A Multi-Dataset Characterization of Window-based Hyperparameters for Deep CNN-driven sEMG Pattern Recognition

The control performance of myoelectric prostheses would not only depend on the feature extraction and classification algorithms but also on interactions of dynamic window-based hyper-parameters (WBHP) used to construct input signals. However, the relationship between these hyper-parameters and how they influence the performance of the convolutional neural networks (CNNs) during motor intent decoding has not been studied. Therefore, we investigated the impact of various combinations of WBHP (window length and overlap) employed for the construction of raw 2-dimensional (2D) surface electromyogram signals on the performance of CNNs when used for motion intent decoding. Moreover, we examined the relationship between the window length of the 2D sEMG and three commonly used CNN kernel sizes. To ensure high confidence in the findings, we implemented three CNNs which are variants of the existing models, and a newly proposed CNN model. Experimental analysis was conducted using three distinct benchmark databases, two from upper limb amputees and one from able-bodied subjects. The results demonstrate that the performance of the CNNs improved as the overlap between consecutively generated 2D signals increased, with 75% overlap yielding the optimal improvement by 12.62% accuracy and 39.60% F1-score compared to no overlap. Moreover, the CNNs performance was better for kernel size of seven than three and five across the databases. For the first time, we have established with multiple evidence that WBHP would substantially impact the decoding outcome and computational complexity of deep neural networks, and we anticipate that this may spur positive advancement in myoelectric control and related fields

Enhanced Deep Transfer Learning Model based on Spatial-Temporal driven Scalograms for Precise Decoding of Motor Intent in Stroke Survivors

Motor function loss greatly impacts post-stroke survivors while performing activities of daily living. In the recent years, intelligent rehabilitation robotics have been proposed to enable the patients recover their lost limb functions. Besides, a large proportion of these robots function in passive mode that only allow users to navigate trajectories that rarely align with their limb movement intent, thus precluding full functional recovery. A potential solution would be to explore utilizing an efficient Transfer Learning based Convolutional Neural Network (TL-CNN) to decode multiple classes of post-stroke patients’ motion intentions towards realizing dexterously active robotic training during rehabilitation. In this regard, we propose and examined for the first time, the use of Spatial-Temporal Descriptor based Continuous Wavelet Transform (STD-CWT) as input to TL-CNN to optimally decode limb movement intent patterns of stroke patients to provide adequate input for active motor training in rehabilitation robots. Importantly, we examined the proposed (STD-CWT) method on three distinct wavelets including the Morse, Amor, and Bump, and compared their decoding outcomes with those of the commonly adopted CWT technique under similar experimental conditions. Our method was validated using electromyogram signals of five stroke survivors who performed up to twenty-two distinct limb motions. The obtained results showed that the proposed technique recorded a significantly higher decoding (p<0.05) and converges faster compared to the commonly adopted method. The proposed method equally recorded obvious class separability for individual movement classes across the stroke patients. Findings from this study suggest that the STD-CWT Scalograms would provide potential inputs for robust decoding of motor intent that may facilitate intuitively active motor training in stroke rehabilitation robots. © 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Food intake and dietary diversity of farming households in Morogoro region,Tanzania

The Tanzanian economy depends heavily on agriculture and hence human labor provides much of the power needed for farming activities. This study was carried out to determine the diversity and dietary adequacy of farming households in four selected districts of Morogoro region in Tanzania. Adult household members from 140 households participated in the study. A 24-hour dietary recall, dietary diversity score and frequency of food consumption tools were used to assess and quantify nutrient intake and adequacy of consumed diets in farming households. Tanzania food composition tables were used to compute estimates of the energy intake, macro and micronutrients consumed by farming households. Analysis was done using SPSS version 18 and Microsoft excel version 10. Cereal food group was consumed in relatively large quantities compared to other food groups in the surveyed households. The contribution of cereal group to energy intake was 75-82%, protein 8-16% and fat 9-14%. Stiff porridge made of maize flour was the mostly consumed cereal dish followed by rice. The mean intake of energy per day was inadequate; the intake of energy for men was 1402 kcal/day while for female was 1347 kcal/day meeting only 52% and 72%, respectively of the recommended energy intake. Generally, the consumption of protein from the animal sources was significantly low in all districts. Ninety-nine percent of the households rarely consumed eggs; 83% rarely consumed meat and poultry. Consumption of milk and milk products was inadequate as 92% of the households indicated that they rarely consumed these products. The intake of fat was also low by 53% compared to the recommended intake for adults. The intake of iron, zinc, and calcium was 40, 53 and 64%, respectively, which was not sufficient to meet daily requirements. Low intake of nutrients was generally attributed to inadequate food intake due to low feeding frequency, poorly diversified diets and sub-optimal practices in food preparation and cooking. The results from surveyed areas indicated that all districts are rich in terms of bio-diversity and food availability, nevertheless the consumption of these foods in the study communities was inadequate with regards to quantity and quality. This situation compromises nutritional status and pre-disposes farming households to diseases and infections hence affects work output, labor productivity and wealth generation. Educating farmers on the importance of consuming diversified and adequate diets from different food groups will improve their nutrition situation and stimulate more production hence increased agricultural productivity.Key words: Dietary adequacy, Dietary diversity, Nutrition status, Tanzania farming household

