Manufacturing environments require changes in work procedures and settings based on changes in product demand affecting the types of products for production. Resource re-organization and time needed for worker adaptation to such frequent changes can be expensive. For example, for each change, managers in a factory may be required to manually create a list of inventory items to be picked up by workers. Uncertainty in predicting the appropriate pick-up time due to differences in worker-determined routes may make it difficult for managers to generate a fixed schedule for delivery to the assembly line. To address these problems, we propose OPaPi, a human-centric system that improves the efficiency of manufacturing by optimizing parts pick-up routes and schedules. OPaPi leverages frequent pattern mining and the traveling salesman problem solver to suggest rack placement for more efficient routes. The system further employs interactive visualization to incorporate an expert’s domain knowledge and different manufacturing constraints for real-time adaptive decision making.

Work breaks -- both physical and digital -- play an important role in productivity and workplace wellbeing. Yet, the growing availability of digital distractions from online content can turn breaks into prolonged "cyberloafing". In this paper, we present UpTime, a system that aims to support workers' transitions from breaks back to work--moments susceptible to digital distractions. Combining a browser extension and chatbot, users interact with UpTime through proactive and reactive chat prompts. By sensing transitions from inactivity, UpTime helps workers avoid distractions by automatically blocking distracting websites temporarily, while still giving them control to take necessary digital breaks. We report findings from a 3-week comparative field study with 15 workers. Our results show that automatic, temporary blocking at transition points can significantly reduce digital distractions and stress without sacrificing workers' sense of control. Our findings, however, also emphasize that overloading users' existing communication channels for chatbot interaction should be done thoughtfully.

We present an approach to detect speech impairments from video of people with aphasia, a neurological condition that affects the ability to comprehend and produce speech. To counter inherent privacy issues, we propose a cross-media approach using only visual facial features to detect speech properties without listening to the audio content of speech. Our method uses facial landmark detections to measure facial motion over time. We show how to detect speech and pause instances based on temporal mouth shape analysis and identify repeating mouth patterns using a dynamic warping mechanism. We relate our developed features for pause frequency, mouth pattern repetitions, and pattern variety to actual symptoms of people with aphasia in the AphasiaBank dataset. Our evaluation shows that our developed features are able to reliably differentiate dysfluent speech production of people with aphasia from those without aphasia with an accuracy of 0.86. A combination of these handcrafted features and further statistical measures on talking and repetition improves classification performance to an accuracy of 0.88.

Video telehealth is growing to allow more clinicians to see patients from afar. As a result, clinicians, typically trained for in-person visits, must learn to communicate both health information and non-verbal affective signals to patients through a digital medium. We introduce a system called ReflectLive that senses and provides real-time feedback about non-verbal communication behaviors to clinicians so they can improve their communication behaviors. A user evaluation with 10 clinicians showed that the real-time feedback helped clinicians maintain better eye contact with patients and was not overly distracting. Clinicians reported being more aware of their non-verbal communication behaviors and reacted positively to summaries of their conversational metrics, motivating them to want to improve. Using ReflectLive as a probe, we also discuss the benefits and concerns around automatically quantifying the “soft skills” and complexities of clinician-patient communication, the controllability of behaviors, and the design considerations for how to present real-time and summative feedback to clinicians.

Work breaks can play an important role in the mental and physical well-being of workers and contribute positively to productivity. In this paper we explore the use of activity-, physiological-, and indoor-location sensing to promote mobility during work-breaks. While the popularity of devices and applications to promote physical activity is growing, prior research highlights important constraints when designing for the workplace. With these constraints in mind, we developed BreakSense, a mobile application that uses a Bluetooth beacon infrastructure, a smartphone and a smartwatch to encourage mobility during breaks with a game-like design. We discuss constraints imposed by design for work and the workplace, and highlight challenges associated with the use of noisy sensors and methods to overcome them. We then describe a short deployment of BreakSense within our lab that examined bound vs. unbound augmented breaks and how they affect users’ sense of completion and readiness to work.

As workstyles change to include more dynamic contexts and denser spaces, connected objects and spaces in the workplace can play a bigger role in helping people get their work done, while also helping them navigate the continually blending boundaries between work- and home lives. In this talk, we argue that the workplace is particularly well-suited for realizing the "connected life" by including both company-initiated sensing in the workplace and personal tracking devices introduced by individual workers. We describe some examples of ubiquitous sensing in the workplace, and future opportunities as well as open technical and ethical issues for designing for the connected [work]life.