1,159 research outputs found
Evaluating Mental Stress Among College Students Using Heart Rate and Hand Acceleration Data Collected from Wearable Sensors
Stress is various mental health disorders including depression and anxiety
among college students. Early stress diagnosis and intervention may lower the
risk of developing mental illnesses. We examined a machine learning-based
method for identification of stress using data collected in a naturalistic
study utilizing self-reported stress as ground truth as well as physiological
data such as heart rate and hand acceleration. The study involved 54 college
students from a large campus who used wearable wrist-worn sensors and a mobile
health (mHealth) application continuously for 40 days. The app gathered
physiological data including heart rate and hand acceleration at one hertz
frequency. The application also enabled users to self-report stress by tapping
on the watch face, resulting in a time-stamped record of the self-reported
stress. We created, evaluated, and analyzed machine learning algorithms for
identifying stress episodes among college students using heart rate and
accelerometer data. The XGBoost method was the most reliable model with an AUC
of 0.64 and an accuracy of 84.5%. The standard deviation of hand acceleration,
standard deviation of heart rate, and the minimum heart rate were the most
important features for stress detection. This evidence may support the efficacy
of identifying patterns in physiological reaction to stress using smartwatch
sensors and may inform the design of future tools for real-time detection of
stress
Sensor-AssistedWeighted Average Ensemble Model for Detecting Major Depressive Disorder
The present methods of diagnosing depression are entirely dependent on self-report
ratings or clinical interviews. Those traditional methods are subjective, where the individual may
or may not be answering genuinely to questions. In this paper, the data has been collected using
self-report ratings and also using electronic smartwatches. This study aims to develop a weighted
average ensemble machine learning model to predict major depressive disorder (MDD) with superior
accuracy. The data has been pre-processed and the essential features have been selected using a
correlation-based feature selection method. With the selected features, machine learning approaches
such as Logistic Regression, Random Forest, and the proposedWeighted Average Ensemble Model are
applied. Further, for assessing the performance of the proposed model, the Area under the Receiver
Optimization Characteristic Curves has been used. The results demonstrate that the proposed
Weighted Average Ensemble model performs with better accuracy than the Logistic Regression and
the Random Forest approaches
Di\v{s}imo: Anchoring Our Breath
We present a system that raises awareness about users' inner state.
Di\v{s}imo is a multimodal ambient display that provides feedback about one's
stress level, which is assessed through heart rate monitoring. Upon detecting a
low heart rate variability for a prolonged period of time, Di\v{s}imo plays an
audio track, setting the pace of a regular and deep breathing. Users can then
choose to take a moment to focus on their breath. By doing so, they will
activate the Di\v{s}imo devices belonging to their close ones, who can then
join for a shared relaxation session
Emotional self-regulation of individuals with autism spectrum disorders: smartwatches for monitoring and interaction
In this paper, we analyze the needs of individuals with Autism Spectrum Disorders (ASD)
to have a pervasive, feasible and non-stigmatizing form of assistance in their emotional self-regulation,
in order to ease certain behavioral issues that undermine their mental health throughout their life.
We argue the potential of recent widespread wearables, and more specifically smartwatches, to achieve
this goal. Then, a smartwatch system that implements a wide range of self-regulation strategies
and infers outburst patterns from physiological signals and movement is presented, along with an
authoring tool for smartphones that is to be used by caregivers or family members to create and edit
these strategies, in an adaptive way. We conducted an intensive experiment with two individuals
with ASD who showed varied, representative behavioral responses to their emotional dysregulation.
Both users were able to employ effective, customized emotional self-regulation strategies by means
of the system, recovering from the majority of mild stress episodes and temper tantrums experienced
in the nine days of experiment in their classroomThis work has been partially funded by the projects “e-Training y e-Coaching para la
integración socio—laboral” (TIN2013-44586-R) and “eMadrid-CM: Investigación y Desarrollo de Tecnologías Educativas en la Comunidad de Madrid” (S2013/ICE-2715). It has been also funded by Fundación Orange during the early stages of the project “Tic-Tac-TEA: Sistema de asistencia para la autorregulación emocional en momentos
de crisis para personas con TEA mediante smartwatches
A conceptual approach to enhance the well-being of elderly people
The number of elderly people living alone is increasing. Consequently, a lot of research works have been addressing this issue in order to propose solutions that can enhance the quality of life of elderly people. Most of them have been concerned in dealing with objective issues such as forgetfulness or detecting falls. In this paper, we propose a conceptual approach of a system that intends to enhance the daily sense of user’s well-being. For that, our proposal consists in a system that works as a social network and a smartwatch application that works unobtrusively and collects the user’s physiological data. In addition, we debate how important features such as to detect user’s affective states and to potentiate user’s memory could be implemented. Our study shows that there are still some important limitations which affect the success of applications built in the context of elderly care and which are mostly related with accuracy and usability of this kind of system. However, we believe that with our approach we will be able to address some of those limitations and define a system that can enhance the well-being of elderly people and improve their cognitive capabilities.The work presented in this paper has been developed under the EUREKA - ITEA3 Project PHE (PHE-16040), and by National Funds through FCT (Fundação para a Ciência e a Tecnologia) under the projects UID/EEA/00760/2019 and UID/CEC/00319/2019 and by NORTE-01-0247-FEDER-033275 (AIRDOC - “Aplicação móvel Inteligente para suporte individualizado e monitorização da função e sons Respiratórios de Doentes Obstrutivos Crónicos ”) by NORTE 2020 (Programa Operacional Regional do Norte)
Machine Learning Models for Mental Stress Classification based on Multimodal Biosignal Input
Mental stress is a largely prevalent condition directly or indirectly responsible for
almost half of all work-related diseases. Work-Related Stress is the second most impactful
occupational health problem in Europe, behind musculoskeletal diseases. When mental
health is adequately handled, a worker’s well-being, performance, and productivity can
be considerably improved.
