Mental illness, whether it is medically diagnosed or undiagnosed, affects a large proportion of the population. It is one of the causes of extensive disability, and f not properly treated, it can lead to severe emotional, behavioral, and physical health problems. In most mental health research studies, the focus is on treatment, but fewer resources are focused on technical solutions to mental health issues. The present paper carried out a systematic review of available literature using PRISMA guidelines to address various monitoring solutions in mental health through the use of wearable sensors. Wearable sensors can offer several advantages over traditional methods of mental health assessment, including convenience, cost-effectiveness, and the ability to capture data in real-world settings. Their ability to collect data related to anxiety and stress levels, as well as panic attacks, is discussed. The available sensors on the market are described, as well as their success in providing data that can be correlated with the aforementioned health issues. The current wearable landscape is quite dynamic, and the current offerings have enough quality to deliver meaningful data targeted for machine learning algorithms. The results indicate that mental health monitoring is feasible.

Anxiety disorders refer to mental health conditions characterized by excessive and persistent worry, fear or dread, which can interfere with daily life. These disorders are pervasive worldwide but can be treated with psychotherapy, medication, or both. Detecting them in time is key to avoid further severe development after an initial crisis. In this paper, we present a solution based on self-monitoring, using wearables, smartphones and Machine Learning (ML) to assess users' anxiety and panic levels. This is the first paper to embrace and present a visualisation system for mental health focused on patients. User studies support the solution's quality. With this system, patients are empowered to control their situation better, helping the medical staff to get more insight into when and where a crisis occurs.

Asset management involves the coordinated activities of an organisation to derive value from assets, which may include physical assets. It encompasses activities related to design, construction, installation, operation, maintenance, renewal, and asset disposal. Asset management ensures the coordination of all activities, resources, and data related to physical assets. Recording and monitoring all maintenance activities is a key part of asset management, often done using work orders (WOs). Technicians typically create WOs using “free text”, which can result in missing or ungrammatical words, making it difficult to identify trends and analyse information. To standardise the terminology used for the same asset maintenance operation, this paper proposes a method that suggests words to technicians as they complete WOs. The word suggestion algorithm is based on past maintenance records, and a Bayesian network-based recommender system adapts to present needs verified by technicians using implicit user feedback. Implementing this system aims to normalise the terms used by technicians when filling in a WO. The corpus for this work comes from asset management records collected in a health facility in Portugal operated by a private company.

Stress has a great effect on people's lives that can not be understated. While it can be good, since it helps humans to adapt to new and different situations, it can also be harmful when not dealt with properly, leading to chronic stress. The objective of this paper is developing a stress monitoring solution, that can be used in real life, while being able to tackle this challenge in a positive way. The SMILE data set was provided to team Anxolotl, and all it was needed was to develop a robust model. We developed a supervised learning model for classification in Python, presenting the final result of 64.1% in accuracy and a f1-score of 54.96%. The resulting solution stood the robustness test, presenting low variation between runs, which was a major point for it's possible integration in the Anxolotl app in the future.