4,494 research outputs found
Attention-Block Deep Learning Based Features Fusion in Wearable Social Sensor for Mental Wellbeing Evaluations
With the progressive increase of stress, anxiety and depression in working and living environment, mental health assessment becomes an important social interaction research topic. Generally, clinicians evaluate the psychology of participants through an effective psychological evaluation and questionnaires. However, these methods suffer from subjectivity and memory effects. In this paper, a new multi- sensing wearable device has been developed and applied in self-designed psychological tests. Speech under different emotions as well as behavior signals are captured and analyzed. The mental state of the participants is objectively assessed through a group of psychological questionnaires. In particular, we propose an attention-based block deep learning architecture within the device for multi-feature classification and fusion analysis. This enables the deep learning architecture to autonomously train to obtain the optimum fusion weights of different domain features. The proposed attention-based architecture has led to improving performance compared with direct connecting fusion method. Experimental studies have been carried out in order to verify the effectiveness and robustness of the proposed architecture. The obtained results have shown that the wearable multi-sensing devices equipped with the attention-based block deep learning architecture can effectively classify mental state with better performance
Sonification of guidance data during road crossing for people with visual impairments or blindness
In the last years several solutions were proposed to support people with
visual impairments or blindness during road crossing. These solutions focus on
computer vision techniques for recognizing pedestrian crosswalks and computing
their relative position from the user. Instead, this contribution addresses a
different problem; the design of an auditory interface that can effectively
guide the user during road crossing. Two original auditory guiding modes based
on data sonification are presented and compared with a guiding mode based on
speech messages.
Experimental evaluation shows that there is no guiding mode that is best
suited for all test subjects. The average time to align and cross is not
significantly different among the three guiding modes, and test subjects
distribute their preferences for the best guiding mode almost uniformly among
the three solutions. From the experiments it also emerges that higher effort is
necessary for decoding the sonified instructions if compared to the speech
instructions, and that test subjects require frequent `hints' (in the form of
speech messages). Despite this, more than 2/3 of test subjects prefer one of
the two guiding modes based on sonification. There are two main reasons for
this: firstly, with speech messages it is harder to hear the sound of the
environment, and secondly sonified messages convey information about the
"quantity" of the expected movement
Affective Medicine: a review of Affective Computing efforts in Medical Informatics
Background: Affective computing (AC) is concerned with emotional interactions performed with and through computers. It is defined as âcomputing that relates to, arises from, or deliberately influences emotionsâ. AC enables investigation and understanding of the relation between human emotions and health as well as application of assistive and useful technologies in the medical domain. Objectives: 1) To review the general state of the art in AC and its applications in medicine, and 2) to establish synergies between the research communities of AC and medical informatics. Methods: Aspects related to the human affective state as a determinant of the human health are discussed, coupled with an illustration of significant AC research and related literature output. Moreover, affective communication channels are described and their range of application fields is explored through illustrative examples. Results: The presented conferences, European research projects and research publications illustrate the recent increase of interest in the AC area by the medical community. Tele-home healthcare, AmI, ubiquitous monitoring, e-learning and virtual communities with emotionally expressive characters for elderly or impaired people are few areas where the potential of AC has been realized and applications have emerged. Conclusions: A number of gaps can potentially be overcome through the synergy of AC and medical informatics. The application of AC technologies parallels the advancement of the existing state of the art and the introduction of new methods. The amount of work and projects reviewed in this paper witness an ambitious and optimistic synergetic future of the affective medicine field
Jefferson Digital Commons quarterly report: January-March 2020
This quarterly report includes: New Look for the Jefferson Digital Commons Articles COVID-19 Working Papers Educational Materials From the Archives Grand Rounds and Lectures JeffMD Scholarly Inquiry Abstracts Journals and Newsletters Master of Public Health Capstones Oral Histories Posters and Conference Presentations What People are Saying About the Jefferson the Digital Common
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
Multimodal Emotion Recognition among Couples from Lab Settings to Daily Life using Smartwatches
Couples generally manage chronic diseases together and the management takes
an emotional toll on both patients and their romantic partners. Consequently,
recognizing the emotions of each partner in daily life could provide an insight
into their emotional well-being in chronic disease management. The emotions of
partners are currently inferred in the lab and daily life using self-reports
which are not practical for continuous emotion assessment or observer reports
which are manual, time-intensive, and costly. Currently, there exists no
comprehensive overview of works on emotion recognition among couples.
