Tamil Selvan

Proprioception is the body’s ability to sense the position and movement of each limb, as well as the amount of effort exerted onto or by them. Methods to assess proprioception have been introduced before, yet there is little to no study on assessing the degree of proprioception on body parts for use cases like gesture recognition wearable computing. We propose the use of Fitts’ law coupled with the N-Back task to evaluate proprioception of the hand. We evaluate 15 distinct points at the back of the hand and assess the musing extended 3D Fitts’ law. Our results show that the index of difficulty of tapping point from thumb to pinky increases gradually with a linear regression factor of 0.1144. Additionally, participants perform the tap before performing the N-Back task. From these results, we discuss the fundamental limitations and suggest how Fitts’ law can be further extended to assess proprioception

Radar is primarily used for applications like tracking and large-scale ranging, and its use for object identification has been rarely explored. This paper introduces RaITIn, a radar-based identification (ID) method for tangible interactions. Unlike conventional radar solutions, RaITIn can track and identify objects on a tabletop scale. We use frequency modulated continuous wave (FMCW) radar sensors to classify different objects embedded with low-cost radar reflectors of varying sizes on a tabletop setup. We also introduce Stackable IDs, where different objects can be stacked and combined to produce unique IDs. The result allows RaITIn to accurately identify visually identical objects embedded with different low-cost reflector configurations. When combined with a radar’s ability for tracking, it creates novel tabletop interaction modalities. We discuss possible applications and areas for future work.

We introduce RadarHand, a wrist-worn wearable with millimetre wave radar that detects on-skin touch-based proprioceptive hand gestures. Radars are robust, private, small, penetrate materials, and require low computation costs. We first evaluated the proprioceptive and tactile perception nature of the back of the hand and found that tapping on the thumb is the least proprioceptive error of all the finger joints, followed by the index finger, middle finger, ring finger, and pinky finger in the eyes-free and high cognitive load situation. Next, we trained deep-learning models for gesture classification. We introduce two types of gestures based on the locations of the back of the hand: generic gestures and discrete gestures. Discrete gestures are gestures that start at specific locations and end at specific locations at the back of the hand, in contrast to generic gestures, which can start anywhere and end anywhere on the back of the hand. Out of 27 gesture group possibilities, we achieved 92% accuracy for a set of seven gestures and 93% accuracy for the set of eight discrete gestures. Finally, we evaluated RadarHand’s performance in real-time under two interaction modes: Active interaction and Reactive interaction. Active interaction is where the user initiates input to achieve the desired output, and reactive interaction is where the device initiates interaction and requires the user to react. We obtained an accuracy of 87% and 74% for active generic and discrete gestures, respectively, as well as 91% and 81.7% for reactive generic and discrete gestures, respectively. We discuss the implications of RadarHand for gesture recognition and directions for future works.

The concept of cultural empathy, which entails the ability to empathize with individuals from diverse cultural backgrounds, has garnered attention across multiple disciplines. However, there has been limited research focusing on the utilization of immersive experiences to deliver journalistic encounters that foster cultural empathy. In this regard, we propose the development of Virtual Journalist, a virtual reality (VR) system that immerses users in the role of a journalist, allowing them to consume 360° video documentaries and capture moments that evoke cultural empathy through photography. Furthermore, this system integrates both cognitive and affective dimensions of empathy by linking physiological data, namely electrodermal activity (EDA) and heart rate variability (HRV), to the captured photographs. Our findings suggest that the impact of Virtual Journalist varies based on the user’s cultural background, and we have observed that empathic accuracy can be achieved by combining user-generated photographs with corresponding physiological data.