Emotions in context: examining pervasive affective sensing systems, applications, and analyses

Pervasive sensing has opened up new opportunities for measuring our feelings and understanding our behavior by monitoring our affective states while mobile. This review paper surveys pervasive affect sensing by examining and considering three major elements of affective pervasive systems, namely; “sensing”, “analysis”, and “application”. Sensing investigates the different sensing modalities that are used in existing real-time affective applications, Analysis explores different approaches to emotion recognition and visualization based on different types of collected data, and Application investigates different leading areas of affective applications. For each of the three aspects, the paper includes an extensive survey of the literature and finally outlines some of challenges and future research opportunities of affective sensing in the context of pervasive computing

Increasing the Capacity of Primary Care Through Enabling Technology.

Primary care is the foundation of effective and high-quality health care. The role of primary care clinicians has expanded to encompass coordination of care across multiple providers and management of more patients with complex conditions. Enabling technology has the potential to expand the capacity for primary care clinicians to provide integrated, accessible care that channels expertise to the patient and brings specialty consultations into the primary care clinic. Furthermore, technology offers opportunities to engage patients in advancing their health through improved communication and enhanced self-management of chronic conditions. This paper describes enabling technologies in four domains (the body, the home, the community, and the primary care clinic) that can support the critical role primary care clinicians play in the health care system. It also identifies challenges to incorporating these technologies into primary care clinics, care processes, and workflow

An Observational Study With the Janssen Autism Knowledge Engine (JAKE((R))) in Individuals With Autism Spectrum Disorder

Objective: The Janssen Autism Knowledge Engine (JAKE(R)) is a clinical research outcomes assessment system developed to more sensitively measure treatment outcomes and identify subpopulations in autism spectrum disorder (ASD). Here we describe JAKE and present results from its digital phenotyping (My JAKE) and biosensor (JAKE Sense) components. Methods: An observational, non-interventional, prospective study of JAKE in children and adults with ASD was conducted at nine sites in the United States. Feedback on JAKE usability was obtained from caregivers. JAKE Sense included electroencephalography, eye tracking, electrocardiography, electrodermal activity, facial affect analysis, and actigraphy. Caregivers of individuals with ASD reported behaviors using My JAKE. Results from My JAKE and JAKE Sense were compared to traditional ASD symptom measures. Results: Individuals with ASD (N = 144) and a cohort of typically developing (TD) individuals (N = 41) participated in JAKE Sense. Most caregivers reported that overall use and utility of My JAKE was easy (69%, 74/108) or very easy (74%, 80/108). My JAKE could detect differences in ASD symptoms as measured by traditional methods. The majority of biosensors included in JAKE Sense captured sizable amounts of quality data (i.e., 93-100% of eye tracker, facial affect analysis, and electrocardiogram data was of good quality), demonstrated differences between TD and ASD individuals, and correlated with ASD symptom scales. No significant safety events were reported. Conclusions: My JAKE was viewed as easy or very easy to use by caregivers participating in research outside of a clinical study. My JAKE sensitively measured a broad range of ASD symptoms. JAKE Sense biosensors were well-tolerated. JAKE functioned well when used at clinical sites previously inexperienced with some of the technologies. Lessons from the study will optimize JAKE for use in clinical trials to assess ASD interventions. Additionally, because biosensors were able to detect features differentiating TD and ASD individuals, and also were correlated with standardized symptom scales, these measures could be explored as potential biomarkers for ASD and as endpoints in future clinical studies. Clinical Trial Registration: https://clinicaltrials.gov/ct2/show/NCT02668991 identifier: NCT02668991

A review of the internet of floods : near real-time detection of a flood event and its impact

Worldwide, flood events frequently have a dramatic impact on urban societies. Time is key during a flood event in order to evacuate vulnerable people at risk, minimize the socio-economic, ecologic and cultural impact of the event and restore a society from this hazard as quickly as possible. Therefore, detecting a flood in near real-time and assessing the risks relating to these flood events on the fly is of great importance. Therefore, there is a need to search for the optimal way to collect data in order to detect floods in real time. Internet of Things (IoT) is the ideal method to bring together data of sensing equipment or identifying tools with networking and processing capabilities, allow them to communicate with one another and with other devices and services over the Internet to accomplish the detection of floods in near real-time. The main objective of this paper is to report on the current state of research on the IoT in the domain of flood detection. Current trends in IoT are identified, and academic literature is examined. The integration of IoT would greatly enhance disaster management and, therefore, will be of greater importance into the future