University of Alberta computing scientists are developing an app to aid health-care staff to assess and manage pain in patients suffering from dementia and other neurodegenerative diseases. "The challenge with understanding pain in patients with dementia is that the expressions of pain in these individuals are often mistaken for psychiatric problems," said Eleni Stroulia, professor in the Department of Computing Science and co-lead on the project. "So we asked, how can we use technology to better understand the pain of people with dementia?" Along with Stroulia, the project is led by Thomas Hadjistavropoulos at the University of Regina as part of AGE-WELL, one of Canada's Networks of Centres of Excellence. The app will serve to digitize a pen-and-paper observational checklist that past research has shown helps health-care workers such as nurses when assessing pain in their patients suffering from dementia.

Scientists developed a new system with the help of the latest technology to keep an eye on your depression. Isn't this amazing that whenever you talk, Artificial Intelligence can analyze your voice within seconds either you are depressed or not? Some diseases are easy to detect with its symptoms, for example, chest pain points out heart problems but it is very difficult to detect depression. Depression is a common and very serious issue of today's generation that leads to health complications. According to the World Health Organization (WHO), India ranked 6th most depressed country.

To help identify depression early, scientists have now enhanced a technology that uses Artificial Intelligence (AI) to sift through sound of your voice to gauge whether you are depressed or not. Computing science researchers from University of Alberta in Canada have improved technology for identifying depression through vocal cues. The study, conducted by Mashrura Tasnim and Professor Eleni Stroulia, builds on past research that suggests that the timbre of our voice contains information about our mood. Using standard benchmark data sets, Tasnim and Stroulia developed a methodology that combines several Machine Learning (ML) algorithms to recognize depression more accurately using acoustic cues. A realistic scenario is to have people use an app that will collect voice samples as they speak naturally.

To help identify depression early, scientists have now enhanced a technology that uses Artificial Intelligence (AI) to sift through the sound of your voice to gauge whether you are depressed or not. Computing science researchers from the University of Alberta in Canada have improved technology for identifying depression through vocal cues. The study, conducted by Mashrura Tasnim and Professor Eleni Stroulia, builds on past research that suggests that the timbre of our voice contains information about our mood. Using standard benchmark data sets, Tasnim and Stroulia developed a methodology that combines several Machine Learning (ML) algorithms to recognize depression more accurately using acoustic cues. A realistic scenario is to have people use an app that will collect voice samples as they speak naturally. "The app, running on the user's phone, will recognize and track indicators of mood, such as depression, over time.

It was held on Sunday, 28 July 2002. The workshop papers are available as a technical report from AAAI Press. After a welcome and introductions, David Martin of SRI International started the day's presentations with an overview of the The objective of the language is to enable automated software agents to easily accomplish real-world planning tasks by discovering related services, selecting the most appropriate among them, composing them into effective plans, and invoking them to execute these plans and accomplish their tasks. The ServiceModel specification is aimed at supporting service invocation, composition, and monitoring and consists of a workflow model describing how the service is accomplished in terms of atomic and composite processes and their data and control dependencies. Finally, the ServiceGrounding specification specifies the implementation-specific details of service invocation, related to protocols, message formatting, and type serialization.