ترغب بنشر مسار تعليمي؟ اضغط هنا

Significant Otter: Understanding the Role of Biosignals in Communication

85   0   0.0 ( 0 )
 نشر من قبل Fannie Liu
 تاريخ النشر 2021
  مجال البحث الهندسة المعلوماتية
والبحث باللغة English




اسأل ChatGPT حول البحث

With the growing ubiquity of wearable devices, sensed physiological responses provide new means to connect with others. While recent research demonstrates the expressive potential for biosignals, the value of sharing these personal data remains unclear. To understand their role in communication, we created Significant Otter, an Apple Watch/iPhone app that enables romantic partners to share and respond to each others biosignals in the form of animated otter avatars. In a one-month study with 20 couples, participants used Significant Otter with biosignals sensing OFF and ON. We found that while sensing OFF enabled couples to keep in touch, sensing ON enabled easier and more authentic communication that fostered social connection. However, the addition of biosignals introduced concerns about autonomy and agency over the messages they sent. We discuss design implications and future directions for communication systems that recommend messages based on biosignals.



قيم البحث

اقرأ أيضاً

Efforts to make machine learning more widely accessible have led to a rapid increase in Auto-ML tools that aim to automate the process of training and deploying machine learning. To understand how Auto-ML tools are used in practice today, we performe d a qualitative study with participants ranging from novice hobbyists to industry researchers who use Auto-ML tools. We present insights into the benefits and deficiencies of existing tools, as well as the respective roles of the human and automation in ML workflows. Finally, we discuss design implications for the future of Auto-ML tool development. We argue that instead of full automation being the ultimate goal of Auto-ML, designers of these tools should focus on supporting a partnership between the user and the Auto-ML tool. This means that a range of Auto-ML tools will need to be developed to support varying user goals such as simplicity, reproducibility, and reliability.
We present Animo, a smartwatch app that enables people to share and view each others biosignals. We designed and engineered Animo to explore new ground for smartwatch-based biosignals social computing systems: identifying opportunities where these sy stems can support lightweight and mood-centric interactions. In our work we develop, explore, and evaluate several innovative features designed for dyadic communication of heart rate. We discuss the results of a two-week study (N=34), including new communication patterns participants engaged in, and outline the design landscape for communicating with biosignals on smartwatches.
With the advent of wearable technologies, Human Body Communication (HBC) has emerged as a physically secure and power-efficient alternative to the otherwise ubiquitous Wireless Body Area Network (WBAN). Whereas the most investigated nodes of HBC have been Electric and Electro-quasistatic (EQS) Capacitive and Galvanic, recently Magnetic HBC (M-HBC) has been proposed as a viable alternative. Previous works have investigated M-HBC through an application point of view, without developing a fundamental working principle for the same. In this paper, for the first time, a ground up analysis has been performed to study the possible effects and contributions of the human body channel in M-HBC over a broad frequency range (1kHz to 10 GHz), by detailed electromagnetic simulations and supporting experiments. The results show that while M-HBC can be successfully operated as a body area network, the human body itself plays a minimal or negligible role in its functionality. For frequencies less than about 30 MHz, in the domain of operation of Magneto-quasistatic (MQS) HBC, the human body is transparent to the quasistatic magnetic field. Conversely for higher frequencies, the conductive nature of human tissues end up attenuating Magnetic HBC fields due to Eddy currents induced in body tissues, eliminating the possibility of the body to support efficient waveguide modes. With this better understanding at hand, different modes of operations of MQS HBC have been outlined for both high impedance capacitive and 50 Ohm termination cases, and their performances have been compared with EQS HBC for similar sized devices, over varying distance between TX and RX. The resulting report presents the first fundamental understanding towards M-HBC operation and its contrast with EQS HBC, aiding HBC device designers to make educated design decisions, depending on mode of applications.
Program tracing, or mentally simulating a program on concrete inputs, is an important part of general program comprehension. Programs involve many kinds of virtual state that must be held in memory, such as variable/value pairs and a call stack. In t his work, we examine the influence of short-term working memory (WM) on a persons ability to remember program state during tracing. We first confirm that previous findings in cognitive psychology transfer to the programming domain: people can keep about 7 variable/value pairs in WM, and people will accidentally swap associations between variables due to WM load. We use a restricted focus viewing interface to further analyze the strategies people use to trace through programs, and the relationship of tracing strategy to WM. Given a straight-line program, we find half of our participants traced a program from the top-down line-by-line (linearly), and the other half start at the bottom and trace upward based on data dependencies (on-demand). Participants with an on-demand strategy made more WM errors while tracing straight-line code than with a linear strategy, but the two strategies contained an equal number of WM errors when tracing code with functions. We conclude with the implications of these findings for the design of programming tools: first, programs should be analyzed to identify and refactor human-memory-intensive sections of code. Second, programming environments should interactively visualize variable metadata to reduce WM load in accordance with a persons tracing strategy. Third, tools for program comprehension should enable externalizing program state while tracing.
80 - Chien-Ju Ho , Ming Yin 2018
Crowdsourcing has gained popularity as a tool to harness human brain power to help solve problems that are difficult for computers. Previous work in crowdsourcing often assumes that workers complete crowdwork independently. In this paper, we relax th e independent property of crowdwork and explore how introducing direct, synchronous, and free-style interactions between workers would affect crowdwork. In particular, motivated by the concept of peer instruction in educational settings, we study the effects of peer communication in crowdsourcing environments. In the crowdsourcing setting with peer communication, pairs of workers are asked to complete the same task together by first generating their initial answers to the task independently and then freely discussing the tasks with each other and updating their answers after the discussion. We experimentally examine the effects of peer communication in crowdwork on various common types of tasks on crowdsourcing platforms, including image labeling, optical character recognition (OCR), audio transcription, and nutrition analysis. Our experiment results show that the work quality is significantly improved in tasks with peer communication compared to tasks where workers complete the work independently. However, participating in tasks with peer communication has limited effects on influencing workers independent performance in tasks of the same type in the future.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا