From head to toe:body movement for human-computer interaction

Our bodies are the medium through which we experience the world around us, so human-computer interaction can highly benefit from the richness of body movements and postures as an input modality. In recent years, the widespread availability of inertial measurement units and depth sensors led to the development of a plethora of applications for the body in human-computer interaction. However, the main focus of these works has been on using the upper body for explicit input. This thesis investigates the research space of full-body human-computer interaction through three propositions. The first proposition is that there is more to be inferred by natural users’ movements and postures, such as the quality of activities and psychological states. We develop this proposition in two domains. First, we explore how to support users in performing weight lifting activities. We propose a system that classifies different ways of performing the same activity; an object-oriented model-based framework for formally specifying activities; and a system that automatically extracts an activity model by demonstration. Second, we explore how to automatically capture nonverbal cues for affective computing. We developed a system that annotates motion and gaze data according to the Body Action and Posture coding system. We show that quality analysis can add another layer of information to activity recognition, and that systems that support the communication of quality information should strive to support how we implicitly communicate movement through nonverbal communication. Further, we argue that working at a higher level of abstraction, affect recognition systems can more directly translate findings from other areas into their algorithms, but also contribute new knowledge to these fields. The second proposition is that the lower limbs can provide an effective means of interacting with computers beyond assistive technology To address the problem of the dispersed literature on the topic, we conducted a comprehensive survey on the lower body in HCI, under the lenses of users, systems and interactions. To address the lack of a fundamental understanding of foot-based interactions, we conducted a series of studies that quantitatively characterises several aspects of foot-based interaction, including Fitts’s Law performance models, the effects of movement direction, foot dominance and visual feedback, and the overhead incurred by using the feet together with the hand. To enable all these studies, we developed a foot tracker based on a Kinect mounted under the desk. We show that the lower body can be used as a valuable complementary modality for computing input. Our third proposition is that by treating body movements as multiple modalities, rather than a single one, we can enable novel user experiences. We develop this proposition in the domain of 3D user interfaces, as it requires input with multiple degrees of freedom and offers a rich set of complex tasks. We propose an approach for tracking the whole body up close, by splitting the sensing of different body parts across multiple sensors. Our setup allows tracking gaze, head, mid-air gestures, multi-touch gestures, and foot movements. We investigate specific applications for multimodal combinations in the domain of 3DUI, specifically how gaze and mid-air gestures can be combined to improve selection and manipulation tasks; how the feet can support the canonical 3DUI tasks; and how a multimodal sensing platform can inspire new 3D game mechanics. We show that the combination of multiple modalities can lead to enhanced task performance, that offloading certain tasks to alternative modalities not only frees the hands, but also allows simultaneous control of multiple degrees of freedom, and that by sensing different modalities separately, we achieve a more detailed and precise full body tracking