Rethinking Assessment: The Body as a Compass for Understanding

This research investigates the potential of utilizing sitting posture as a novel indicator of engagement and focus in different activities. Recognizing that even subtle shifts in how we sit can reflect our cognitive and emotional states, this study employs a sensor fusion IoT platform embedded within a chair to capture detailed postural data. By employing both quantitative and qualitative analysis, this research aims to determine if specific sitting postures can reliably correlate to a user's level of focus and attention, and present an alternative approach to assess and understand engagement by providing a non-intrusive, real-time window into an individual's cognitive state.We conducted an experiment to observe participants over a moderate period, between one and two hours, while engaging in either focused studying or passive streaming content consumption. The sensor array continuously monitors and records nuanced changes in sitting posture, including leaning, slouching, shifting, and micro-movements. This high-resolution data is analyzed to identify patterns and variations that correlate with different levels of engagement. To analyze the vast amount of postural data collected, we employed machine learning techniques. This allowed us to classify different sitting postures and identify patterns associated with varying levels of engagement during both studying and streaming activities.Furthermore, the sensor fusion IoT platform employs the collected data to generate a comprehensive report for each participant's sitting period. This report provided a detailed visualization of posture changes over time, highlighting key moments of engagement and disengagement, and offering insights into individual patterns of behavior. These personalized reports have the potential to be valuable tools for self-reflection and behavioral modification, allowing individuals to gain a deeper understanding of their own focus and attention patterns.