Alwakeel Ahmed, Alwakeel Mohammed, Zahra Syed Rameem, Saleem Tausifa Jan, Hijji Mohammad, Alwakeel Sami S, Alwakeel Abdullah M, Alzorgi Sultan
Faculty of Computers & Information Technology, University of Tabuk, Tabuk 71491, Saudi Arabia.
Department of Computer Science and Engineering, Netaji Subhas University of Technology, Delhi 110078, India.
Diagnostics (Basel). 2023 Mar 13;13(6):1082. doi: 10.3390/diagnostics13061082.
Cities have undergone numerous permanent transformations at times of severe disruption. The Lisbon earthquake of 1755, for example, sparked the development of seismic construction rules. In 1848, when cholera spread through London, the first health law in the United Kingdom was passed. The Chicago fire of 1871 led to stricter building rules, which led to taller skyscrapers that were less likely to catch fire. Along similar lines, the COVID-19 epidemic may have a lasting effect, having pushed the global shift towards greener, more digital, and more inclusive cities. The pandemic highlighted the significance of smart/remote healthcare. Specifically, the elderly delayed seeking medical help for fear of contracting the infection. As a result, remote medical services were seen as a key way to keep healthcare services running smoothly. When it comes to both human and environmental health, cities play a critical role. By concentrating people and resources in a single location, the urban environment generates both health risks and opportunities to improve health. In this manuscript, we have identified the most common mental disorders and their prevalence rates in cities. We have also identified the factors that contribute to the development of mental health issues in urban spaces. Through careful analysis, we have found that multimodal feature fusion is the best method for measuring and analysing multiple signal types in real time. However, when utilizing multimodal signals, the most important issue is how we might combine them; this is an area of burgeoning research interest. To this end, we have highlighted ways to combine multimodal features for detecting and predicting mental issues such as anxiety, mood state recognition, suicidal tendencies, and substance abuse.
在严重破坏时期，城市经历了无数永久性的转变。例如，1755年的里斯本地震引发了抗震建筑规则的制定。1848年，霍乱在伦敦蔓延时，英国通过了第一部卫生法。1871年的芝加哥大火导致了更严格的建筑规则，催生了更不易起火的更高摩天大楼。同样，新冠疫情可能会产生持久影响，推动全球向更绿色、更数字化、更具包容性的城市转变。这场大流行凸显了智能/远程医疗的重要性。具体而言，老年人因担心感染而推迟寻求医疗帮助。因此，远程医疗服务被视为确保医疗服务平稳运行的关键方式。在人类健康和环境健康方面，城市都发挥着关键作用。通过将人口和资源集中在一个地方，城市环境既产生了健康风险，也创造了改善健康的机会。在本手稿中，我们确定了城市中最常见的精神障碍及其患病率。我们还确定了导致城市地区心理健康问题产生的因素。通过仔细分析，我们发现多模态特征融合是实时测量和分析多种信号类型的最佳方法。然而，在利用多模态信号时，最重要的问题是我们如何将它们结合起来；这是一个新兴的研究兴趣领域。为此，我们强调了结合多模态特征来检测和预测焦虑、情绪状态识别、自杀倾向和药物滥用等心理问题的方法。
