2012
DOI: 10.3390/s121115888
|View full text |Cite
|
Sign up to set email alerts
|

A Presence-Based Context-Aware Chronic Stress Recognition System

Abstract: Stressors encountered in daily life may play an important role in personal well-being. Chronic stress can have a serious long-term impact on our physical as well as our psychological health, due to ongoing increased levels of the chemicals released in the ‘fight or flight’ response. The currently available stress assessment methods are usually not suitable for daily chronic stress measurement. The paper presents a context-aware chronic stress recognition system that addresses this problem. The proposed system … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
11
0
1

Year Published

2012
2012
2023
2023

Publication Types

Select...
7
1
1

Relationship

1
8

Authors

Journals

citations
Cited by 28 publications
(16 citation statements)
references
References 31 publications
0
11
0
1
Order By: Relevance
“…A user-context recognition system is presented in [87]. It uses Hidden Markov Models (HMM) to assess users' stress levels.…”
Section: Applicationsmentioning
confidence: 99%
“…A user-context recognition system is presented in [87]. It uses Hidden Markov Models (HMM) to assess users' stress levels.…”
Section: Applicationsmentioning
confidence: 99%
“…Os critérios de comparação são: (i) medidas fisiológicas, que ilustra as medidas usadas para avaliar o SNA; (ii) elementos do contexto, que apresenta quais os dados foram extraídos dos dispositivos, caracterizando as interações com o ambiente; (iii) remoção de ruído, necessário para diferenciar alterações fisiológicas provenientes das atividades físicas daquelas provenientes das psicológicas [Kusserow et al 2013]; (iv) relação entre estresse e localização, que fornece um método de retirar informação contextual significativa em relação ao ambiente. Desta forma, salienta-se que os trabalhos apresentados não combinam efetivamente os doisúltimos critérios, com exceção do trabalho de Peternel et al 2012. Entretanto, os autores não consideraram a flexibilidade de localização proveniente do cotidiano para a relação, possuindo os dados direcionados apenas para a Casa e o Trabalho do indivíduo.…”
Section: Trabalhos Relacionadosunclassified
“…The presented example clearly implies some form of highspeed communication and some form of HPC, especially when complex algorithms and processes are used on them. Algorithms that are regularly performed on streamed sensor signals in biofeedback systems are [25][26][27][28][29][30][31] statistical analysis, temporal signal parameters extraction, correlation, convolution, spectrum analysis, orientation calculation, matrix multiplication, and so forth. Processes include motion tracking, time-frequency analysis, identification, classification, and clustering.…”
Section: The Need For Hpc In Real-time Biofeedbackmentioning
confidence: 99%