Ahreum LeeUniversity of Eastern Finland | UEF · School of Computing
Ahreum Lee
Ph.D, Industrial Engineering
About
33
Publications
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316
Citations
Introduction
Additional affiliations
October 2013 - December 2013
Education
March 2009 - February 2013
Publications
Publications (33)
Purpose:
The main goals of this mixed-methods systematic review are to identify what types of intraoperative stressors for operating room personnel have been reported in collected studies and examine the characteristics of each intraoperative stressor.
Methods:
With a systematic literature search, we retrieved empirical studies examining intraop...
The platforms that host online gaming groups and communities continue to evolve, and it has become possible to join, participate in, and consume content from groups that exist across multiple tools, platforms, and spaces at the same time. In this paper, we explore how groups use and rely upon assemblages of multiple online spaces to accomplish the...
Real-time mental stress monitoring from surgeons and surgical staff in operating rooms may reduce surgical injuries, improve performance and quality of medical care, and accelerate implementation of stress-management strategies. Motivated by the increase in usage of objective and subjective metrics for cognitive monitoring and by the gap in reviews...
Research in surgical intervention and technology development is increasingly interdisciplinary. Despite the great potential of working in this way, recent research suggests that interdisciplinary collaborations and competing stakeholder interests can be challenging to initiate and manage, with the result that knowledge and expertise from different...
External Human-Machine Interfaces (eHMI) are widely used on robots and autonomous vehicles to convey the machine's intent to humans. Delivery robots are getting common, and they share the sidewalk along with the pedestrians. Current research has explored the design of eHMI and its effectiveness for social robots and autonomous vehicles, but the use...
While research on human-robot interaction is ongoing as robots become more readily available and easier to use, the study of interactions between a human and a team of multiple robots represents a relatively new field of research. In particular, how multi-robots could be used for everyday users and how the characteristics of multi-robots would affe...
As robots engage more in society in various forms, it is important to understand the public perception of robots. In this poster, we focus on a campus-centric subreddit to explore online discourse about delivery robots on university campus. We specifically identify how people share their experiences with robots and how people perceive robots in soc...
Design practitioners are increasingly engaged in describing ethical complexity in their everyday work, exemplified by concepts such as "dark patterns" and "dark UX." In parallel, researchers have shown how interactions and discourses in online communities allow access to the various dimensions of design complexity in practice. In this paper, we con...
This paper introduces a new ROSbag-based multimodal affective dataset for emotional and cognitive states generated using Robot Operating System (ROS). We utilized images and sounds from the International Affective Pictures System (IAPS) and the International Affective Digitized Sounds (IADS) to stimulate targeted emotions (happiness, sadness, anger...
This paper presents a framework for monitoring human and robot conditions in human multi-robot interactions. The proposed framework consists of four modules: 1) human and robot conditions monitoring interface, 2) synchronization time filter, 3) data feature extraction interface, and 4) condition monitoring interface. The framework is based on Robot...
The rise of co-working and co-living spaces, as well as related shared spaces such as makerspaces and hackerspaces-a group we refer to as various types of "co-spaces" - has helped facilitate a parallel expansion of the "digital nomad (DN)" lifestyle. Digital nomads, colloquially, are those individuals that leverage digital infrastructures and socio...
Purpose
The purpose of this paper is to explore how people differently create meaning from photos taken by either a lifelogging camera (LC) (i.e. automatic capture) or a mobile phone camera (MC) (i.e. manual capture). Moreover, the paper investigates the different changes in the interpretative stance of lifelog photos and manually captured photos o...
Co-working and co-living companies are rising globally and the increasing participation within the gig economy has extended the range of users of community-based spaces (co-spaces) and raised a set of different community models in considering how to support them. In this paper, we specifically focus on the needs of digital nomads in co-spaces who s...
The lifelogging camera continuously captures one's surroundings, therefore lifelog photos can form a medium by which to sketch out and share one's autobiographical memory with others. Frequently, the lifelog photos do not provide the context or significance of the situations to those not present when the photos were taken. This paper solicits the s...
Older adults are known to have lesser cognitive control capability and greater susceptibility to distraction than young adults. Previous studies have reported age-related problems in selective attention and inhibitory control, yielding mixed results depending on modality and context in which stimuli and tasks were presented. The purpose of the stud...
