Upper Limb Muscle Strength Analysis For Movement Sequence Based on Maximum Voluntary Contraction Using EMG Signal

Cahyadi, B. N.; Zunaidi, I.; Bakar, Shahriman Abu; Khairunizam, Wan; Majid, S. H.; Razlan, Z. M.; Muhammad, M. Nor; Rudzuan, M. N.; Mustafa, Wan Azani

doi:10.1109/icassda.2018.8477638

Cited by 12 publications

(4 citation statements)

References 5 publications

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“…The MVC (maximum voluntary contraction-maximum amplitude of the signal) normalization is widely used in SEMG signals as an amplitude analysis technique. The results are shown as a percentage (%MVC) of the MVC value that can be used to create a common background when comparing data between subjects [60, 61]. SEMG signals depend on the user and have a structure that can cause records to change even when measured from the same position with the same motion.…”

Section: Methodsmentioning

confidence: 99%

“…SEMG signals depend on the user and have a structure that can cause records to change even when measured from the same position with the same motion. Therefore, MVC normalization is used to eliminate this difference and to enable data comparison between subjects [61]. MVC expresses the highest value obtained in a repeat during this measurement to normalize SEMG signals obtained for a specific purpose, while SMVC (submaximal voluntary contraction) refers to the voluntarily recorded SEMG data.…”

Section: Methodsmentioning

confidence: 99%

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Design and Validation of Multichannel Wireless Wearable SEMG System for Real-Time Training Performance Monitoring

Örücü

Selek

2019

Journal of Healthcare Engineering

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Monitoring of training performance and physical activity has become indispensable these days for athletes. Wireless technologies have started to be widely used in the monitoring of muscle activation, in the sport performance of athletes, and in the examination of training efficiency. The monitorability of performance simultaneously in the process of training is especially a necessity for athletes at the beginner level to carry out healthy training in sports like weightlifting and bodybuilding. For this purpose, a new system consisting of 4 channel wireless wearable SEMG circuit and analysis software has been proposed to detect dynamic muscle contractions and to be used in real-time training performance monitoring and analysis. The analysis software, the Haar wavelet filter with threshold cutting, can provide performance analysis by using the methods of moving RMS and %MVC. The validity of the data obtained from the system was investigated and compared with a biomedical system. In this comparison, 90.95% ± 3.35 for left biceps brachii (BB) and 90.75% ± 3.75 for right BB were obtained. The output of the power and %MVC analysis of the system was tested during the training of the participants at the gym, and the training efficiency was measured as 96.87% ± 2.74.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Design and Validation of Multichannel Wireless Wearable SEMG System for Real-Time Training Performance Monitoring

Örücü

Selek

2019

Journal of Healthcare Engineering

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“…Permainan VR ini dirancang dengan menggunakan metode non-immersive karena metode ini tidak memerlukan kinerja grafis tingkat tertinggi, tidak ada perangkat keras khusus seperti head-mounted display (HMD), biaya rendah, dan dapat digunakan untuk banyak aplikasi. Gerakan dalam permainan ini telah dianalisis pada penelitian sebelumnya mengenai aktivitas otot [16], [17]. Lingkungan VR untuk gerakan lengan dirancang dengan luas ruang 3 x 3 m. Sistem tersebut terdiri dari sensor Kinect, tampilan layar, personal computer (PC), proyektor, dan audio (built-in PC).…”

Section: B Desian Permainan Vr Untuk Rehabilitasi Pasca Strokeunclassified

Analisa Upper-limb Movement Sequence (UMS) menggunakan Absolute Trajectory Error dan Hand Speed Movement untuk Rehabilitasi Pasca Stroke

