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The innovativeness of dressmakers is a concern to respondents to satisfy their clothing needs. The purpose of this study is to determine the criteria that respondents use to judge the quality of clothing and its influences on the innovative ability of dressmakers in the clothing manufacturing industries.

Three hundred and ninety-seven (397) respondents in the Takoradi Metropolis of Ghana filled out a questionnaire, and the results were used to compile data for the study. The sample size was calculated using Miller and Brewer formula. The data was analysed using structural equational modelling with the SmartPLS v.4 software.

The results showed that respondents are very interested in the calibre of clothing produced by their dressmakers. However, the study revealed that when evaluating the quality of a garment, respondents do not simply accept what has been sewn for them but also consider the performance, appearance, fit and shape of the garment. Findings revealed that there was a significant relationship between garment appearance quality (t = 2.605; p < 0.05), garment performance quality (t = 3.915; p < 0.05), garment shape quality (t = 6.248; p <0.05) and fashion innovations. Subsequently, the evaluation of garment fit quality by respondents revealed it does not bring about innovations (t = 1.310; p > 0.05).

The continuous evaluation of custom-made clothing from customers will go a long way towards highlighting the relative criteria they use to evaluate the innovation of dressmakers. This will help improve the creativity of the dressmakers since such feedback will help them understand and innovate their production skills to meet the preferences of customers.

The present study provides an in-depth understanding of how garment quality evaluation by customers influences the innovation of dressmakers in Takoradi, Ghana. The constructs were developed for the study to capture the appropriate data from customers for the study. This presents an evaluation criterion on four garment quality variables imperative for use or modification by other studies.

The paper proposes a privacy-preserving artificial intelligence-enabled video surveillance technology to monitor social distancing in public spaces.

The paper proposes a new Responsible Artificial Intelligence Implementation Framework to guide the proposed solution's design and development. It defines responsible artificial intelligence criteria that the solution needs to meet and provides checklists to enforce the criteria throughout the process. To preserve data privacy, the proposed system incorporates a federated learning approach to allow computation performed on edge devices to limit sensitive and identifiable data movement and eliminate the dependency of cloud computing at a central server.

The proposed system is evaluated through a case study of monitoring social distancing at an airport. The results discuss how the system can fully address the case study's requirements in terms of its reliability, its usefulness when deployed to the airport's cameras, and its compliance with responsible artificial intelligence.

The paper makes three contributions. First, it proposes a real-time social distancing breach detection system on edge that extends from a combination of cutting-edge people detection and tracking algorithms to achieve robust performance. Second, it proposes a design approach to develop responsible artificial intelligence in video surveillance contexts. Third, it presents results and discussion from a comprehensive evaluation in the context of a case study at an airport to demonstrate the proposed system's robust performance and practical usefulness.