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The purpose of this paper is to map and describe findings from research conducted in workspaces designed to support activity-based working (ABW) over the past 10 years (2010–2020) with a view of informing post-COVID workplaces of the positive and negative attributes of ABW.

Scopus was used as the search engine for this review. Papers which reported findings related to ABW and performed field study in ABW workspaces with adult occupants were included. Out of the 442 initial papers, 40 papers were included following iterative title and abstract and full text review process and consideration of inclusion and exclusion criteria. These papers were divided into three groupings (organizational, human and physical environment) based on their major focus. Positive and negative effects of ABW environments on occupants are discussed within these three topics in consideration of the implications for the post-COVID workplace.

Although the included studies were inclined to be either more positive (i.e. interior design) or negative (i.e. indoor environmental quality, productivity, distraction and privacy) in relation to various attributes of ABW, no single effect of ABW environments on occupants was in full agreement between the studies. The shortcomings of ABW environments are more related to how this way of working is implemented and how occupants use it, rather than the concept itself. A partial uptake of ABW leads to occupants’ dissatisfaction, lower productivity and lower well-being, while a holistic approach increases the chance of success. It is hypothesised that many currently reported negative aspects of the ABW concept might diminish overtime as ABW evolves and as new challenges arise. A continuous post-occupancy evaluation after relocation to an ABW-supportive environment can inform the organization about the changing needs and preference of the occupants; hence, the organization can tailor the ABW solution to the arising needs. The inter-connection between the three key ABW pillars (organizational, human and physical environment) is crucial to the success of this concept specifically in the context of the post-COVID-19 workplace.

This paper highlights the key shortcomings and limitations of studies produced over the past decade and identifies keys gaps in the current body of literature. It provides a new insight on how findings related to open-plan offices designed to support ABW can be categorized on the three big heading of organizational, physical and human-related aspects, and further investigates the positive and negatives outcomes reported on ABW under these headings. It also discusses how the findings arising from this literature review can inform the post-COVID workplace.

This study proposes a collaborative federated learning (CFL) framework to address personal data transmission and retention issues for artificial intelligence (AI)-enabled video surveillance in public spaces.

This study examines specific challenges for long-term people monitoring in public spaces and defines AI-enabled video surveillance requirements. Based on the requirements, this study proposes a CFL framework to gradually adapt AI models’ knowledge while reducing personal data transmission and retention. The framework uses three different federated learning strategies to rapidly learn from different new data sources while minimizing personal data transmission and retention to a central machine.

The findings confirm that the proposed CFL framework can help minimize the use of personal data without compromising the AI model's performance. The gradual learning strategies help develop AI-enabled video surveillance that continuously adapts for long-term deployment in public spaces.

This study makes two specific contributions to advance the development of AI-enabled video surveillance in public spaces. First, it examines specific challenges for long-term people monitoring in public spaces and defines AI-enabled video surveillance requirements. Second, it proposes a CFL framework to minimize data transmission and retention for AI-enabled video surveillance. The study provides comprehensive experimental results to evaluate the effectiveness of the proposed framework in the context of facial expression recognition (FER) which involves large-scale datasets.

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.