Chronic lower back pain due to improper lifting techniques poses a major workplace safety hazard. The major risk factors for improper loading posture (ILP) include overloading, and improper loading of the lumbar muscles, ligaments, and vertebrae due to repetitive mechanical stresses exerted upon them. The current intervention technology relies on the users' intent and willingness to self-correct ILP through alert-based feedback or involves wearing bulky lift assist devices to prevent ILP. We address these issues with a physiological feedback system that utilizes IMU sensors for ILP detection and Electrical Muscle Stimulation (EMS) for automatic dynamic ILP correction for restoring ideal lifting angles for torso inclination and knee bend. In a user study involving 36 participants, our automatic approach delivered significantly faster correction and outperformed alternative feedback mechanisms (Audio and Vibro-tactile) and was perceived to be interesting, comfortable and a potential commercial product.
https://doi.org/10.1145/3544548.3581435
Reflecting on personal challenges can be difficult. Without encouragement, the reflection process often remains superficial, thus inhibiting deeper understanding and learning from past experiences. To allow people to immerse themselves in and deeply reflect on past challenges, we developed SelVReflect, a VR experience which offers active voice-based guidance and a space to freely express oneself. SelVReflect was developed in an iterative design process (N=5) and evaluated in a user study with N=20 participants. We found that SelVReflect enabled participants to approach their challenge and its (emotional) components from different perspectives and discover new relationships between these components. By making use of the spatial possibilities in VR, they got a better understanding of the situation and of themselves. We contribute empirical evidence of how a guided VR experience can support reflection. We discuss opportunities and design requirements for guided VR experiences that aim to foster deeper reflection.
https://doi.org/10.1145/3544548.3580763
Is the current state of fitness applications effective at motivating and satisfying the needs of Hispanic users? With most mHealth research conducted with a predominantly white population, the answer to this question is lacking. In this study, we address this question through a survey study with Hispanic users of fitness applications (N= 211) and use the Motivational Technology Model (MTM) and Self-Determination Theory (SDT) as theoretical frameworks. We found that using interactivity features is essential to inspire more autonomous forms of motivation to use fitness applications. This is because interactivity helps satisfy users’ needs for relatedness. However, interactivity also decreased autonomy and competence suggesting the need to design fitness applications that increase relatedness without compromising autonomy. Implications for the design of fitness applications for the population at large and Hispanics, in particular, are discussed.
https://doi.org/10.1145/3544548.3581200
Ambient Information Systems (AIS) have shown some success when used as a notification towards users' health-related activities. But in the actual busy lives of users, ambient notifications might be forgotten or even missed, nullifying the original notification. Could a system use multiple levels of noticeability to ensure its message is received, and how could this concept be effectively portrayed? To examine these questions, we took a Research through Design approach and created plant-mimicking Shape-Changing Interface (S-CI) artifacts, then conducted interviews with 10 participants who currently used a reminder system for health-related activities. We report findings on acceptable scenarios to disrupting people for health-related activities, and participants’ reactions to our design choices, including how using naturalistic aesthetics led to interpretations of the uncanny and morose, and which ways system physicality affected imagined uses. We offer design suggestions in health-related notification systems and S-CIs, and discuss future work in ambient-to-disruptive technology.
https://doi.org/10.1145/3544548.3581486
Excessive alcohol consumption causes disability and death. Digital interventions are promising means to promote behavioral change and thus prevent alcohol-related harm, especially in critical moments such as driving. This requires real-time information on a person’s blood alcohol concentration (BAC). Here, we develop an in-vehicle machine learning system to predict critical BAC levels. Our system leverages driver monitoring cameras mandated in numerous countries worldwide. We evaluate our system with n=30 participants in an interventional simulator study. Our system reliably detects driving under any alcohol influence (area under the receiver operating characteristic curve [AUROC] 0.88) and driving above the WHO recommended limit of 0.05 g/dL BAC (AUROC 0.79). Model inspection reveals reliance on pathophysiological effects associated with alcohol consumption. To our knowledge, we are the first to rigorously evaluate the use of driver monitoring cameras for detecting drunk driving. Our results highlight the potential of driver monitoring cameras and enable next-generation drunk driver interaction preventing alcohol-related harm.
https://doi.org/10.1145/3544548.3580975
The experience of what we eat depends not only on the taste of the food, but also on other modalities of sensory feedback. Perceptual research has shown the potential of altering visual, olfactory, and textural food cues to affect flavor, texture, and satiety. Recently, the HCI community has leveraged such research to encourage healthy eating, but the resulting tools often require specialised and/or invasive devices. Ubiquitous and unobtrusive, audio feedback-based tools could alleviate those drawbacks, but research in this area has been limited to food texture. We expand on prior psychology research by exploring a wide range of auditory feedback styles to modify not only flavor attributes but also appetite-related measures. We present Auditory Seasoning, a mobile app that offers various curated audio modes to alter chewing sounds. In a Pringles-tasting experiment (N=37), this tool significantly influenced food perception and eating behavior beyond texture alone. Based on these results, we discuss design implications to create custom real-world flavor/satiety-enhancing tools.
https://doi.org/10.1145/3544548.3580755