7,633 research outputs found
Modeling Individual Cyclic Variation in Human Behavior
Cycles are fundamental to human health and behavior. However, modeling cycles
in time series data is challenging because in most cases the cycles are not
labeled or directly observed and need to be inferred from multidimensional
measurements taken over time. Here, we present CyHMMs, a cyclic hidden Markov
model method for detecting and modeling cycles in a collection of
multidimensional heterogeneous time series data. In contrast to previous cycle
modeling methods, CyHMMs deal with a number of challenges encountered in
modeling real-world cycles: they can model multivariate data with discrete and
continuous dimensions; they explicitly model and are robust to missing data;
and they can share information across individuals to model variation both
within and between individual time series. Experiments on synthetic and
real-world health-tracking data demonstrate that CyHMMs infer cycle lengths
more accurately than existing methods, with 58% lower error on simulated data
and 63% lower error on real-world data compared to the best-performing
baseline. CyHMMs can also perform functions which baselines cannot: they can
model the progression of individual features/symptoms over the course of the
cycle, identify the most variable features, and cluster individual time series
into groups with distinct characteristics. Applying CyHMMs to two real-world
health-tracking datasets -- of menstrual cycle symptoms and physical activity
tracking data -- yields important insights including which symptoms to expect
at each point during the cycle. We also find that people fall into several
groups with distinct cycle patterns, and that these groups differ along
dimensions not provided to the model. For example, by modeling missing data in
the menstrual cycles dataset, we are able to discover a medically relevant
group of birth control users even though information on birth control is not
given to the model.Comment: Accepted at WWW 201
Implementation of a real-time dance ability for mini maggie
The increasing rise of robotics and the growing interest in some fields
like the human-robot interaction has triggered the birth a new generation of
social robots that develop and expand their abilities. Much late research has
focused on the dance ability, what has caused it to experience a very fast
evolution. Nonetheless, real-time dance ability still remains immature in many
areas such as online beat tracking and dynamic creation of choreographies.
The purpose of this thesis is to teach the robot Mini Maggie how to dance
real-time synchronously with the rhythm of music from the microphone. The
number of joints of our robot Mini Maggie is low and, therefore, our main
objective is not to execute very complex dances since our range of action is
small. However, Mini Maggie should react with a low enough delay since we
want a real-time system. It should resynchronise as well if the song changes or
there is a sudden tempo change in the same song.
To achieve that, Mini Maggie has two subsystems: a beat tracking
subsystem, which tell us the time instants of detected beats and a dance
subsystem, which makes Mini dance at those time instants. In the beat tracking
system, first, the input microphone signal is processed in order to extract the
onset strength at each time instant, which is directly related to the beat
probability at that time instant. Then, the onset strength signal will be delivered
to two blocks. The music period estimator block will extract the periodicities of
the onset strength signal by computing the 4-cycled autocorrelation, a type of
autocorrelation in which we do not only compute the similarity of a signal by a
displacement of one single period but also of its first 4 multiples. Finally, the
beat tracker takes the onset strength signal and the estimated periods real-time
and decides at which time instants there should be a beat. The dance
subsystem will then execute different dance steps according to several prestored
choreographies thanks to Mini Maggie’s dynamixel module, which is in
charge of more low-level management of each joint.
With this system we have taught Mini Maggie to dance for a general set
of music genres with enough reliability. Reliability of this system generally
remains stable among different music styles but if there is a clear lack of minimal stability in rhythm, as it happens in very expressive and subjectively
interpreted classical music, our system is not able to track its beats. Mini
Maggie’s dancing was adjusted so that it was appealing even though there was
a very limited range of possible movements, due to the lack of degrees of
freedom.IngenierĂa de Sistemas Audiovisuale
Care-Chair: Opportunistic health assessment with smart sensing on chair backrest
A vast majority of the population spend most of their time in a sedentary position, which potentially makes a chair a huge source of information about a person\u27s daily activity. This information, which often gets ignored, can reveal important health data but the overhead and the time consumption needed to track the daily activity of a person is a major hurdle. Considering this, a simple and cost-efficient sensory system, named Care-Chair, with four square force sensitive resistors on the backrest of a chair has been designed to collect the activity details and breathing rate of the users. The Care-Chair system is considered as an opportunistic environmental sensor that can track each and every activity of its occupant without any human intervention. It is specifically designed and tested for elderly people and people with sedentary job. The system was tested using 5 users data for the sedentary activity classification and it successfully classified 18 activities in laboratory environment with 86% accuracy. In an another experiment of breathing rate detection with 19 users data, the Care-Chair produced precise results with slight variance with ground truth breathing rate. The Care-Chair yields contextually good results when tested in uncontrolled environment with single user data collected during long hours of study. --Abstract, page iii
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