ArticlePDF Available

Activity-logging for self-coaching of knowledge workers

  • Radboud University Nijmegen & TNO

Abstract and Figures

With an increased societal focus on a sustainable economy and a healthy population, well-being of knowledge workers has become an important topic. This paper investigates techniques to support a knowledge worker to manage his well-being. A possible solution is to monitor the workers' behaviour and use this information for giving feedback as soon as his well-being is threatened. Knowledge workers use a broad range of communication means to achieve their goals, like a computer and mobile phone. Our research aims at using features like mouse clicks, active applications or key presses, because these are rather simple features to ob-tain instead of more invasive tools like a heart-rate monitor. This paper presents the first results of our research. First, logging of low-level features is developed. Based on these features the behaviour of different users is investigated. At first sight, this behaviour seems to be rather chaotic, but by taking into account different tasks, more structure is ob-served within the data. This paper shows that different be-haviour is observed for different users and different tasks, while the same characteristics are observed when a user is performing the same task. This suggests that also anoma-lous behaviour might be recognized, which is an important result for developing self-coaching tools.
Content may be subject to copyright.
Activity-logging for self-coaching of knowledge workers
Saskia Koldijk
Radboud University Nijmegen
The Netherlands
Mark van Staalduinen
Delft, The Netherlands
Stephan Raaijmakers
Delft, The Netherlands
Wessel Kraaij
Institute for Computing and
Information Sciences
Radboud University Nijmegen
Iris van Rooij
Donders Institute for Brain,
Cognition, and Behaviour
Radboud University Nijmegen
With an increased societal focus on a sustainable economy
and a healthy population, well-being of knowledge workers
has become an important topic. This paper investigates
techniques to support a knowledge worker to manage his
well-being. A possible solution is to monitor the workers’
behaviour and use this information for giving feedback as
soon as his well-being is threatened. Knowledge workers
use a broad range of communication means to achieve their
goals, like a computer and mobile phone. Our research aims
at using features like mouse clicks, active applications or
key presses, because these are rather simple features to ob-
tain instead of more invasive tools like a heart-rate monitor.
This paper presents the first results of our research. First,
logging of low-level features is developed. Based on these
features the behaviour of different users is investigated. At
first sight, this behaviour seems to be rather chaotic, but
by taking into account different tasks, more structure is ob-
served within the data. This paper shows that different be-
haviour is observed for different users and different tasks,
while the same characteristics are observed when a user is
performing the same task. This suggests that also anoma-
lous behaviour might be recognized, which is an important
result for developing self-coaching tools.
In the modern knowledge economy, the demands for pro-
ductivity of knowledge workers are steadily increasing. At
the same time, information sources and communication means
are more fragmented than ever. Real-time communication
means, such as e-mail, (micro)blogging and other social me-
dia have generated an overflow of information, lacking a
structure that is adapted to the user’s tasks. Networked
information systems and portable devices make it possible
to work anywhere, posing challenges to context aware net
Permission to make digital or hard copies of all or part of this work for
personal or classroom use is granted without fee provided that copies are
not made or distributed for profit or commercial advantage and that copies
bear this notice and the full citation on the first page. To copy otherwise, to
republish, to post on servers or to redistribute to lists, requires prior specific
permission and/or a fee.
Copyright 20XX ACM X-XXXXX-XX-X/XX/XX ...$10.00.
centric organisation of documents, task lists etc. Finally,
since the work force in Western countries is ageing it is in-
creasingly important to develop supportive techniques that
help people having a reduced work capacity due to a medi-
cal condition to maintain a healthy work-style. The project
User Centric Reasoning for Well-working (UCR4W)1is in-
vestigating the key determinants for well-being at work. One
of the guiding hypotheses of the project is that logging the
activities of knowledge workers can be the basis for an ef-
fective computer based coach. The objective of the project
is to develop user-centric sensing and reasoning techniques
that help to improve well-being at home and at work. Tech-
nology should help people improve their sense of being and
feeling in control, with a positive impact on work efficiency
and effectiveness, work pleasure, mental and physical health
status. An example of empowerment is to have relevant in-
formation from personal data collections available ’just-in-
time’. We think that understanding the activities and tasks
of individuals is a key condition to achieve this.
