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.

The information about affective states in individuals with autism spectrum disorder (ASD) is difficult to obtain as they usually suffer from deficits in facial expression. Affective state conditions of individuals with ASD were associated with impaired regulation of speech, communication, and social skills leading towards poor socio-emotion interaction. It is conceivable that the advance of technology could offer a psychophysiological alternative modality, particularly useful in persons who cannot verbally communicate their emotions as affective states such as individuals with ASD. The study is focusing on the investigation of technology-assisted approach and its relationship to affective states recognition. A systematic review was executed to summarize relevant research that involved technology-assisted implementation to identify the affective states of individuals with ASD using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach. The output from the online search process obtained from six publication databases on relevant studies published up to 31 July 2020 was analyzed. Out of 391 publications retrieved, 20 papers met the inclusion and exclusion criteria set in prior. Data were synthesized narratively despite methodological and heterogeneity variations. In this review, some research methods, systems, equipment and models to address all the related issues to the technology-assisted and affective states concerned were presented. As for the consequence, it can be assumed that the emotion recognition with assisted by technology, for evaluating and classifying affective states could help to improve efficacy in therapy sessions between therapists and individuals with ASD. This review will serve as a concise reference for providing general overviews of the current state-of-the-art studies in this area for practitioners, as well as for experienced researchers who are searching for a new direction for future works.

Children with Autism Spectrum Disorder are identified as a group of people who has difficulties in socio-emotional interaction. Most of them lack the proper context in producing social response through facial expression and speech. Since emotion is the key for effective social interaction, it is justifiably vital for them to comprehend the correct emotion expressions and recognitions. Emotion is a type of affective states and can be detected through physical reaction and physiological signals. In general, recognition of affective states from physical reaction such as facial expression and speech for autistic children is often unpredictable. Hence, an alternative method of identifying the affective states through physiological signals is proposed. Though considered non-invasive, most of the current recognition methods require sensors to be patched on to the skin body to measure the signals. This would most likely cause discomfort to the children and mask their “true” affective states. The study proposed the use of thermal imaging modality as a passive medium to analyze the physiological signals associated with the affective states nonobtrusively. The study hypothesized that, the impact of cutaneous temperature changes due to the pulsating blood flow in the blood vessels at the frontal face area measured from the modality could have a direct impact to the different affective states of autistic children. A structured experimental setup was designed to measure thermal imaging data generated from different affective state expressions induced using different sets of audio-video stimuli. A wavelet-based technique for pattern detection in time series was deployed to spot the changes measured from the region of interest. In the study, the affective state model for typical developing children aged between 5 and 9 years old was used as the baseline to evaluate the performance of the affective state classifier for autistic children. The results from the classifier showed the efficacy of the technique and accorded good performance of classification accuracy at 88% in identifying the affective states of autistic children. The results were momentous in distinguishing basic affective states and the information could provide a more effective response towards improving social-emotion interaction amongst the autistic children.

Human Robot Interaction (HRI) is a multidisciplinary field which involves developing, perceiving and assessing robotic systems. To ensure the effectiveness in communication, the understanding of emotions and intentions is essential. In the development of an emotionally intelligent robot, the issues on how to perceive human affective states and how to manifest the robot's emotion should be addressed. Recently, thermal imaging has exhibits potential solution for non-invasive recording of Autonomic Nervous System (ANS). The ANS works by measuring the spontaneous thermal radiation radiated from human body. In this paper, we present an efficient method for thermal image feature extractions using the Gray Level Co-occurrence Matrix (GLCM) technique. This work attempts to investigate the suitability and sensitivity of the thermal imaging technique for affect detection by analysing the heat pattern on the facial skin. Four region of interests (ROIs), Supraorbital, Periorbital, Nasal and Mouth are looked into where the second order statistical features (Contrast, Correlation, Energy, and Homogeneity) are extracted and used to predict the emotional states. The findings of this study indicates that thermal imaging as an alternative, contactless and non-invasive method for appraising human emotional states.

To map the evidence from the relevant studies regarding the use of music and sound-based intervention for autism spectrum disorder (ASD) using a scoping review study design. Scoping review was conducted according to the inclusion criteria using Google Scholar, PubMed, CINAHL, MEDLINE, and Scopus. The review was accomplished in five steps: 1) identify the inclusion criteria, 2) search for relevant studies, 3) studies selection, 4) data extraction and charting, and 5) data analysis and presentation. Four major themes emerged from 39 studies that matched the inclusion criteria as follows: 1) forms of sound therapy discussing methods of sound therapy and stimulus used, 2) duration of the intervention explain in terms of listening time and total listening sessions, 3) clinical characteristics of the intervention exploring the main interest of sound therapy study in ASD, and 4) evidence for the intervention effectiveness looking into the positive, negative, and mixed findings of previous studies. Each theme was explored to identify the knowledge gaps in sound-intervention therapy. This review demonstrated the need for further studies to address several issues including identifying the effectiveness of sound-therapy intervention for ASD according to the individual sound types, the minimum duration for ASD sound-therapy intervention and more details on the use of technology, and clinical features of the sound-therapy intervention. These elements are important to further demonstrate the effectiveness of sound therapy intervention for ASD children.

The aim of this research was to evaluate the level of greenhouse gas emission from broiler chicken farming industry in Malaysia. In order to achieve that, Life Cycle Assessment method was chosen as a framework to complete the task. A case study was conducted at a broiler chicken farm to gather the data and information related to the broiler chicken production. Cradle-to-gate assessment including distribution stage was conducted based on the ISO14040/1044 guidelines. Inventory data for this case study was gathered in collaboration with one of the selected case study broiler chicken farm company. Greenhouse gas emission that consists of several most affected gases such as carbon dioxide, methane and nitrous oxide was studied. Result shows that the highest carbon dioxide emission came from manure, which accounted for 1,665,342 kg CO2 equivalent per total broilers while the highest methane emission came from feed, which accounted for 126,207.84 g CH4 equivalent per total broilers. For nitrous oxide emission, the highest values came from bedding which accounted for 20,316.87 g N2O equivalent per total broilers in the commercial modern broiler chicken farm. In this case study, it can be concluded that manure gives the most prominent effect to the greenhouse gas emission followed by feed and bedding materials.