Search results for: Richie Moalosi

Authors: Oanthata Jester Sealetsa, Richie Moalosi

Abstract:

Musculoskeletal Disorders (MSDs) are reported to be amongst the leading contributing factors of low productivity in many industries across the world, and the most affected being New Emerging Economies (NEC) such as Botswana. This is due to lack of expertise and resources to deal with existing ergonomics challenges. This study was aimed to evaluate occupational postures and the prevalence of musculoskeletal disorders among diamond polishers in a diamond company in Botswana. A case study was conducted with about 106 diamond polishers in Gaborone, Botswana. A case study was chosen because it can investigate and explore an issue thoroughly and deeply, and record behaviour over time so changes in behaviour can be identified. The Corlett and Bishop Body Map was used to determine frequency of MSDs symptoms in different body parts of the workers. This was then followed by the use of the Rapid Entire Body Assessment (REBA) to evaluate the occupational postural risks of MSDs. Descriptive statistics, chi square, and logistic regression were used for data analysis. The results of the study reveal that workers experienced pain in the upper back, lower back, shoulders, neck, and wrists with the most pain reported in the upper back (44.6%) and lower back (44.2%). However, the mean REBA score of 6.07 suggests that sawing, bruiting and polishing were the most dangerous processes in diamond polishing. The study recommends that a redesign of the diamond polishing workstations is necessary to accommodate the anthropometry characteristic of Batswana (people from Botswana) to prevent the development of MSDs.

Keywords: assessment, Botswana, diamond polishing, ergonomics, musculoskeletal disorders, occupational postural risks

Procedia PDF Downloads 168

Authors: Apostolos Kalatzis, Ashish Teotia, Vishnunarayan Girishan Prabhu, Laura Stanley

Abstract:

Emotions are functional behaviors influenced by thoughts, stimuli, and other factors that induce neurophysiological changes in the human body. Understanding and classifying emotions are challenging as individuals have varying perceptions of their environments. Therefore, it is crucial that there are publicly available databases and virtual reality (VR) based environments that have been scientifically validated for assessing emotional classification. This study utilized two commercially available VR applications (Guided Meditation VR™ and Richie’s Plank Experience™) to induce acute stress and calm state among participants. Subjective and objective measures were collected to create a validated multimodal dataset and classification scheme for affective state classification. Participants’ subjective measures included the use of the Self-Assessment Manikin, emotional cards and 9 point Visual Analogue Scale for perceived stress, collected using a Virtual Reality Assessment Tool developed by our team. Participants’ objective measures included Electrocardiogram and Respiration data that were collected from 25 participants (15 M, 10 F, Mean = 22.28  4.92). The features extracted from these data included heart rate variability components and respiration rate, both of which were used to train two machine learning models. Subjective responses validated the efficacy of the VR applications in eliciting the two desired affective states; for classifying the affective states, a logistic regression (LR) and a support vector machine (SVM) with a linear kernel algorithm were developed. The LR outperformed the SVM and achieved 93.8%, 96.2%, 93.8% leave one subject out cross-validation accuracy, precision and recall, respectively. The VR assessment tool and data collected in this study are publicly available for other researchers.

Keywords: affective computing, biosignals, machine learning, stress database

Procedia PDF Downloads 126