In this paper we describe a study that is carried out as a
precursor to the UCR4W project and present initial results
of an experiment. The underlying idea of the study is that
knowledge workers could possibly be helped to adapt their
work style by providing them neutral feedback about their
work style and activities. Section 2 discusses the background
and assumptions of the study. Experimental results are pre-
sented in Section 3. We conclude with the implications of
our results and future research in Section 4.
The information overload and context switches of knowl-
edge workers can be a threat for productivity and well-being
at work. Let us consider the following scenario:
Imagine a typical working day as a knowledge worker. You
have different projects running and today your plan is to
work on project A and B. For project A you do some inter-
net search and start typing a document. While you are busy
1UCR4W is a 8 MEuro 4 year project co-funded through
the Dutch national research programme COMMIT. Partners
are: TNO, Novay, University of Twente, Radboud Univer-
sity Nijmegen, Philips research, Ericsson, Roessing R&D,
Noldus Information Technology, Netherlands Centre for So-
cial Innovation, Dutch Ministry of the Interior.
a colleague asks your help for project C. You interrupt your
work to help her search for some information. When a mail
about project B arrives you decide to switch to this project
as it is quite urgent to finish the required document. Sud-
denly you notice it is 5 o’ clock and you did not finish your
work on project A as planned. You might start wondering:
How did I spend my time today?
The relation between work-style and well-being at work
Knowledge workers typically have various tasks and dead-
lines and they have to produce results. The possibility to
easily switch tasks makes working very fragmented. The
course of action is not always self planned but also deter-
mined by external causes, like phone calls, mails, informa-
tion requests, other persons or appointments [3]. Knowledge
workers typically have to self-manage their work and make
a good planning in order to be able to accomplish all their
tasks. All in all this way of working easily causes a feeling of
stress and it is quite difficult to keep a good overview what
it is one has done over the course of a day, weeks or even
months. A study has shown that knowledge workers often
spend effort in tracking their own tasks (13%). Automating
this process would be of great benefit for the working pro-
cess. A system that could monitor and provide overviews
of performed activities could support the worker with her
self-management, adapt her work-style [1] and in this way
diminish cognitive load and stress. More awareness of ones
own working process might also have beneficial effects on
the on-task behavior and adherence to scheduled activities
Feedback based on action recognition
As a first step to test the hypothesis that tracking activ-
ity and work-style can improve well-being at work a simple
feedback tool is under development which can automatically
infer and log the tasks a user is performing. The log infor-
mation could be presented in the form of a daily or weekly
overview, showing the amount of time spent on tasks and
the number of interruptions or task switches. Such a tool
requires the following steps: i) design of an activity ontol-
ogy, ii) automatic logging of low level computer interaction
data iii) developing an inference module that maps low level
activity to the activity ontology level iv) developing an ef-
fective presentation mode for feedback purposes. In this
paper we report work on the first three steps, the main con-
tributions of our study are related to i) and iii). The first
step necessary in this research is the creation of a taxonomy
of tasks people could be performing. Several taxonomies of
tasks have already been proposed in the literature. The tax-
onomies about internet use by Morrison, Pirolli and Card [7]
indicate that a distinction between actions on three different
levels might be appropriate: The method the user adopts,
the purpose of his actions and the specific content. The
next step of this project is task recognition. A model will
be made for the inference process from simple logging data
to higher level tasks. We intend to compare several types
of features, both static and temporal in combination with
various classifier schemes. In section 3 we will report initial
work, since the experiments are still ongoing. A final step is
connecting the tasks recognition module to a graphical user
interface. It is important to make the interaction with the
system as pleasant as possible. Myers et al. [8] state that
the system should be directable, personalizable, teachable
and transparent. So in order to work well the system should
optimally cooperate with – and adapt to – to the user. The
tool should provide a means for the user to give feedback
without irritating him and it should keep learning.
Related work
There has already been done much theoretical and applied
research in the field of action understanding (e.g. [2, 4, 11]).
Research on pattern recognition in sensor data, multimodal
fusion and models for human goal directed behaviour are
relevant for our work. Some research has specifically focused
on recognizing patterns of user activities on PCs [6, 10].
These studies all focus on the detection of a specific kind of
information to trigger certain actions. Our research differs
as we want to log all kinds of activity in order to make a
human understandable overview and categorization of tasks.
Our intent is to give the user a better overview and more
awareness about his working process and in this way help
him improving his performance.
