Combating COVID-19 using indoor data analytics
21 April 2022
After two years of combating COVID-19, many countries are opening their borders, gradually restoring human mobility. This unprecedented pandemic situation has forcefully changed the lifestyles of humans; the most noticeable change is social distancing. Based on World Health Organisation (WHO) on public health, the transmission of the SARS-CoV-2 virus can be prevented effectively by social distancing. Thus, the usage of indoor location analytics.
"Based on the indoor location data, the clustering mechanism can analyse spatial data, Spatio-temporal data, and movement behaviour features for proximity detection or contact tracing application to enforce social distancing. The feature extraction method is adopted to extract valuable features that can assist the proposed system in constructing the network of users based on the similarity of movement behaviours of the users. The network of users models the optimisation problem to manage human mobility in an enclosed site. The formulation of the objective function is to minimise the probability of contact between the users. The optimisation problem is solved using the proposed effective scheduling solution," stated Dr Tan Chee Keong from the School of Information Technology, Monash University Malaysia.
Even though many countries start with the new norms by allowing their people to live with the virus, they still impose strict public health guidelines such as wearing masks and social distancing. Many governments currently rely on the manual system to control human mobility, which is laborious and inefficient. Some uncooperative citizens may be reluctant to report or share their locations for mobility management. For companies or employers to manage their staff, purely controlling the number of staff accessing a site is inefficient and accurate.
The social distancing among the employees highly depends on their movement behaviours. "Therefore, the indoor location analytics platform can identify the hotspots, construct the network of users, and optimise human mobility to enable efficient social distancing. This new system can significantly impact the local and international community to adjust themselves to the new normal in a more effective way," Dr Tan said.
He added that the indoor data analytics used to enforce social distancing is a novel and original technology. "It can analyse individual positions, cluster the individuals based on their positions, and predict the "hotspots" and human movement trajectories. The clustering outcome and predicted human movement behaviours can optimise human mobility in an enclosed area to enforce efficient social distancing. The research project is 90% completed, and it is pending the development of a prototype. Potential investors interested in this technology are employers, mega shopping malls, higher education institutions, private and public organisations," Dr Tan shared.
He also added that Malaysia's contract tracing app (MySejahtera) could embed the system. The Monash Malaysia School of Information Technology Collaborative funded this simulation work and experiment for this research project.