488,321 research outputs found
Fine-grained Human Activity Recognition Using Virtual On-body Acceleration Data
Previous work has demonstrated that virtual accelerometry data, extracted
from videos using cross-modality transfer approaches like IMUTube, is
beneficial for training complex and effective human activity recognition (HAR)
models. Systems like IMUTube were originally designed to cover activities that
are based on substantial body (part) movements. Yet, life is complex, and a
range of activities of daily living is based on only rather subtle movements,
which bears the question to what extent systems like IMUTube are of value also
for fine-grained HAR, i.e., When does IMUTube break? In this work we first
introduce a measure to quantitatively assess the subtlety of human movements
that are underlying activities of interest--the motion subtlety index
(MSI)--which captures local pixel movements and pose changes in the vicinity of
target virtual sensor locations, and correlate it to the eventual activity
recognition accuracy. We then perform a "stress-test" on IMUTube and explore
for which activities with underlying subtle movements a cross-modality transfer
approach works, and for which not. As such, the work presented in this paper
allows us to map out the landscape for IMUTube applications in practical
scenarios
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
Classification of stress based on speech features
Contemporary life is filled with challenges, hassles, deadlines, disappointments, and endless demands. The consequent of which might be stress. Stress has become a global
phenomenon that is been experienced in our modern daily lives. Stress might play a
significant role in psychological and/or behavioural disorders like anxiety or
depression. Hence early detection of the signs and symptoms of stress is an antidote towards reducing its harmful effects and high cost of stress management efforts. This research work thereby presented Automatic Speech Recognition (ASR) technique to stress detection as a better alternative to other approaches such as chemical analysis, skin conductance, electrocardiograms that are obtrusive, intrusive, and also costly. Two set of voice data was recorded from ten Arabs students at Universiti Utara Malaysia (UUM) in neural and stressed mode. Speech features of fundamental, frequency (f0); formants (F1, F2, and F3), energy and Mel-Frequency Cepstral Coefficients (MFCC) were extracted and classified by K-nearest neighbour, Linear Discriminant Analysis and Artificial Neural Network. Result from average value of fundamental frequency
reveals that stress is highly correlated with increase in fundamental frequency value. Of
the three classifiers, K-nearest neighbor (KNN) performance is best followed by linear
discriminant analysis (LDA) while artificial neural network (ANN) shows the least performance. Stress level classification into low, medium and high was done based of the classification result of KNN. This research shows the viability of ASR as better means of stress detection and classification
Tracking daily fatigue fluctuations in multiple sclerosis : ecological momentary assessment provides unique insights
The preparation of this manuscript was supported by a UK Economic and Social Research Council (ESRC) PhD studentship (ES/1026266/1) awarded to DP. The study was funded by the Psychology Unit at the University of Southampton. The authors declare that they have no conflict of interest. The authors thank all participants of this study. Open access via Springer Compact Agreement.Peer reviewedPublisher PD
Emotions in context: examining pervasive affective sensing systems, applications, and analyses
Pervasive sensing has opened up new opportunities for measuring our feelings and understanding our behavior by monitoring our affective states while mobile. This review paper surveys pervasive affect sensing by examining and considering three major elements of affective pervasive systems, namely; âsensingâ, âanalysisâ, and âapplicationâ. Sensing investigates the different sensing modalities that are used in existing real-time affective applications, Analysis explores different approaches to emotion recognition and visualization based on different types of collected data, and Application investigates different leading areas of affective applications. For each of the three aspects, the paper includes an extensive survey of the literature and finally outlines some of challenges and future research opportunities of affective sensing in the context of pervasive computing
An Empirical Study Comparing Unobtrusive Physiological Sensors for Stress Detection in Computer Work.
Several unobtrusive sensors have been tested in studies to capture physiological reactions to stress in workplace settings. Lab studies tend to focus on assessing sensors during a specific computer task, while in situ studies tend to offer a generalized view of sensors' efficacy for workplace stress monitoring, without discriminating different tasks. Given the variation in workplace computer activities, this study investigates the efficacy of unobtrusive sensors for stress measurement across a variety of tasks. We present a comparison of five physiological measurements obtained in a lab experiment, where participants completed six different computer tasks, while we measured their stress levels using a chest-band (ECG, respiration), a wristband (PPG and EDA), and an emerging thermal imaging method (perinasal perspiration). We found that thermal imaging can detect increased stress for most participants across all tasks, while wrist and chest sensors were less generalizable across tasks and participants. We summarize the costs and benefits of each sensor stream, and show how some computer use scenarios present usability and reliability challenges for stress monitoring with certain physiological sensors. We provide recommendations for researchers and system builders for measuring stress with physiological sensors during workplace computer use
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Tensions of Data-Driven Reflection: A Case Study of Real-Time Emotional Biosensing
Biosensing displays, increasingly enrolled in emotional reflection, promise authoritative insight by presenting usersâ emotions as discrete categories. Rather than machines interpreting emotions, we sought to explore an alternative with emotional biosensing displays in which users formed their own interpretations and felt comfortable critiquing the display. So, we designed, implemented, and deployed, as a technology probe, an emotional biosensory display: Ripple is a shirt whose pattern changes color responding to the wearerâs skin conductance, which is associated with excitement. 17 participants wore Ripple over 2 days of daily life. While some participants appreciated the âphysical connectionâ Ripple provided between body and emotion, for others Ripple fostered insecurities about âhow muchâ feeling they had. Despite our design intentions, we found participants rarely questioned the displayâs relation to their feelings. Using biopolitics to speculate on Rippleâs surprising authority, we highlight ethical stakes of biosensory representations for sense of self and ways of feeling
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