Introduction: In this work we explore a potential approach to improve human-robot collaboration experience by adapting cobot behavior based on natural cues from the operator. Methods: Inspired by the literature on human-human interactions, we conducted a wizard-of-oz study to examine whether a gaze towards the cobot can serve as a trigger for initiating joint activities in collaborative sessions. In this study, 37 participants engaged in an assembly task while their gaze behavior was analyzed. We employed a gaze-based attention recognition model to identify when the participants look at the cobot. Results: Our results indicate that in most cases (83.74%), the joint activity is preceded by a gaze towards the cobot. Furthermore, during the entire assembly cycle, the participants tend to look at the cobot mostly around the time of the joint activity. Given the above results, a fully integrated system triggering joint action only when the gaze is directed towards the cobot was piloted with 10 volunteers, of which one characterized by high-functioning Autism Spectrum Disorder. Even though they had never interacted with the robot and did not know about the gaze-based triggering system, most of them successfully collaborated with the cobot and reported a smooth and natural interaction experience. Discussion: To the best of our knowledge, this is the first study to analyze the natural gaze behavior of participants working on a joint activity with a robot during a collaborative assembly task and to attempt the full integration of an automated gaze-based triggering system.

Parenting practices have a profound effect on children\'s well-being and are a core target of several psychological interventions for child mental health. However, there is only limited understanding in HCI so far about how to design socio-technical systems that could support positive shifts in parent-child social practices in situ. This paper focuses on parental socialisation of emotion as an exemplar context in which to explore this question. We present a two-step study, combining theory-driven identification of plausible design directions with co-design workshops with 22 parents of children aged 6-10 years. Our data suggest the potential for technology-enabled systems that aim to facilitate positive changes in parent-child social practices in situ, and highlight a number of plausible design directions to explore in future work.

Goal setting is critical to achieving desired changes in life. Many technologies support defining and tracking progress toward goals, but these are just some parts of the process of setting and achieving goals. People want to set goals that are more complex than the ones supported through technology. Additionally, people use goal-setting technologies longitudinally, yet the understanding of how people\'s goals evolve is still limited. We study the collaborative practices of mental health therapists and clients for longitudinally setting and working toward goals through semi-structured interviews with 11 clients and 7 therapists who practiced goal setting in their therapy sessions. Based on the results, we create the Longitudinal Goal Setting Model in mental health, a three-stage model. The model describes how clients and therapists select among multiple complex problems, simplify complex problems to specific goals, and adjust goals to help people address complex issues. Our findings show collaboration between clients and therapists can support transformative reflection practices that are difficult to achieve without the therapist, such as seeing problems through new perspectives, questioning and changing practices, or addressing avoided issues.

There has been a resurgence of applications focused on human activity recognition (HAR) in smart homes, especially in the field of ambient intelligence and assisted-living technologies. However, such applications present numerous significant challenges to any automated analysis system operating in the real world, such as variability, sparsity, and noise in sensor measurements. Although state-of-the-art HAR systems have made considerable strides in addressing some of these challenges, they suffer from a practical limitation: they require successful pre-segmentation of continuous sensor data streams prior to automated recognition, i.e., they assume that an oracle is present during deployment, and that it is capable of identifying time windows of interest across discrete sensor events. To overcome this limitation, we propose a novel graph-guided neural network approach that performs activity recognition by learning explicit co-firing relationships between sensors. We accomplish this by learning a more expressive graph structure representing the sensor network in a smart home in a data-driven manner. Our approach maps discrete input sensor measurements to a feature space through the application of attention mechanisms and hierarchical pooling of node embeddings. We demonstrate the effectiveness of our proposed approach by conducting several experiments on CASAS datasets, showing that the resulting graph-guided neural network outperforms the state-of-the-art method for HAR in smart homes across multiple datasets and by large margins. These results are promising because they push HAR for smart homes closer to real-world applications.

We propose an interactive visual analytics tool, Vis-SPLIT, for partitioning a population of individuals into groups with similar gene signatures. Vis-SPLIT allows users to interactively explore a dataset and exploit visual separations to build a classification model for specific cancers. The visualization components reveal gene expression and correlation to assist specific partitioning decisions, while also providing overviews for the decision model and clustered genetic signatures. We demonstrate the effectiveness of our framework through a case study and evaluate its usability with domain experts. Our results show that Vis-SPLIT can classify patients based on their genetic signatures to effectively gain insights into RNA sequencing data, as compared to an existing classification system.