3.1 Task analysis
Taking a user centred approach, a questionnaire was used
to investigate the typical way of working of knowledge work-
ers and their demands on software supporting them in their
daily practices. From the responses by 47 knowledge workers
at TNO we concluded that for a tool to be usable for sup-
port, the captured activity data should be aggregated to a
higher level in order to provide the user with valuable infor-
mation. The recognition of the task a user is performing is a
useful first step towards providing the user understandable
feedback and insights about his working process. On the ba-
sis of the questionnaire a set of tasks that knowledge workers
perform was identified. The answers to the questions ‘What
tasks do you perform and how do you use your computer for
this?’ and ‘Describe a typical working day’ were manually
grouped into sets of similar answers to derive a set of typical
task types. The appropriateness of the identified set of task
types was confirmed by several knowledge workers. From all
task types, the tasks performed at the computer were finally
selected for automatic task recognition (see Table 1 for the
task labels used).
3.2 Data collection
After identification of the software demands by the users,
our next step consisted of investigating whether the com-
puter could possibly fulfill these demands. In an experimen-
tal phase the computer activities of three knowledge workers
were logged using uLog.2An additional tool was created
that reminded the user every 10 minutes to annotate his
or her activity by selecting one of the labels from the task
list (and indicating his level of wellworking). About two
weeks of data collection resulted in a labelled raw data set.
The labelled raw data set was processed to extract several
features, for example how often the user clicked or which
application was mainly in focus within a five minute time
frame (cf. Table 3 for a full list of extracted features; cf.
Table 1 for the amount of data points per label). In total
20, 180, 66 labelled segments were recorded for the three
users respectively.
Table 1: Dataset - amount of data per label
Task label # data as percentage F-value
read mail 11 4% 0.583
write mail 12 5% 0.348
organize archive data 5 2% 0
plan 14 5% 0
make presentation 3 1% 1
create visualisation 4 2% 0.857
program 63 24% 0.977
write report paper 82 31% 0.8
search information 17 6% 0.654
read article text 17 6% 0.746
make overview 31 12% 0.621
analyse data 7 3% 0
TOTAL 266 100% 0.656
3.3 Analysis of the labelled data
First analysis showed that distinguishable patterns of com-
puter activity arose per assigned task label. The most in-
dicative feature seems to be the application that was mainly
in focus, which is logical as specific tasks require specific
applications, as for example ‘programming’ is done in a pro-
gramming application. But there is not always a simple one
to one mapping between application and task. For both
the tasks ‘write report’ and ‘search information’ Word has
main focus, but someone ‘searching for information’ addi-
tionally uses an Internet browser and AcrobatReader (see
Figure 1). Therefore the distribution of all applications in
the time frame should be considered.
Besides the used applications the keyboard and mouse
activity can be used to further distinguish tasks. Figure
2 shows the distribution of clicks and typed characters for
the different task labels. Some features alone already have
discriminative power (see Figure 3 for an indication of infor-
mation gain ratio per feature), for example the amount of
typed characters is about 0 for searching information, about
50 for mail writing and about 200 for report writing. Com-
bining more features increases the discriminative power, for
example tasks not discriminable by number of typed charac-
ters (for example writing mail and making an overview, both
about 50 typed characters) could be recognized on basis of
the number of clicks (about 40 vs. about 80).
A final useful feature that could indicate the task a user is
performing is the amount of switching between different ap-
plications. Figure 3 plots the typical distribution for various
users to show that there are clear individual differences.
3.4 Experiment: Automatic activity labelling
Some initial results about automatic activity labelling are
available (see Table 2). We used Weka (see Hall et al. [5]) to
train some classifiers and tested their performance by means
of 10 fold cross validation. Labelling each activity simply as
the majority class ’write report/ paper’ with Weka’s ZeroR
classifier yielded a baseline accuracy of 30.83% (F=0.145).
Using Weka’s Naive Bayes classifier with just the feature
mainApp to classify tasks resulted in an accuracy of 59.77%
(F=0.468), so we can conclude that the application that
was mainly in focus is a very strong feature. Adding the
other features with mouse and keyboard information and in-
dications about active applications and application switches
Figure 1: Distribution of application usage (as per-
centage of time that the application was in focus)
per task
Figure 2: Distribution of #clicks and #characters
per task
(discretized with Weka’s preprocessing option) improved the
classification accuracy to 70.30% (macro-averaged F=0.656;
F values per task can be found in Table 1). Leaving out all
features that address the use of specific applications, clas-
sification accuracy drops to 52%, with an average F=0.45,
which stresses that application-dependent information is im-
portant as well for task identification.
