Facial expression recognition (FER) is the task of determining a person’s current emotion. It plays an important role in healthcare, marketing, and counselling. With the advancement in deep learning algorithms like Convolutional Neural Network (CNN), the system’s accuracy is improving. A hybrid CNN and k-Nearest Neighbour (KNN) model can improve FER’s accuracy. This paper presents a hybrid CNN-KNN model for FER on the Raspberry Pi 4, where we use CNN for feature extraction. Subsequently, the KNN performs expression recognition. We use the transfer learning technique to build our system with an EfficientNet-Lite model. The hybrid model we propose replaces the Softmax layer in the EfficientNet with the KNN. We train our model using the FER-2013 dataset and compare its performance with different architectures trained on the same dataset. We perform optimization on the Fully Connected layer, loss function, loss optimizer, optimizer learning rate, class weights, and KNN distance function with the k-value. Despite running on the Raspberry Pi hardware with very limited processing power, low memory capacity, and small storage capacity, our proposed model achieves a similar accuracy of 75.26% (with a slight improvement of 0.06%) to the state-of-the-art’s Ensemble of 8 CNN model.

Work-related musculoskeletal disorders (WMSDs) are among the most common disorders in any work sector and industry. Ergonomic risk assessment can reduce the risk of WMSDs. Motion capture that can provide accurate and real-time quantitative data has been widely used as a tool for ergonomic risk assessment. However, most ergonomic risk assessments that use motion capture still depend on the traditional ergonomic risk assessment method, focusing on qualitative data. Therefore, this article aims to provide a view on the ergonomic risk assessment and apply current motion capture technology to understand classical mechanics of physics that include velocity, acceleration, force, and momentum in ergonomic risk assessment. This review suggests that using motion capture technologies with kinetic and kinematic variables, such as velocity, acceleration, and force, can help avoid inconsistency and develop more reliable results in ergonomic risk assessment. Most studies related to the physical measurement conducted with motion capture prefer to use non-optical motion capture because it is a low-cost system and simple experimental setup. However, the present review reveals that optical motion capture can provide more accurate data.

In animal husbandry, the traceability of individual cattle, their health information, and performance records greatly depend on computer vision and image processing-based approaches. However, some of these approaches perform below expectations in obtaining real-time information about individual cattle. No doubt, inaccurate segmentation and incomplete extraction of each cattle object from an image are notable contributory factors. As accurate segmentation is a prerequisite for obtaining real-time information about individual cattle, and since the algorithm of Mask R-CNN relies on the algorithm of simultaneous localization and mapping (SLAM), for the construction of the semantic map, which sometimes exchanges image background for the foreground, there is a need to enhance the available approaches towards achieving precision animal husbandry. To achieve this, an enhanced Mask R-CNN instance segmentation method is proposed to support indistinct boundaries and irregular shapes of cattle bodies. The methods employed in the research are in multiple folds: (1) Pre-enhancement of the image using generalized color Fourier descriptors (GCFD); (2) Provision of optimal filter size that was smaller than ResNet101 (the backbone of Mask R-CNN) for the extraction of smaller and composite features; (3) Utilization of multiscale semantic features using region proposals; (4) A fully connected layer of existing Mask R-CNN integrated with a sub-network for enhanced segmentation and (5) Post-enhancement of the image using Grabcut. Experiments on the datasets of cattle images produced better results when compared to other state-of-the-art methods with 0.93 mAP.

Sustainable development plays a vital role in information and communication technology. In times of pandemics such as COVID-19, vulnerable people need help to survive. This help includes the distribution of relief packages and materials by the government with the primary objective of lessening the economic and psychological effects on the citizens affected by disasters such as the COVID-19 pandemic. However, there has not been an efficient way to monitor public funds’ accountability and transparency, especially in developing countries such as Nigeria. The understanding of public emotions by the government on distributed palliatives is important as it would indicate the reach and impact of the distribution exercise. Although several studies on English emotion classification have been conducted, these studies are not portable to a wider inclusive Nigerian case. This is because Informal Nigerian English (Pidgin), which Nigerians widely speak, has quite a different vocabulary from Standard English, thus limiting the applicability of the emotion classification of Standard English machine learning models. An Informal Nigerian English (Pidgin English) emotions dataset is constructed, pre-processed, and annotated. The dataset is then used to classify five emotion classes (anger, sadness, joy, fear, and disgust) on the COVID-19 palliatives and relief aid distribution in Nigeria using standard machine learning (ML) algorithms. Six ML algorithms are used in this study, and a comparative analysis of their performance is conducted. The algorithms are Multinomial Naïve Bayes (MNB), Support Vector Machine (SVM), Random Forest (RF), Logistics Regression (LR), K-Nearest Neighbor (KNN), and Decision Tree (DT). The conducted experiments reveal that Support Vector Machine outperforms the remaining classifiers with the highest accuracy of 88%. The “disgust” emotion class surpassed other emotion classes, i.e., sadness, joy, fear, and anger, with the highest number of counts from the classification conducted on the constructed dataset. Additionally, the conducted correlation analysis shows a significant relationship between the emotion classes of “Joy” and “Fear”, which implies that the public is excited about the palliatives’ distribution but afraid of inequality and transparency in the distribution process due to reasons such as corruption. Conclusively, the results from this experiment clearly show that the public emotions on COVID-19 support and relief aid packages’ distribution in Nigeria were not satisfactory, considering that the negative emotions from the public outnumbered the public happiness.

Lower back pain is a musculoskeletal disorder that is commonly reported among warehouse workers due to the nature of the work environment and manual handling activities. The objective of this study was to assess the ergonomic risks among warehouse workers carrying out high shelf (HS) and low shelf (LS) binning processes. A questionnaire was used to determine the prevalence of musculoskeletal symptoms, while a marker-based motion capture (MoCap) system worksheet was used to record the participants’ motion and determine the action risk level. A total of 33% of the participants reported lower back pain in the past seven days, based on the Cornell Musculoskeletal Discomfort Questionnaire (CMDQ) results. Analysis of the body velocities showed that the HS binning process had four major velocity peaks, defined as the initial, lowering, lifting, and final phases. In comparison, the LS binning process had two major peaks defined, the crouching and rising phases. There were significant differences between the mean velocities of the workers for the HS binning process, indicating that the workers have different movement patterns with varying velocities.