Chronic pain is a leading cause of morbidity among children and adolescents affecting 35% of the global population. Pediatric chronic pain management requires integrative health methods spanning physical and psychological subsystems through various mind-body interventions. Yoga therapy is one such method, known for its ability to improve the quality of life both physically and psychologically in chronic pain conditions. However, maintaining the clinical outcomes of personalized yoga therapy sessions at-home is challenging due to fear of movement, lack of motivation, and boredom. Virtual Reality (VR) has the potential to bridge the gap between the clinic and home by motivating engagement and mitigating pain-related anxiety or fear of movement. We developed a multi-modal algorithmic architecture for fusing real-time 3D human body pose estimation models with custom developed inverse kinematics models of physical movement to render biomechanically informed 6-DoF whole-body avatars capable of embodying an individual\'s real-time yoga poses within the VR environment. Experiments conducted among control participants demonstrated superior movement tracking accuracy over existing commercial off-the-shelf avatar tracking solutions, leading to successful embodiment and engagement. These findings demonstrate the feasibility of rendering virtual avatar movements that embody complex physical poses such as those encountered in yoga therapy. The impact of this work moves the field one step closer to an interactive system to facilitate at-home individual or group yoga therapy for children with chronic pain conditions.

Over the past century, multichannel fluorescence imaging has been pivotal in myriad scientific breakthroughs by enabling the spatial visualization of proteins within a biological sample. With the shift to digital methods and visualization software, experts can now flexibly pseudocolor and combine image channels, each corresponding to a different protein, to explore their spatial relationships. We thus propose psudo, an interactive system that allows users to create optimal color palettes for multichannel spatial data. In psudo, a novel optimization method generates palettes that maximize the perceptual differences between channels while mitigating confusing color blending in overlapping channels. We integrate this method into a system that allows users to explore multi-channel image data and compare and evaluate color palettes for their data. An interactive lensing approach provides on-demand feedback on channel overlap and a color confusion metric while giving context to the underlying channel values. Color palettes can be applied globally or, using the lens, to local regions of interest. We evaluate our palette optimization approach using three graphical perception tasks in a crowdsourced user study with 150 participants, showing that users are more accurate at discerning and comparing the underlying data using our approach. Additionally, we showcase psudo in a case study exploring the complex immune responses in cancer tissue data with a biologist.

Accurate depth estimation poses a significant challenge in egocentric Augmented Reality (AR), particularly for precision-dependent tasks in the medical field, such as needle or tool insertions during percutaneous procedures. Augmented Mirrors (AMs) provide a unique solution to this problem by offering additional non-egocentric viewpoints that enhance spatial understanding of an AR scene. Despite the perceptual advantages of using AMs, their practical utility has yet to be thoroughly tested. In this work, we present results from a pilot study involving five participants tasked with simulating epidural injection procedures in an AR environment, both with and without the aid of an AM. Our findings indicate that using AM contributes to reducing mental effort while improving alignment accuracy. These results highlight the potential of AM as a powerful tool for AR-enabled medical procedures, setting the stage for future exploration involving medical professionals.

We describe a smartphone/smartwatch system to evaluate anomia in individuals with aphasia by using audio-recording-based ecological momentary assessments. The system delivers object-naming assessments to a participant\'s smartwatch, whereby a prompt signals the availability of images of these objects on the watch screen. Participants attempt to speak the names of the images that appear on the watch display out loud and into the watch as they go about their lives. We conducted a three-week feasibility study with six participants with mild to moderate aphasia. Participants were assigned to either a nine-item (four prompts per day with nine images) or single-item (36 prompts per day with one image each) ecological momentary assessment protocol. Compliance in recording an audio response to a prompt was approximately 80% for both protocols. Qualitative analysis of the participants\' interviews suggests that the participants felt capable of completing the protocol, but opinions about using a smartwatch were mixed. We review participant feedback and highlight the importance of considering a population\'s specific cognitive or motor impairments when designing technology and training protocols.

Smoking is the leading cause of preventable death worldwide. Cigarette smoke includes thousands of chemicals that are harmful and cause tobacco-related diseases. To date, the causality between human exposure to specific compounds and the harmful effects is unknown. A first step in closing the gap in knowledge has been measuring smoking topography, or how the smoker smokes the cigarette (puffs, puff volume, and duration). However, current gold-standard approaches to smoking topography involve expensive, bulky, and obtrusive sensor devices, creating unnatural smoking behavior and preventing their potential for real-time interventions in the wild. Although motion-based wearable sensors and their corresponding machine-learned models have shown promise in unobtrusively tracking smoking gestures, they are notorious for confounding smoking with other similar hand-to-mouth gestures such as eating and drinking. In this paper, we present SmokeMon, a chest-worn thermal-sensing wearable system that can capture spatial, temporal, and thermal information around the wearer and cigarette all day to unobtrusively and passively detect smoking events. We also developed a deep learning-based framework to extract puffs and smoking topography. We evaluate SmokeMon in both controlled and free-living experiments with a total of 19 participants, more than 110 hours of data, and 115 smoking sessions achieving an F1-score of 0.9 for puff detection in the laboratory and 0.8 in the wild. By providing SmokeMon as an open platform, we provide measurement of smoking topography in free-living settings to enable testing of smoking topography in the real world, with potential to facilitate timely smoking cessation interventions.