The inability to complete instrumental activities of daily living (IADL) is a precursor to various neuropsychological diseases. Questionnaire-based assessments of IADL are easy to use but prone to subjective bias. Here, we describe a novel virtual reality (VR) test to assess two complex IADL tasks: handling financial transactions and using public t...
As the local manufacturing industry has entered a phase of stagnation, service and product design based on user experience has been highlighted as an alternative for the innovation. However, SMEs(Small and Medium-sized Enterprises) are still struggling to overcome the current crisis. One of the reasons is that SMEs do not have enough contact points...
중소기업과 UX 전문가 간 협업 지원 환경 구축의 필요성에 따라, 전문가와 수요 기업을 대상으로 지식 마켓, 매칭 지원, 협업 프로젝트 관리 지원 서비스를 제공하는 온라인 플랫폼이 개발 중이다. 본 연구는 이 플랫폼 구축 사업의 초기 단계에서 온라인 전문 커뮤니티의 성공적 개발을 위해 고려해야 할 가치 속성 도출을 목표로 한다. 온라인 커뮤니티의 성공요인에 대해 커뮤니티 유형 및 수명 주기와 결부하여 기존 연구가 있었지만 일반적인 관점을 나타내는 한계가 있었다. 따라서 본 플랫폼 구축에 실제적으로 고려할 가치 추출을 위해 이해당사자들 대상으로 이슈 발상 워크샵, 기능 분류 워크샵, 가치 추출 워크샵을 순차적으로 시행하였...
Sustaining user engagement is an important criterion for a success of life-log devices. However, wearable camera seems have not extended out beyond its initial adoption due to its voluminous photo data collected. This paper presents a brief overview of challenges and opportunities of photo archives from wearable camera, by which highlights what wou...
The wearable camera industry is facing low
adoption rates due to concerns over the amount of data
the devices collect and the inability to differentiate from
mobile phones and digital cameras. To improve
adoption rates, the perception of the wearable camera
should be changed. This research attempts to
portray mobile cameras as tools for personal
ex...
Although the privacy issue is still debated, an incessant interest and attention on the lifelog could be explained by its potential applications. Though there are many challenges to realize its potentials, lifelog as one’s memory aiding tool has been paid much attention. A series of images captured by the wearable camera are too voluminous and usel...
Mobile learning has been a topic of research and development for 20 years. Over that time it has encompassed a wide range of concepts, theories, designs, experiments and evaluations. With increasing interest in the subject from researchers and practitioners, a comprehensive, yet accessible, overview of mobile learning that encompasses its many face...
An increasingly widespread interest in developing fully adaptable e-learning systems (e.g., intelligent tutoring systems) has led to the development of a wide range of adaptive processes and techniques. In particular, advances in these systems are based on optimization for each user's learning style and characteristics, to enable a personalized lea...
With the 4G mobile technology, LG U+ established a new business model, inter-network mirroring game service, that allows PC and mobile game users to play against each other. However, due to an unsolicited input command design for touch-sensitive UIs, it is hard to adjust competitive levels between them. The traditional Keystroke-Level Model (KLM) w...
Questions
Questions (4)
Dear all,
I am looking for a proper method to choose the number of clusters for K modes.
I tried to find the optimal number of clusters by maximizing the average silhouette width though.
In k-modes, the average silhouette width increases with the increase of the number of clusters with my case.
So i tried to derive the elbow plot and I got the attached graph.
It is quite hard to which point is the location of a bend in this plot..
In this case, how can I choose the best number of groups?
Can anyone introduce better method that help choose the optimal number of clusters for K-modes?
Is there anyone who would verify my code about filtering the fNIRS data?
hello,
my name is anna lee and my major is based on Human-Computer Interaction.
We use the Sectratech OEG-16 (fNIRS device) to analyze the relative change in hemoglobin levels.
To filter the raw data, we referenced many articles and built a matlab code with following procedure.
1.To eliminate the trends and do the DC offset,
we use the detrend function.
2. To eliminate the motion effects, do the Common average reference(CAR)
: (raw data - the total average of data)
3. Band-pass filtering (LPF 0.1hz/ HPF 0.01hz)
however, i am not sure whether i did right or not...
it would be much helpful if anyone would verify my code or give some comments
(i attached the matlab code)
Thansk :)
Hello, I have just started to use fNIRS to compare the cognitive workload of subjects when they saw some emotional pictures.
I want to compare the Hemodynamic response change between when they saw a negative picture and a positive one.