This thesis presents machine learning models to classify mental stress experienced by
computer users using physiological signals including heart rate, acquired using a smart-
watch; respiration, derived from a smartphone’s acc placed on the chest; and trapezius
electromyography, using proprietary electromyography sensors. Two interactive proto-
cols were implemented to collect data from 12 individuals. Time and frequency domain
features were extracted from the heart rate and electromyography signals, and statistical
and temporal features were extracted from the derived respiration signal.
Three algorithms: Support Vector Machine, Random Forest, and K-Nearest-Neighbor
were employed for mental stress classification. Different input modalities were tested
for the machine learning models: one for each physiological signal and a multimodal
one, combining all of them. Random Forest obtained the best mean accuracy (98.5%) for
the respiration model whereas K-Nearest-Neighbor attained higher mean accuracies for
the heart rate (89.0%) left, right and total electromyography (98.9%, 99.2%, and 99.3%)
models. KNN algorithm was also able to achieve 100% mean accuracy for the multimodal
model. A possible future approach would be to validate these models in real-time.O stress mental é uma condição amplamente prevalente direta ou indiretamente
responsável por quase metade de todas doenças relacionadas com trabalho. O stress expe-
rienciado no trabalho é o segundo problema de saúde ocupacional com maior impacto na
Europa, depois das doenças músculo-esqueléticas. Quando a saúde mental é adequada-
mente cuidada, o bem-estar, o desempenho e a produtividade de um trabalhador podem
ser consideravelmente melhorados.
Esta tese apresenta modelos de aprendizagem automática que classificam o stress
mental experienciado por utilizadores de computadores recorrendo a sinais fisiológi-
cos, incluindo a frequência cardíaca, adquirida pelo sensor de fotopletismografia de um
smartwatch; a respiração, derivada de um acelerómetro incorporado no smartphone po-
sicionado no peito; e electromiografia de cada um dos músculos trapézios, utilizando
sensores electromiográficos proprietários. Foram implementados dois protocolos inte-
ractivos para recolha de dados de 12 indivíduos. Características do domínio temporal
e de frequência foram extraídas dos sinais de frequência cardíaca e electromiografia, e
características estatísticas e temporais foram extraídas do sinal respiratório.
Três algoritmos entitulados K-Nearest-Neighbor, Random Forest, e Support Vector
Machine foram utilizados para a classificação do stress mental. Foram testadas diferentes
modalidades de dados para os modelos de aprendizagem automática: uma para cada sinal
fisiológico e uma multimodal, combinando os três. O Random Forest obteve a melhor
precisão média (98,5%) para o modelo de respiração enquanto que o K-Nearest-Neighbor
atingiu uma maior precisão média nos modelos de frequência cardíaca (89,0%) e electro-
miografia esquerda, direita e total (98,9%, 99,2%, e 99,3%). O algoritmo KNN conseguiu
ainda atingir uma precisão média de 100% para o modelo multimodal. Uma possível
abordagem futura seria efetuar uma validação destes modelos em tempo real
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
Development of a digital biomarker and intervention for subclinical depression: study protocol for a longitudinal waitlist control study
Background
Depression remains a global health problem, with its prevalence rising worldwide. Digital biomarkers are increasingly investigated to initiate and tailor scalable interventions targeting depression. Due to the steady influx of new cases, focusing on treatment alone will not suffice; academics and practitioners need to focus on the prevention of depression (i.e., addressing subclinical depression).
Aim
With our study, we aim to (i) develop digital biomarkers for subclinical symptoms of depression, (ii) develop digital biomarkers for severity of subclinical depression, and (iii) investigate the efficacy of a digital intervention in reducing symptoms and severity of subclinical depression.
Method
Participants will interact with the digital intervention BEDDA consisting of a scripted conversational agent, the slow-paced breathing training Breeze, and actionable advice for different symptoms. The intervention comprises 30 daily interactions to be completed in less than 45 days. We will collect self-reports regarding mood, agitation, anhedonia (proximal outcomes; first objective), self-reports regarding depression severity (primary distal outcome; second and third objective), anxiety severity (secondary distal outcome; second and third objective), stress (secondary distal outcome; second and third objective), voice, and breathing. A subsample of 25% of the participants will use smartwatches to record physiological data (e.g., heart-rate, heart-rate variability), which will be used in the analyses for all three objectives.
Discussion
Digital voice- and breathing-based biomarkers may improve diagnosis, prevention, and care by enabling an unobtrusive and either complementary or alternative assessment to self-reports. Furthermore, our results may advance our understanding of underlying psychophysiological changes in subclinical depression. Our study also provides further evidence regarding the efficacy of standalone digital health interventions to prevent depression.
Trial registration Ethics approval was provided by the Ethics Commission of ETH Zurich (EK-2022-N-31) and the study was registered in the ISRCTN registry (Reference number: ISRCTN38841716, Submission date: 20/08/2022)
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