Valuation of rice postharvest losses in Sub-Saharan Africa and its mitigation strategies

Data on rice harvest and postharvest loss in Sub-Sahara Africa (SSA) is scanty making it difficult for stakeholders to appreciate the loss and set priority areas for loss reduction along the value chain. To address this problem, a protocol was developed and validated for postharvest loss (PHL) quantification in SSA. Quantitative losses at each segment were determined by field measurements. Interactive effect of origin of rice (domestic versus imported) and type of processing (white versus parboiled milled) on rice price in 33 markets in Africa was used to estimate qualitative loss for both white and parboiled milled rice. Total PHL for rice in SSA in 2018 is estimated at about US$ 10.24 billion, representing 47.63% of the expected total production. The highest loss recorded was quantitative loss before and during harvesting, followed by qualitative loss along the entire value chain, quantitative loss during milling, parboiling, threshing in that order, with the lowest being quantitative loss during drying. Priority areas to be targeted for PHL reduction in SSA and some loss mitigation tools and technologies piloted or suitable for SSA are proposed

Teachers' digital competency in using digital lesson content for teaching and learning in secondary schools in Zanzibar

Teachers' digital competence has been emphasised for improving quality education at all levels worldwide. In Zanzibar, the educational policy adopted digital lesson content (DLC) for teaching and learning in ordinary secondary schools. The study investigated the teachers' digital competence and factors that affect their use of DLCs for teaching and learning. One fifty-four (154) secondary school teachers from Wete district were given 5-point Likert Scale questionnaires. Inferential and descriptive statistics were used to analyse the collected data with the help of SPSS version 25. The study found that the teachers' digital competence is high for using it in teaching and learning. Also, it was observed that the digital competence of the teachers was significantly associated with their attitude and in-service training. The paper concludes that using DLCs in third-world countries takes work. However, it is possible if the government and private sectors, such as NGOs, invest seriously in secondary school levels. Also, it recommends that responsible leaders distribute digital devices and plan regular in-service training for teachers on integrating DLCs at secondary schools to cope with the global vision of 2025. Last, the paper recommends further study considering a large sample size of secondary schools in Zanzibar through random sampling techniques

Teachers' pedagogical attitude on using digital lesson contents in teaching and learning in Zanzibar secondary schools

Digital curricula have been emphasized to improve the quality of education at all levels of education. Implementing a new curriculum depends on teachers' attitudes to integrate it during teaching and learning. This study investigated the attitudes of teachers and factors that affect them towards using digital lesson content in Zanzibar, the case of Wete District. One hundred fifty-four secondary school teachers responded to the questionnaire. The SPSS version 25 was used to run the Chi-squared test and descriptive statistics to analyse the data. The overall attitude was calculated by finding the questions' mean value per Likert level. The study found that teachers’ pedagogical attitude is positive, but implementing digital lesson content in Zanzibar is still challenging. It was also observed that the attitude was significantly associated with gender (p=0.0084), whereby female respondents had higher positive attitudes (68.82%) compared to male respondents (47.44%). The paper concludes that most teachers have a positive attitude towards using digital lesson content in teaching in secondary schools. However, they lack enough relevant digital resources that support digital lesson content. Based on that, policymakers, curriculum developers, and other education stakeholders should take serious action to improve the quality of education practice in Zanzibar. Therefore, this paper recommends that the availability of digital lesson content and in-service training may encourage teachers’ pedagogical attitude to integrate digital materials into their everyday teaching process