We have found promising first results showing that it is
feasible to log the activities of knowledge workers and use
this information to classify the tasks that they are perform-
ing. Future research at TNO will focus on extending this
research to larger data sets and more systematic compar-
isons of different task classifiers. The present results suggest
individual differences between users, indicating that person-
alization may be an essential feature of a task classification
Figure 3: Application usage and switching behavior
per user
Table 2: Classification - initial results
Classifier Accuracy Averaged F
Baseline (classify as main class) 30.83% 0.145
Naive Bayes (use only mainApp) 59.77% 0.468
Naive Bayes (use all features) 70.30% 0.656
The next steps within the UCR4W project aim at the
recognition of anomalous behaviour for each task that might
signal a decreasing well-being of a worker. The data col-
lection will probably be extended with a component that
captures some semantic content that helps to model the in-
teraction of well-being with an activity related to a particu-
lar project. Furthermore, the project will evaluate the self-
coaching tools together with end-users in order to improve
its acceptance. Finally, proper privacy protection mecha-
nisms and procedures will be an integral part of the project,
because these tools are based on personal data.
[1] C. Argyris and D. Sch¨
on. Organizational learning: A
theory of action perspective. Addison Wesley, 1978.
[2] C. L. Baker, R. Saxe, and J. B. Tenenbaum. Action
understanding as inverse planning. Cognition, 113(3):329 –
349, 2009. Reinforcement learning and higher cognition.
[3] M. Czerwinski, E. Horvitz, and S. Wilhite. A diary study of
task switching and interruptions. In CHI ’04: Proceedings
of the SIGCHI conference on Human factors in computing
systems, pages 175–182, New York, NY, USA, 2004. ACM.
[4] T. Duong, H. Bui, D. Phung, and S. Venkatesh. Activity
recognition and abnormality detection with the switching
hidden semi-markov model. volume 1, pages 838 – 845 vol.
1, jun. 2005.
[5] M. Hall, E. Frank, G. Holmes, B. Pfahringer,
P. Reutemann, and I. H. Witten. The weka data mining
software: an update. SIGKDD Explor. Newsl., 11:10–18,
November 2009.
[6] E. Horvitz, J. Breese, D. Heckerman, D. Hovel, and
D. Rommelse. The lumiere project: Bayesian user modeling
for inferring the goals and needs of software users. In
Fourteenth Conference on Uncertainty in Artificial
Intelligence, pages 256–265. Morgan Kaufmann Publishers,
July 1998.
Table 3: Features extracted within 5 minute time-
frames, sorted by information gain ratio (GR)
Feature name Description GR
mspaint % time that mspaint had focus 0.847
user the user who logged and labelled
the data
programApp % time that a programming appli-
cation had focus (eclipse, cmd...) 0.677
OUTLOOK % time that OUTLOOK had focus 0.626
WINWORD % time that WINWORD had focus
within the timeframe
mainApp application that was most of the
time in focus
AcroRd32 % time that AcroRd32 had focus 0.472
spaces # spaces typed 0.39
characters # characters typed 0.364
backspaces # backspaces (inc. ’delete’-key) 0.361
specialKeys # special keys typed 0.344
clicks # clicks within the timeframe 0.29
switches # switches between applications 0.26
internet % time that an internet applica-
tion had focus (iexplorer, firefox...)
within the timeframe
daytime time of the day (as hour, i.e. 9-18) 0
scrolls # scrolls 0
nrApps # different applications used within
the timeframe 0
time % time this mainApp was in focus 0
editor % time that an editing application
had focus (notepad++, wordpad...)
POWERPNT % time that POWERPNT had fo-
explorer % time that the explorer had focus 0
EXCEL % time that EXCEL had focus 0
MATLAB % time that MATLAB had focus 0
label task label for the activity given by
the user
[7] J. B. Morrison, P. Pirolli, and S. K. Card. A taxonomic
analysis of what world wide web activities significantly
impact people’s decisions and actions. In CHI ’01: CHI ’01
extended abstracts on Human factors in computing
systems, pages 163–164, New York, NY, USA, 2001. ACM.