Furthermore, approaches for emotion recognition among couples have (1) focused
on English-speaking couples in the U.S., (2) used data collected from the lab,
and (3) performed recognition using observer ratings rather than partner's
self-reported / subjective emotions. In this body of work contained in this
thesis (8 papers - 5 published and 3 currently under review in various
journals), we fill the current literature gap on couples' emotion recognition,
develop emotion recognition systems using 161 hours of data from a total of
1,051 individuals, and make contributions towards taking couples' emotion
recognition from the lab which is the status quo, to daily life. This thesis
contributes toward building automated emotion recognition systems that would
eventually enable partners to monitor their emotions in daily life and enable
the delivery of interventions to improve their emotional well-being.Comment: PhD Thesis, 2022 - ETH Zuric
Quality assessment technique for ubiquitous software and middleware
The new paradigm of computing or information systems is ubiquitous computing systems. The technology-oriented issues of ubiquitous computing systems have made researchers pay much attention to the feasibility study of the technologies rather than building quality assurance indices or guidelines. In this context, measuring quality is the key to developing high-quality ubiquitous computing products. For this reason, various quality models have been defined, adopted and enhanced over the years, for example, the need for one recognised standard quality model (ISO/IEC 9126) is the result of a consensus for a software quality model on three levels: characteristics, sub-characteristics, and metrics. However, it is very much unlikely that this scheme will be directly applicable to ubiquitous computing environments which are considerably different to conventional software, trailing a big concern which is being given to reformulate existing methods, and especially to elaborate new assessment techniques for ubiquitous computing environments. This paper selects appropriate quality characteristics for the ubiquitous computing environment, which can be used as the quality target for both ubiquitous computing product evaluation processes ad development processes. Further, each of the quality characteristics has been expanded with evaluation questions and metrics, in some cases with measures. In addition, this quality model has been applied to the industrial setting of the ubiquitous computing environment. These have revealed that while the approach was sound, there are some parts to be more developed in the future
On the Generalizability of ECG-based Stress Detection Models
Stress is prevalent in many aspects of everyday life including work,
healthcare, and social interactions. Many works have studied handcrafted
features from various bio-signals that are indicators of stress. Recently, deep
learning models have also been proposed to detect stress. Typically, stress
models are trained and validated on the same dataset, often involving one
stressful scenario. However, it is not practical to collect stress data for
every scenario. So, it is crucial to study the generalizability of these models
and determine to what extent they can be used in other scenarios. In this
paper, we explore the generalization capabilities of Electrocardiogram
(ECG)-based deep learning models and models based on handcrafted ECG features,
i.e., Heart Rate Variability (HRV) features. To this end, we train three HRV
models and two deep learning models that use ECG signals as input. We use ECG
signals from two popular stress datasets - WESAD and SWELL-KW - differing in
terms of stressors and recording devices. First, we evaluate the models using
leave-one-subject-out (LOSO) cross-validation using training and validation
samples from the same dataset. Next, we perform a cross-dataset validation of
the models, that is, LOSO models trained on the WESAD dataset are validated
using SWELL-KW samples and vice versa. While deep learning models achieve the
best results on the same dataset, models based on HRV features considerably
outperform them on data from a different dataset. This trend is observed for
all the models on both datasets. Therefore, HRV models are a better choice for
stress recognition in applications that are different from the dataset
scenario. To the best of our knowledge, this is the first work to compare the
cross-dataset generalizability between ECG-based deep learning models and HRV
models.Comment: Published in Proceedings of 2022 21st IEEE International Conference
on Machine Learning and Applications (ICMLA
Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout
Heart-rate estimation is a fundamental feature of modern wearable devices. In
this paper we propose a machine intelligent approach for heart-rate estimation
from electrocardiogram (ECG) data collected using wearable devices. The novelty
of our approach lies in (1) encoding spatio-temporal properties of ECG signals
directly into spike train and using this to excite recurrently connected
spiking neurons in a Liquid State Machine computation model; (2) a novel
learning algorithm; and (3) an intelligently designed unsupervised readout
based on Fuzzy c-Means clustering of spike responses from a subset of neurons
(Liquid states), selected using particle swarm optimization. Our approach
differs from existing works by learning directly from ECG signals (allowing
personalization), without requiring costly data annotations. Additionally, our
approach can be easily implemented on state-of-the-art spiking-based
neuromorphic systems, offering high accuracy, yet significantly low energy
footprint, leading to an extended battery life of wearable devices. We
validated our approach with CARLsim, a GPU accelerated spiking neural network
simulator modeling Izhikevich spiking neurons with Spike Timing Dependent
Plasticity (STDP) and homeostatic scaling. A range of subjects are considered
from in-house clinical trials and public ECG databases. Results show high
accuracy and low energy footprint in heart-rate estimation across subjects with
and without cardiac irregularities, signifying the strong potential of this
approach to be integrated in future wearable devices.Comment: 51 pages, 12 figures, 6 tables, 95 references. Under submission at
Elsevier Neural Network
- âŠ