frequency (MPF) was extracted from the EMG signal by using spectral analysis. In addition, determination of fatigue level as an indicator of the subject's sleep quality was evaluated with fatigue severity scale (FSS) questionnaire. Based on results, the accuracy values of logistic regression, random forest, knearest neighbor (k-NN), support vector machine (SVM), and SVM with grid-search were 93%, 93%, 93%, 40%, and 100%, respectively. The proposed method was applicable for investigating sleep quality.

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Section: Introductionmentioning

confidence: 99%

Sleep Quality Assessment from Robust Heart and Muscle Fatigue Estimation Using Supervised Machine Learning

2023

IJIES

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“…Intensifying training effectiveness hinges on injury prevention and expediting the recovery process, which plays a pivotal role in harnessing the body's adaptive response to exercise stress (Impellizzeri et al, 2020). Consequently, after intense physical exertion, individuals may encounter exercise-induced muscle damage, a phenomenon commonly recognized as delayed-onset muscle soreness (DOMS) (Barnes, 2023;Setiawan et al, 2022). DOMS, characterized by muscle discomfort or pain, is frequently associated with various forms of muscle contractions, including isometric, concentric, eccentric, or combinations thereof (Mautner & Sussman, 2016;Ruas et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Enhancing recovery from plyometric circuit training: The synergistic impact of dynamic stretching, BCAA supplementation, and sports massage on DOMS

Fitrianto,

Prayoga,

Habibie

et al. 2023

j. Sport Area

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Understanding the significance of efficient post-exercise recovery methods in alleviating muscle damage and addressing delayed onset muscle soreness (DOMS) among athletes following plyometric circuit training. The study aimed to determine the effect of dynamic stretching in water (active pool-based) and sports massage (SM) on muscle damage indices post-plyometric circuit training. A total of 30 participants were divided into three control groups and one experimental group, who were given two recovery protocols (APB + BCAA and sports massage) after the trial. Data were statistically analysed using a one-way ANOVA test with post-test measurements and a Bonferroni test (P < 0.05). It is concluded that statistically the APB + BCAA post-hoc test with SM did not show any significance. However, on average, APB + BCAA was preferable at reducing DOMS symptoms; measurements 3 and 4 in the intervention groups also showed a significant effect on the control group. The study findings underscore the valuable contribution of active recovery in water and the use of supplements in improving initial recovery outcomes after strenuous exercise or sports, aligning with the exercise recovery principle that highlights the inseparable relationship between nutrition and soft tissue modalities.

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“…The standard instrumentation for EMG as described in [9] has a stipulation that EMG instrumentation must consist of amplifiers and filters in the hardware part. Modular EMG instrumentation was also used in study [10] to record EMG signals and analyze fatigue levels. However, there are many simple instrumentation amplifiers that can be used to record EMG signals.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Amplifiers Utilization in Instrumentation to Record Muscle Signals in the Neck for Electrolarynx Applications

Fatoni

Ashriyah²,

Sardjono

et al. 2022

JAREE

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Electrolarynx is an assistive technology commonly used by speech impaired people to speak. The speech impaired people who have lost their larynx (laryngectomee) or have damaged larynx use an electrolarynx device to be able to speak again. The use of the electrolarynx is generally equipped with a button to turn on and start the generation of sound from the electrolarynx. Several studies have tried to use other control methods by using the muscles in the neck, namely the sternohyoid muscle. Activation of the sternohyoid muscle has an influence on sound formation. The sternohyoid muscle is a small and long muscle so recording EMG (electromyograph) signals from this muscle is quite difficult. If the recording process of this signal can be carried out properly, then the electrolarynx control by empowering this muscle will be another solution in using the electrolarynx. In this study, instrumentation amplifier which is an important stage of recording EMG signals of neck muscle was tested and compared. There are two types of instrumentation amplifier tested. The first instrumentation amplifier uses a single IC from IC AD620 while the other is a combination circuit of IC OP07. The EMG signal in the subject's neck muscles was then recorded using the instrumentation amplifiers. The subject will sit down and pronounce the vowels "a", and "i". From the testing process, it was found that the average gain on IC AD620 (minimum 1.74362 volts and maximum 3.70538 volts) was greater than the gain on IC OP07 (minimum 0.57779 volts and maximum 1.71190 volts). IC AD620 also has an overall use area of 5.61 cm2 with the use of 4 external components. Thus, it can be concluded that the best instrumentation amplifier for recording EMG neck muscle for electrolarynx application is by using IC AD620.

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Real-Time Delayed Onset Muscle Soreness (DOMS) Detection in High Intensity Interval Training Using Artificial Neural Network

Cited by 6 publications

References 12 publications

Sleep Quality Assessment from Robust Heart and Muscle Fatigue Estimation Using Supervised Machine Learning

Sleep Quality Assessment from Robust Heart and Muscle Fatigue Estimation Using Supervised Machine Learning

Enhancing recovery from plyometric circuit training: The synergistic impact of dynamic stretching, BCAA supplementation, and sports massage on DOMS

Comparison of Amplifiers Utilization in Instrumentation to Record Muscle Signals in the Neck for Electrolarynx Applications

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