[8] K. Myers, P. Berry, J. Blythe, K. Conleyn, M. Gervasio,
D. McGuinness, D. Morley, A. Pfeffer, M. Pollack, and
M. Tambe. An intelligent personal assistant for task and
time management. AI Magazine, 28(2):47–61, 2007.
[9] G. S. Richman, M. R. Riordan, M. L. Reiss, D. A. Pyles,
and J. S. Bailey. The effects of self-monitoring and
supervisor feedback on staff performance in a residential
setting. J Appl Behav Anal., 21(4):401 ˝
U409, 1988.
[10] J. Shen, L. Li, T. G. Dietterich, and J. L. Herlocker. A
hybrid learning system for recognizing user tasks from
desktop activities and email messages. In IUI ’06:
Proceedings of the 11th international conference on
Intelligent user interfaces, pages 86–92, New York, NY,
USA, 2006. ACM.
[11] K. Tahboub. Intelligent human-machine interaction based
on dynamic bayesian networks probabilistic intention
recognition. Journal of Intelligent & Robotic Systems,
45:31–52, 2006. 10.1007/s10846-005-9018-0.
... In the pilot studies and the field study, participants uniformly expected different measurements to be visualized at different levels of granularity, similar to findings in other areas [Koldijk et al., 2011;McDuff et al., 2012]. These individual expectations might be explained by the very different types of tasks and work that software developers, even with very similar job profiles, have to accomplish [Meyer et al., 2017a]. ...
Full-text available
Software development organizations strive to enhance the productivity of their developers. All too often, efforts aimed at improving developer productivity are undertaken without knowledge about how developers spend their time at work and how it influences their own productivity. In our research, we focus on two aspects for improving developers' productivity: better understanding developer productivity and using these findings to foster productivity at work. To better understand developer productivity, we took a bottom-up approach by investigating developers' perceptions of productivity in the field, and by examining the individual differences in each developer's work. We found that developers spend their time on a wide variety of activities and tasks that they regularly switch between, resulting in highly fragmented work. Extending our understanding of developers' work and the factors that impact their productivity then allowed us to develop models of developers' work and productivity, and build approaches that support developers with productive behavior changes. To support the identification of self-improvement opportunities that motivate productive behavior changes, we studied how we can increase developers' awareness about work and productivity by combining our models with three persuasive strategies: self-monitoring, self-reflection, and an external indicator. Based on successful applications in the health and physical activity domain and from examining developers’ expectations, we developed PersonalAnalytics, a workplace self-monitoring tool that collects a broad variety of computer interaction data and summarizes the data in a daily and weekly retrospection. A multi-week field-study showed that PersonalAnalytics offered meaningful insights to 82% of the participants, but the insights were not actionable enough to motivate behavior change for 41% of our participants. In a follow-up study, we found that continuous and purposeful self-reflection can motivate productive self-improvements in the workplace, since 83% of our participants stated that it supported the identification of goals and actionable strategies, and 80% reported productivity increasing behavior changes. We further studied how we can increase developers' awareness about their co-workers' availability for interruptions, by sensing and externally indicating interruptibility to developers based on their computer interaction. Our large-scale field study with the FlowLight showed that we can effectively reduce 46% of external interruptions, participants felt more productive, and 86% of them remained active users even after the two-month study period ended. Overall, our research showed that we can successfully foster productivity at developers' work, by increasing their awareness about productive and unproductive work habits, and by encouraging work habit improvements based on the gained insights. In addition, our research can be extended and opens new opportunities to foster productive work for development teams.
... In the pilot studies and the field study, participants uniformly expected different measurements to be visualized at different levels of granularity, similar to findings in other areas [42,52]. These individual expectations might be explained by the very different types of tasks and work that software developers, even with very similar job profiles, have to accomplish [53]. ...
Full-text available
One way to improve the productivity of knowledge workers is to increase their self-awareness about productivity at work through self-monitoring. Yet, little is known about expectations of, the experience with, and the impact of self-monitoring in the workplace. To address this gap, we studied software developers, as one community of knowledge workers. We used an iterative, user-feedback-driven development approach (N=20) and a survey (N=413) to infer design elements for workplace self-monitoring, which we then implemented as a technology probe called WorkAnalytics. We field-tested these design elements during a three-week study with software development professionals (N=43). Based on the results of the field study, we present design recommendations for self-monitoring in the workplace, such as using experience sampling to increase the awareness about work and to create richer insights, the need for a large variety of different metrics to retrospect about work, and that actionable insights, enriched with benchmarking data from co-workers, are likely needed to foster productive behavior change and improve collaboration at work. Our work can serve as a starting point for researchers and practitioners to build self-monitoring tools for the workplace.
