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  1. Translation, validation and cross-cultural adaptation of the Malay emotion regulation checklist (ERC-M): A preliminary study
    Jamal FN, Dzulkarnain AAA, Shahrudin FA, Musa R, Sidek SN, Yusof HM, et al.
    Med J Malaysia, 2021 09;76(5):680-684.
    PMID: 34508374
    INTRODUCTION: Emotion Regulation Checklist (ERC) has been used globally and translated to several languages, including Brazilian Portuguese, Italian and Persian. The aim of this study is to translate and validate ERC to the Malay language and to measure the reliability and validity of the translated version of this scale among Malaysian parents.

    METHODS: This study involved forward and back translation method. The translated questionnaire was then pretested and piloted among 10 parents and 50 participants, respectively. The procedure was repeated using the same questionnaire to evaluate the test-retest reliability.

    RESULTS: The ERC-Malay (ERC-M) has excellent qualitative and quantitative measurements in both item-level content validation index (I-CVI) and scale-level content validation index (S-CVI). In addition, the ERC-M demonstrated good internal consistency from Cronbach's alpha and test-retest reliability based on the Intraclass Correlation Coefficient (ICC) in all domains.

    CONCLUSION: ERC-M can potentially be used as a tool to evaluate emotion for the population with emotional dysregulation issue, such as autism spectrum disorder.

  2. Fulltext The classification of movement intention through machine learning models: the identification of significant time-domain EMG features
    Mohd Khairuddin I, Sidek SN, P P Abdul Majeed A, Mohd Razman MA, Ahmad Puzi A, Md Yusof H
    PeerJ Comput Sci, 2021;7:e379.
    PMID: 33817026 DOI: 10.7717/peerj-cs.379
    Electromyography (EMG) signal is one of the extensively utilised biological signals for predicting human motor intention, which is an essential element in human-robot collaboration platforms. Studies on motion intention prediction from EMG signals have often been concentrated on either classification and regression models of muscle activity. In this study, we leverage the information from the EMG signals, to detect the subject's intentions in generating motion commands for a robot-assisted upper limb rehabilitation platform. The EMG signals are recorded from ten healthy subjects' biceps muscle, and the movements of the upper limb evaluated are voluntary elbow flexion and extension along the sagittal plane. The signals are filtered through a fifth-order Butterworth filter. A number of features were extracted from the filtered signals namely waveform length (WL), mean absolute value (MAV), root mean square (RMS), standard deviation (SD), minimum (MIN) and maximum (MAX). Several different classifiers viz. Linear Discriminant Analysis (LDA), Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM) and k-Nearest Neighbour (k-NN) were investigated on its efficacy to accurately classify the pre-intention and intention classes based on the significant features identified (MIN and MAX) via Extremely Randomised Tree feature selection technique. It was observed from the present investigation that the DT classifier yielded an excellent classification with a classification accuracy of 100%, 99% and 99% on training, testing and validation dataset, respectively based on the identified features. The findings of the present investigation are non-trivial towards facilitating the rehabilitation phase of patients based on their actual capability and hence, would eventually yield a more active participation from them.
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