136,870 research outputs found
Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits
Research has proven that stress reduces quality of life and causes many
diseases. For this reason, several researchers devised stress detection systems
based on physiological parameters. However, these systems require that
obtrusive sensors are continuously carried by the user. In our paper, we
propose an alternative approach providing evidence that daily stress can be
reliably recognized based on behavioral metrics, derived from the user's mobile
phone activity and from additional indicators, such as the weather conditions
(data pertaining to transitory properties of the environment) and the
personality traits (data concerning permanent dispositions of individuals). Our
multifactorial statistical model, which is person-independent, obtains the
accuracy score of 72.28% for a 2-class daily stress recognition problem. The
model is efficient to implement for most of multimedia applications due to
highly reduced low-dimensional feature space (32d). Moreover, we identify and
discuss the indicators which have strong predictive power.Comment: ACM Multimedia 2014, November 3-7, 2014, Orlando, Florida, US
Pain Level Detection From Facial Image Captured by Smartphone
Accurate symptom of cancer patient in regular basis is highly concern to the medical service provider for clinical decision making such as adjustment of medication. Since patients have limitations to provide self-reported symptoms, we have investigated how mobile phone application can play the vital role to help the patients in this case. We have used facial images captured by smart phone to detect pain level accurately. In this pain detection process, existing algorithms and infrastructure are used for cancer patients to make cost low and user-friendly. The pain management solution is the first mobile-based study as far as we found today. The proposed algorithm has been used to classify faces, which is represented as a weighted combination of Eigenfaces. Here, angular distance, and support vector machines (SVMs) are used for the classification system. In this study, longitudinal data was collected for six months in Bangladesh. Again, cross-sectional pain images were collected from three different countries: Bangladesh, Nepal and the United States. In this study, we found that personalized model for pain assessment performs better for automatic pain assessment. We also got that the training set should contain varying levels of pain in each group: low, medium and high
Mining user activity as a context source for search and retrieval
Nowadays in information retrieval it is generally accepted that if we can better
understand the context of users then this could help the search process, either at indexing time by including more metadata or at retrieval time by better modelling the user context. In this work we explore how activity recognition from tri-axial accelerometers can be employed to model a user's activity as a means of enabling context-aware information retrieval. In this paper we discuss how we can gather user activity automatically as a context source from a wearable mobile device and we evaluate the accuracy of our proposed user activity recognition algorithm. Our technique can recognise four kinds of activities which can be used to model part of an individual's current context. We discuss promising experimental results, possible approaches to improve our algorithms, and the impact of this work in modelling user context toward enhanced search and retrieval
MobiBits: Multimodal Mobile Biometric Database
This paper presents a novel database comprising representations of five
different biometric characteristics, collected in a mobile, unconstrained or
semi-constrained setting with three different mobile devices, including
characteristics previously unavailable in existing datasets, namely hand
images, thermal hand images, and thermal face images, all acquired with a
mobile, off-the-shelf device. In addition to this collection of data we perform
an extensive set of experiments providing insight on benchmark recognition
performance that can be achieved with these data, carried out with existing
commercial and academic biometric solutions. This is the first known to us
mobile biometric database introducing samples of biometric traits such as
thermal hand images and thermal face images. We hope that this contribution
will make a valuable addition to the already existing databases and enable new
experiments and studies in the field of mobile authentication. The MobiBits
database is made publicly available to the research community at no cost for
non-commercial purposes.Comment: Submitted for the BIOSIG2018 conference on June 18, 2018. Accepted
for publication on July 20, 201
Real-time food intake classification and energy expenditure estimation on a mobile device
© 2015 IEEE.Assessment of food intake has a wide range of applications in public health and life-style related chronic disease management. In this paper, we propose a real-time food recognition platform combined with daily activity and energy expenditure estimation. In the proposed method, food recognition is based on hierarchical classification using multiple visual cues, supported by efficient software implementation suitable for realtime mobile device execution. A Fischer Vector representation together with a set of linear classifiers are used to categorize food intake. Daily energy expenditure estimation is achieved by using the built-in inertial motion sensors of the mobile device. The performance of the vision-based food recognition algorithm is compared to the current state-of-the-art, showing improved accuracy and high computational efficiency suitable for realtime feedback. Detailed user studies have also been performed to demonstrate the practical value of the software environment
Deep Room Recognition Using Inaudible Echos
Recent years have seen the increasing need of location awareness by mobile
applications. This paper presents a room-level indoor localization approach
based on the measured room's echos in response to a two-millisecond single-tone
inaudible chirp emitted by a smartphone's loudspeaker. Different from other
acoustics-based room recognition systems that record full-spectrum audio for up
to ten seconds, our approach records audio in a narrow inaudible band for 0.1
seconds only to preserve the user's privacy. However, the short-time and
narrowband audio signal carries limited information about the room's
characteristics, presenting challenges to accurate room recognition. This paper
applies deep learning to effectively capture the subtle fingerprints in the
rooms' acoustic responses. Our extensive experiments show that a two-layer
convolutional neural network fed with the spectrogram of the inaudible echos
achieve the best performance, compared with alternative designs using other raw
data formats and deep models. Based on this result, we design a RoomRecognize
cloud service and its mobile client library that enable the mobile application
developers to readily implement the room recognition functionality without
resorting to any existing infrastructures and add-on hardware.
Extensive evaluation shows that RoomRecognize achieves 99.7%, 97.7%, 99%, and
89% accuracy in differentiating 22 and 50 residential/office rooms, 19 spots in
a quiet museum, and 15 spots in a crowded museum, respectively. Compared with
the state-of-the-art approaches based on support vector machine, RoomRecognize
significantly improves the Pareto frontier of recognition accuracy versus
robustness against interfering sounds (e.g., ambient music).Comment: 29 page
Wearable Sensor Data Based Human Activity Recognition using Machine Learning: A new approach
Recent years have witnessed the rapid development of human activity
recognition (HAR) based on wearable sensor data. One can find many practical
applications in this area, especially in the field of health care. Many machine
learning algorithms such as Decision Trees, Support Vector Machine, Naive
Bayes, K-Nearest Neighbor, and Multilayer Perceptron are successfully used in
HAR. Although these methods are fast and easy for implementation, they still
have some limitations due to poor performance in a number of situations. In
this paper, we propose a novel method based on the ensemble learning to boost
the performance of these machine learning methods for HAR
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