2016
DOI: 10.3390/s16081264
|View full text |Cite
|
Sign up to set email alerts
|

Human Behavior Analysis by Means of Multimodal Context Mining

Abstract: There is sufficient evidence proving the impact that negative lifestyle choices have on people’s health and wellness. Changing unhealthy behaviours requires raising people’s self-awareness and also providing healthcare experts with a thorough and continuous description of the user’s conduct. Several monitoring techniques have been proposed in the past to track users’ behaviour; however, these approaches are either subjective and prone to misreporting, such as questionnaires, or only focus on a specific compone… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

0
21
0

Year Published

2018
2018
2022
2022

Publication Types

Select...
4
3
2

Relationship

2
7

Authors

Journals

citations
Cited by 48 publications
(21 citation statements)
references
References 35 publications
0
21
0
Order By: Relevance
“…As investigated in Gao and Groves, 30 the feature comprising the total C/N 0 values summed across the satellite signals above 25 dB-Hz was proven to be more useful rather than the single summed C/N 0 values used alone in indoor/outdoor classification for a pedestrian. Therefore, in addition to the two features described above, it is adopted as a third feature for pedestrian-based environment classification, denoted as sumCNR 25 . The similar feature metric optimized for vehicle context will be discussed in Section 5.2.…”
Section: Features For Environment Detectionmentioning
confidence: 99%
“…As investigated in Gao and Groves, 30 the feature comprising the total C/N 0 values summed across the satellite signals above 25 dB-Hz was proven to be more useful rather than the single summed C/N 0 values used alone in indoor/outdoor classification for a pedestrian. Therefore, in addition to the two features described above, it is adopted as a third feature for pedestrian-based environment classification, denoted as sumCNR 25 . The similar feature metric optimized for vehicle context will be discussed in Section 5.2.…”
Section: Features For Environment Detectionmentioning
confidence: 99%
“…Feature concatenation methods that involve the fusion of vision based sensors and inertial sensors have also been proposed using machine learning for human activity detection and health monitoring. The fusion methods enable identification of mobility changes, complex and concurrent activity details and behaviour tracking [43]. However, each sensor modalities provide different statistical properties for recognition of particular activity details and maybe not be optimal to aggregate these features before applying learning algorithms [44].…”
Section: Feature-level Fusionmentioning
confidence: 99%
“…Gaggioli et al [2] presented a platform to gather users' psychological, physiological and activity information for describing mental health. Banos et al [3] proposed a machine-learning-based framework to recognize behaviour elements, such as physical activities, emotions and locations, which are further combined through ontological methods into more abstract representations of user context. Furthermore, there is an increasing number of commercial frameworks, such as Apple HealthKit, GoogleFit and Samsung SAMI, that use different types of behaviour-related contextual information, while they rely on third-party applications and systems for inferring the behaviour information [3].…”
Section: Introductionmentioning
confidence: 99%