... Therefore we analysed the distribution of clicks, typing or other features per user and task. It turned out that even when users were performing the same task their behaviour differed (Koldijk, van Staalduinen, Raaijmakers, van Rooij, & Kraaij, 2011). For example user G typed extraordinary many characters when writing a report and in general clicked more often than other users. ...
Conference Paper
Full-text available
Motivation -- Supporting knowledge workers in their self-management by providing them overviews of performed tasks. Research approach -- Computer interaction data of knowledge workers was logged during their work. For each user different classifiers were trained and compared on their performance on recognizing 12 specified tasks. Findings/Design -- After only a few hours of training data reasonable classification accuracy can be achieved. There was not one classifier that suited all users best. Take away message -- Task recognition based on knowledge workers' computer activities is feasible with little training, although personalization is an important issue.
Employees often report the experience of stress at work. In the SWELL project we investigate how new context aware pervasive systems can support knowledge workers to diminish stress. The focus of this paper is on developing automatic classiers to infer working conditions and stress related mental states from a multimodal set of sensor data (computer logging, facial expressions, posture and physiology). We address two methodological and applied machine learning challenges: 1) Detecting work stress using several (physically) unobtrusive sensors, and 2) Taking into account individual dierences. A comparison of several classication approaches showed that, for our SWELL-KW dataset, neutral and stressful working conditions can be distinguished with 90% accuracy by means of SVM. Posture yields most valuable information, followed by facial expressions. Furthermore, we found that the subjective variable 'mental eort' can be better predicted from sensor data than e.g. 'perceived stress'. A comparison of several regression approaches showed that mental eort can be predicted best by a decision tree (correlation of 0.82). Facial expressions yield most valuable information, followed by posture. We nd that especially for estimating mental states it makes sense to address individual dierences. When we train models on particular subgroups of similar users, (in almost all cases) a specialized model performs equally well or better than a generic model.
Conference Paper
Full-text available
The TaskTracer system seeks to help multi-tasking users manage the resources that they create and access while car- rying out their work activities. It does this by associating with each user-defined activity the set of files, folders, email messages, contacts, and web pages that the user accesses when performing that activity. The initial TaskTracer sys- tem relies on the user to notify the system each time the user changes activities. However, this is burdensome, and users often forget to tell TaskTracer what activity they are work- ing on. This paper introduces TaskPredictor, a machine learning system that attempts to predict the user's current activity. TaskPredictor has two components: one for general desktop activity and another specifically for email. TaskPre- dictor achieves high prediction precision by combining three techniques: (a) feature selection via mutual information, (b) classification based on a confidence threshold, and (c) a hy- brid design in which a Naive Bayes classifier estimates the classification confidence but where the actual classification decision is made by a support vector machine. This paper provides experimental results on data collected from Task- Tracer users.
Full-text available
We describe an intelligent personal assistant that has been developed to aid a busy knowledge worker in managing time commitments and performing tasks. The design of the system was motivated by the complementary objectives of (a) relieving the user of routine tasks, thus allowing her to focus on tasks that critically require human problem-solving skills, and (b) intervening in situations where cognitive overload leads to oversights or mistakes by the user. The system draws on a diverse set of AI technologies that are linked within a Belief-Desire-Intention agent system. Although the system provides a number of automated functions, the overall framework is highly user-centric in its support for human needs, responsiveness to human inputs, and adaptivity to user working style and preferences.
Full-text available
In this article, a novel human-machine interaction based on the machine intention recognition of the human is presented. This work is motivated by the desire that intelligent machines as robots imitate human-human interaction, that is to minimize the need for classical direct human-machine interface and communication. A philosophical and technical background for intention recognition is discussed. Here, the intention-action-state scenario is modified and modeled by Dynamic Bayesian Networks to facilitate for probabilistic intention inference. The recognized intention, then, drives the interactive behavior of the machine such that it complies with the human intention in light of the real state of the world. An illustrative example of a human commanding a mobile robot remotely is given and discussed in details.
