Everyday Living Artificial Intelligence Hub (2021)

Abstract: We present the Everyday Living Artificial Intelligence (AI) Hub, a novel proof-of-concept framework for enhancing human health and wellbeing via a combination of tailored wear-able and Conversational Agent (CA) solutions for non-invasive monitoring of physiological signals, assessment of behaviors through unobtrusive wearable devices, and the provision of personalized interventions to reduce stress and anxiety. We utilize recent advancements and industry standards in the Internet of Things (IoT)and AI technologies to develop this proof-of-concept framework.

## introduction the significance of stress in disease development and progression has been established for multiple therapeutic areas including cardiovascular disease @xcite , type 2 diabetes @xcite , obesity @xcite , sleep disorders @xcite , depression @xcite , stroke @xcite drug addiction (including opioid, tobacco, cannabis, and cocaine use) @xcite , and alzheimer's disease @xcite . as demonstrated for cardiovascular disease, stressors associated with increased risk of events include those commonly encountered in life such as work stressors, anger episodes and even the viewing of stressful sporting events @xcite . exposure to stressful events is therefore a major risk factor for morbidity and mortality rates especially for conditions that have a great impact on public health. as the commercial iot sector continues to grow, our homes and bodies are increasingly instrumented. we now have digital personal assistants that listen and respond to voice commands and wearable devices equipped with multiple sensors. the availability and maturity of this technology affords an unprecedented opportunity to develop holistic systems to advance the health and wellbeing of many groups including one of the most vulnerable sectors of our population: elders aging in-place. to provide personalized and effective interventions, such a system must be capable of sensing, integrating, responding to physical, emotional, and cognitive status in an accessible way. towards this vision, we have developed a proofof-concept framework for individually tailored detection and management of mental stress and anxiety in everyday life. we target the development and deployment of a novel personalized technology that integrates conversational voice assistants with wearable sensors and smart-textile clothing technology to provide real-time, in-home, unobtrusive sensing and on-body stimulation solutions (e.g., pressure, heat, etc.). the proposed proofof-concept framework integrates three major components: 1) natural language interaction with the user via a conversational voice assistant; 2) physiological signal sensing of activity, heart rate, body temperature, and electrical conductivity of skin; and 3) garment-based delivery of heat and compression interventions to reduce stress and anxiety detected via voice and wearable sensors. ## methodology the everyday living ai hub is a holistic framework that orchestrates the 3 components discussed in section 1, coordinating multiple streams of biometric data and physical interventions (figure 1 ). the hub framework allows for the analysis of selfsupplied information (such as information about a user's schedule, habits, and preferences) alongside biometric data collected from otc wearable devices with the goal of providing interventions into the user's life to help them self-regulate. these interventions are in the form of notifications (reminders to breathe, reminders that a meeting or other scheduled time is coming up), or bindings with devices that operate "in real life (irl)" such as the smarthugs garment (pettys-baker et al., 2018), a wearable shirt designed to perform compression on dysregulated individuals. detected stress events are confirmed with the user via the hub conversational agent (hub ca), which can ask questions about the severity and timing of the stressful event, and confirm whether an intervention is necessary or desirable. ## challenges the everyday living ai hub requires many practical and theoretical advances. while preliminary studies have shown that commercial iot wearables can detect changes in heart-rate that correspond to stress in a naturalistic environment @xcite , existing machine-learning models are not equipped to reliably predict stressful events in real-time. collection of data and the creation of an adequate model are ongoing work. the hub ca is based on established chatbot paradigms, but models and dialog flows are still under development for the new domain of stress management.