Mobile Mental Health Crisis Intervention in the Western Health Region of Newfoundland and Labrador

The impetus for this research is Recommendation #15 of the 2003 Luther Inquiry into the deaths of Norman Reid and Darryl Power: “IT IS FURTHER RECOMMENDED that the Regional Health Boards establish mobile health units to respond to mentally ill persons in crisis where no criminal offence is alleged. Each unit would be developed locally and based on local needs.” Our stakeholder partners in the Western Regional Health Authority asked us to identify a range of mobile crisis intervention service models, some of which may be better suited to lower-density, rural populations and some of which may be better suited to higher-density areas like Corner Brook. Our partners expressed a particular interest in models that can be implemented with minimal additional human resources, but that involve local, face-to-face contact rather than telephone, electronic, or clinic-based models of service delivery. The term “crisis intervention” generally refers to any immediate, short-term therapeutic interventions or assistance provided to an individual or group of individuals who are in acute psychological distress or crisis. The term encompasses a number of after-the-fact interventions – such as rape counseling and critical incident stress debriefing – that would not be relevant to the kinds of situations described in the Luther Report. Given the project parameters specified by our partners at Western Health, we formulated a research question and a literature search strategy that would enable us to focus specifically on forms of crisis intervention that are designed to manage potentially dangerous mental health crises on-site rather than to mediate their impacts after the fact. Our research question is as follows: “What models of mobile– i.e., face-to-face – crisis intervention have proven effective in managing potentially violent mental health crises occurring outside the hospital setting?

Does Your Smartphone “Know” Your Social Life? A Methodological Comparison of Day Reconstruction, Experience Sampling, and Mobile Sensing

Mobile sensing is a promising method that allows researchers to directly observe human social behavior in daily life using people’s mobile phones. To date, limited knowledge exists on how well mobile sensing can assess the quantity and quality of social interactions. We therefore examined the agreement among experience sampling, day reconstruction, and mobile sensing in the assessment of multiple aspects of daily social interactions (i.e., face-to-face interactions, calls, and text messages) and the possible unique access to social interactions that each method has. Over 2 days, 320 smartphone users (51% female, age range = 18–80, M = 39.53 years) answered up to 20 experience-sampling questionnaires about their social behavior and reconstructed their days in a daily diary. Meanwhile, face-to-face and smartphone-mediated social interactions were assessed with mobile sensing. The results showed some agreement between measurements of face-to-face interactions and high agreement between measurements of smartphone-mediated interactions. Still, a large number of social interactions were captured by only one of the methods, and the quality of social interactions is still difficult to capture with mobile sensing. We discuss limitations and the unique benefits of day reconstruction, experience sampling, and mobile sensing for assessing social behavior in daily life

An Active Monitoring System for Real-Time Face-Tracking based on Mobile Sensors

none3Surveillance systems frequently use fixed or semimobile cameras. However, in many cases, the use of intelligent mobile sensors is preferrable over fixed sensors, because the system configuration can be modified according to particular environmental conditions or adapted to compensate for one or more malfunctioning sensors. This paper proposes a real-time surveillance system based on a mobile sensor. Using an Android smartphone and a face-tracking algorithm, the system can move autonomously to track the human face with the longest presence in the video field. In addition, the system can be connected to a computer performing face-recognition through a wireless connection provided by the smartphone. This way the mobile sensor can track a determined human face. The paper provides some experimental results to validate system performance.IEEE Catalogue Number CFP12825-PRTS. Saraceni; A. Claudi; A.F. DragoniS., Saraceni; Claudi, Andrea; Dragoni, Aldo Franc

People tracking and re-identification by face recognition for RGB-D camera networks

This paper describes a face recognition-based people tracking and re-identification system for RGB-D camera networks. The system tracks people and learns their faces online to keep track of their identities even if they move out from the camera's field of view once. For robust people re-identification, the system exploits the combination of a deep neural network- based face representation and a Bayesian inference-based face classification method. The system also provides a predefined people identification capability: it associates the online learned faces with predefined people face images and names to know the people's whereabouts, thus, allowing a rich human-system interaction. Through experiments, we validate the re-identification and the predefined people identification capabilities of the system and show an example of the integration of the system with a mobile robot. The overall system is built as a Robot Operating System (ROS) module. As a result, it simplifies the integration with the many existing robotic systems and algorithms which use such middleware. The code of this work has been released as open-source in order to provide a baseline for the future publications in this field