Full-text available
More than twelve years have elapsed since the first public release of WEKA. In that time, the software has been rewritten entirely from scratch, evolved substantially and now accompanies a text on data mining [35]. These days, WEKA enjoys widespread acceptance in both academia and business, has an active community, and has been downloaded more than 1.4 million times since being placed on Source-Forge in April 2000. This paper provides an introduction to the WEKA workbench, reviews the history of the project, and, in light of the recent 3.6 stable release, briefly discusses what has been added since the last stable version (Weka 3.4) released in 2003.
Full-text available
We evaluated the effects of a self-monitoring procedure to increase staff on-task behavior and adherence to scheduled activities. Self-monitoring involved the use of activity cards that staff members completed and carried with them to assist in determining the activities for which they were responsible at any given time. Increases in both on-schedule and on-task behavior resulted. Supervisor feedback was subsequently added because some staff members did not maintain consistently high levels of performance. Generalization data indicated that staff members implemented the procedure during evening hours without specific programming. The advantages and limitations of using a self-monitoring procedure for improving performance of staff members in residential settings are discussed.
Conference Paper
Full-text available
This paper addresses the problem of learning and recognizing human activities of daily living (ADL), which is an important research issue in building a pervasive and smart environment. In dealing with ADL, we argue that it is beneficial to exploit both the inherent hierarchical organization of the activities and their typical duration. To this end, we introduce the switching hidden semi-markov model (S-HSMM), a two-layered extension of the hidden semi-Markov model (HSMM) for the modeling task. Activities are modeled in the S-HSMM in two ways: the bottom layer represents atomic activities and their duration using HSMMs; the top layer represents a sequence of high-level activities where each high-level activity is made of a sequence of atomic activities. We consider two methods for modeling duration: the classic explicit duration model using multinomial distribution, and the novel use of the discrete Coxian distribution. In addition, we propose an effective scheme to detect abnormality without the need for training on abnormal data. Experimental results show that the S-HSMM performs better than existing models including the flat HSMM and the hierarchical hidden Markov model in both classification and abnormality detection tasks, alleviating the need for presegmented training data. Furthermore, our discrete Coxian duration model yields better computation time and generalization error than the classic explicit duration model.
Full-text available
We report on a diary study of the activities of information workers aimed at characterizing how people interleave multiple tasks amidst interruptions. The week-long study revealed the type and complexity of activities performed, the nature of the interruptions experienced, and the difficulty of shifting among numerous tasks. We present key findings from the diary study and discuss implications of the findings. Finally, we describe promising directions in the design of software tools for task management, motivated by the findings.
Full-text available
In this paper, we present three taxonomic classification schemes based on Web users responses to what Web activities significantly impacted their decisions and actions. The taxonomic classifications focus on three variables: the Purpose of peoples search on the Web, theMethod people use to find information, and the Content of the information for which they are searching. These taxonomies are useful for understanding peoples activity on the Web and for developing ecologically-valid tasks to be used when studying Web behavior.
Humans are adept at inferring the mental states underlying other agents’ actions, such as goals, beliefs, desires, emotions and other thoughts. We propose a computational framework based on Bayesian inverse planning for modeling human action understanding. The framework represents an intuitive theory of intentional agents’ behavior based on the principle of rationality: the expectation that agents will plan approximately rationally to achieve their goals, given their beliefs about the world. The mental states that caused an agent’s behavior are inferred by inverting this model of rational planning using Bayesian inference, integrating the likelihood of the observed actions with the prior over mental states. This approach formalizes in precise probabilistic terms the essence of previous qualitative approaches to action understanding based on an “intentional stance” [Dennett, D. C. (1987). The intentional stance. Cambridge, MA: MIT Press] or a “teleological stance” [Gergely, G., Nádasdy, Z., Csibra, G., & Biró, S. (1995). Taking the intentional stance at 12 months of age. Cognition, 56, 165–193]. In three psychophysical experiments using animated stimuli of agents moving in simple mazes, we assess how well different inverse planning models based on different goal priors can predict human goal inferences. The results provide quantitative evidence for an approximately rational inference mechanism in human goal inference within our simplified stimulus paradigm, and for the flexible nature of goal representations that human observers can adopt. We discuss the implications of our experimental results for human action understanding in real-world contexts, and suggest how our framework might be extended to capture other kinds of mental state inferences, such as inferences about beliefs, or inferring whether an entity is an intentional agent.
The abstract for this document is available on CSA Illumina.To view the Abstract, click the Abstract button above the document title.