Assistive technologies have emerged as powerful tools for assessing physical health and wellness through monitoring physiological parameters such as movement and heart rate. However, our overall health is influenced not only by physiological parameters but also by mental health factors and environmental influences. Therefore, in the pursuit of holistic wellness, assistive technologies need to support multimodal sensing to monitor various aspects of individuals' health, including physiological health, mental wellness, and environmental parameters that influence personal health and wellness. The challenges arise when these technologies must be implemented in real-time and in miniaturized point-of-care platforms where multi-modal sensing algorithms must run efficiently, and resources, including power, are limited. Solving these challenges requires converging engineering practices with psychological and physiological principles. This work aims to implement resource-efficient algorithms to assess social interaction parameters as an important mental health factor and to enable high-performance point-of-care devices to monitor physiological and environmental parameters in a miniaturized and effective manner. In this work, an extensive dataset for human interaction in virtual settings was prepared. Efficient algorithms were developed to identify levels of two highly important social interaction parameters, ‘affect’ and ‘rapport.’ We analyzed affect in time intervals based on the conversation turns and analyzed rapport in 30-second time intervals, which is the highest temporal resolution reported in the literature. We achieved an affect prediction accuracy of 76.8% and a rapport prediction accuracy of 73.6%, which are the highest reported results for analyzing multi-person groups. Furthermore, to support monitoring physiological and environmental parameters, electrochemical solutions were identified as a highly effective method. We introduced new architecture to overcome limited supply potentials in modern point-of-care devices. In our novel design, the potential window for electrochemical reactions doubles compared to the traditional designs. This, in return, facilitates a significantly wider range of target elements that can be monitored with this novel architecture. Overall, the enhanced algorithms and architecture introduced in this work enable multimodal sensing of important personal health and wellness parameters.
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