Cahyadi

Taufiq

Khairunizam

2022

JSC

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2019 World Health Organization informed that the two-thirds of stroke patients have a permanent dissability. Disability disorders can affect performance or daily activities such as eating, drinking, wearing clothes, bathing, and others. Disability caused by stroke can be treated by exercising motor function and muscle strength. Studies in upper extremity rehabilitation report that the most important element in the disability recovery process is monitoring the progress of the rehabilitation itself. To monitor and evaluate upper arm rehabilitation, most therapists still rely on clinical assessments based on ordinal scales or charts and are only monitored in terms of the patient's upper arm movement. In this study, we will analyze the rehabilitation movement based on virtual reality games using the Absolute Trajectory Error and Hand Speed Movement methods. While the rehabilitation movement used in this study will apply the Upper-limb Movement Sequence (UMS) method. 5 subjects contributed to data collection over three sessions and five iterations. Their movements were recorded using the Kinect Xbox V2 sensor with 10 Hz sampling data. Mean absolute trajectory error (ATE) and hand speed method were used to analyze arm movements during VR games. Although this study used healthy subjects, 80% of them experienced an increase in movement, and this condition was evidenced by a decrease in ATE values in each session. Trajectory data can be used as the basis for analysis of arm movements during the VR game rehabilitation process, where with these data errors in hand position, hand speed to reach the target, and movement errors can be analyzed more deeply. Moreover, the mean ATE and hand speed movements can show the progress or changes in hand movement during the rehabilitation process clearly.

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“…In addition, hybrid time-frequency features are proposed to overcome the limitation of time features, which relies in stationary properties of the EMG signal. These features are less applied due to computation costs, and are on time-frequency methods such as Discrete Wavelet Transform and Wavelet Packet Transform (Phinyomark et al, 2012;Nazmi et al, 2016) Comment on current/potential applications: Currently, EMG features are being used to the enhancement of robot-assisted upper limb rehabilitation platforms, by means of using the subject's intentions to generate proper feedback for the robotic system (Cahyadi et al, 2018b;Bouteraa et al, 2020;Khairuddin et al, 2021). In particular, due to their relative low computational cost, their potential combination with machine learning algorithms and other technologies such as virtual reality (Meng et al, 2019) could be the key to develop dynamic rehabilitation devices that can boost the personalization of motor training (Abdallah et al, 2017;Arteaga et al, 2020;Samuel et al, 2021) 4.…”

Section: Emg Time and Frequency Domain Featuresmentioning

confidence: 99%

Neuromechanical Biomarkers for Robotic Neurorehabilitation

et al. 2021

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One of the current challenges for translational rehabilitation research is to develop the strategies to deliver accurate evaluation, prediction, patient selection, and decision-making in the clinical practice. In this regard, the robot-assisted interventions have gained popularity as they can provide the objective and quantifiable assessment of the motor performance by taking the kinematics parameters into the account. Neurophysiological parameters have also been proposed for this purpose due to the novel advances in the non-invasive signal processing techniques. In addition, other parameters linked to the motor learning and brain plasticity occurring during the rehabilitation have been explored, looking for a more holistic rehabilitation approach. However, the majority of the research done in this area is still exploratory. These parameters have shown the capability to become the “biomarkers” that are defined as the quantifiable indicators of the physiological/pathological processes and the responses to the therapeutical interventions. In this view, they could be finally used for enhancing the robot-assisted treatments. While the research on the biomarkers has been growing in the last years, there is a current need for a better comprehension and quantification of the neuromechanical processes involved in the rehabilitation. In particular, there is a lack of operationalization of the potential neuromechanical biomarkers into the clinical algorithms. In this scenario, a new framework called the “Rehabilomics” has been proposed to account for the rehabilitation research that exploits the biomarkers in its design. This study provides an overview of the state-of-the-art of the biomarkers related to the robotic neurorehabilitation, focusing on the translational studies, and underlying the need to create the comprehensive approaches that have the potential to take the research on the biomarkers into the clinical practice. We then summarize some promising biomarkers that are being under investigation in the current literature and provide some examples of their current and/or potential applications in the neurorehabilitation. Finally, we outline the main challenges and future directions in the field, briefly discussing their potential evolution and prospective.

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Upper Limb Muscle Strength Analysis For Movement Sequence Based on Maximum Voluntary Contraction Using EMG Signal

Cited by 12 publications

References 5 publications

Design and Validation of Multichannel Wireless Wearable SEMG System for Real-Time Training Performance Monitoring

Design and Validation of Multichannel Wireless Wearable SEMG System for Real-Time Training Performance Monitoring

Analisa Upper-limb Movement Sequence (UMS) menggunakan Absolute Trajectory Error dan Hand Speed Movement untuk Rehabilitasi Pasca Stroke

Neuromechanical Biomarkers for Robotic Neurorehabilitation

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