We use the Spetratech OEG-16 (it has 16 channels)
First of all, I did the preprocessing of the raw fNIRs data as follows
(do the linear detrend detection -> and do the CAR (by (each sample data - total(16) average -> Do bandpass filtering (0.01~0.1Hz))
and now I try to normalize the filtered data.
But I am confused the way to normalize the data.
Could you tell me how you normalize the data?
This is our code with latent class model:
model { # Marginal tabulations of Latent Diagnosis against Observed Items for (i in 1:n) { for (j in 1:K) { for (k in 1:2) {M1[j,k,i] <- equals(T[i],j)*equals(Y[i,1],k-1)}}} for (j in 1:K) {for (k in 1:2) {Tab1[j,k] <- sum(M1[j,k,1:n])}}
for (i in 1:n) { for (j in 1:K) { for (k in 1:2) {M2[j,k,i] <- equals(T[i],j)*equals(Y[i,2],k-1)}}} for (j in 1:K) {for (k in 1:2) {Tab2[j,k] <- sum(M2[j,k,1:n])}}
for (i in 1:n) { for (j in 1:K) { for (k in 1:2) {M3[j,k,i] <- equals(T[i],j)*equals(Y[i,3],k-1)}}} for (j in 1:K) {for (k in 1:2) {Tab3[j,k] <- sum(M3[j,k,1:n])}}
Bernoulli sampling
for (s in 1:n) {for (j in 1:M) {Y[s,j] ~ dbern(pi[T[s],j]) # new item data Z[s,j] ~ dbern(pi[T[s],j])} for (h in 1:2) { for (i in 1:2) { for (j in 1:2) { g[h,i,j,s] <- equals(Z[s,1]+1,h)*equals(Z[s,2]+1,i)*equals(Z[s,3]+1,j)}}}}
Implied aggregate table (G.new)
for (h in 1:2) { for (i in 1:2) { for (j in 1:2) {G[h,i,j] <- sum(g[h,i,j,])}}}
for (k in 1:M) {for (j in 1:K) {logit(pi[j,k]) <- theta[j,k]}}
Posterior memberships
for (i in 1:n) { for (j in 1:K) {post[j,i] <- equals(T[i],j)}}
Priors
for (k in 1:M) {theta[1,k] ~ dnorm(0,0.1) I(,theta[2,k]) theta[2,k] ~ dnorm(0,0.1) I(theta[1,k],)} eta.s[1] ~ dgamma(w1,1) I(,eta.s[2]) eta.s[2] ~ dgamma(w1,1) I(eta.s[1],) for (j in 1:K) {eta[j] <- eta.s[j]/sum(eta.s[])} for (i in 1:n) {T[i] ~ dcat(eta[])}}
B: Data list( n = 103, # number of patients observed K = 2, # number of latent classes M = 3 , # number of diagnostic tests w1 =1,
observed item data
Y=structure(.Data=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,1,0,1,1,0,1,1,0,1,1,0,1,1,0,1,1,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,1,1,0,1,1,0,1,1,0,1,1,0,1,1,0,1,1,0,1,1,0,1,1,1,0,1,1,0,1,1,0,1,1,0,1,1,0,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1),.Dim=c(103,3)))
B: Inits list(T=c(1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2, 1,2,1,2,1,2,1,2,1,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1),eta.s=c(0.5,0.5),theta=structure(.Data=c(0,0,0,0,0,0),.Dim=c(2,3)),Z=structure(.Data=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,1,0,1,1,0,1,1,0,1,1,0,1,1,0,1,1,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,1,1,0,1,1,0,1,1,0,1,1,0,1,1,0,1,1,0,1,1,0,1,1,1,0,1,1,0,1,1,0,1,1,0,1,1,0,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1),.Dim=c(103,3)))
list(T=c(1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2, 1,2,1,2,1,2,1,2,1,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1),eta.s=c(0.427, 0.573),theta=structure(.Data=c(-2.5, -3.3, -2.7, 1.7, 1.4, 1.72),.Dim=c(2,3)),Z=structure(.Data=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,1,0,1,1,0,1,1,0,1,1,0,1,1,0,1,1,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,1,1,0,1,1,0,1,1,0,1,1,0,1,1,0,1,1,0,1,1,0,1, 1,1,0,1,1,0,1,1,0,1,1,0,1,1,0,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1),.Dim=c(103,3)))
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Until I had done model check-up, there are no errors.
However, when I tried to "update" the sample, suddenly the trap message said "undefined real result" popped up.
How can I deal with this problem?
