Opportunistic sensing for inferring in-the-wild human contexts based on activity pattern recognition using smart computing

In recent years, with the evolution of internet-of-things and smart sensing technologies, sensor-based physical activity recognition has gained substantial prominence, and numerous research works have been conducted in this regard. However, the accurate recognition of in-the-wild human activities an...

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Bibliographic Details
Published in:Future generation computer systems Vol. 106; pp. 374 - 392
Main Authors: Ehatisham-ul-Haq, Muhammad, Azam, Muhammad Awais
Format: Journal Article
Language:English
Published: Elsevier B.V 01.05.2020
Subjects:
Activity recognition
Human context recognition
In-the-wild
Machine learning
Pervasive computing
Smart sensing
ANN
BACC
CNN
PRE
LR
In-the-wild
LDA
SVM
BN
STN
IoT
DT
MLP
DBN
LYD
WLK
GBT
Activity recognition
Smart sensing
KBS
SP
KPCA
K-NN
Pervasive computing
LMA
LSTM
AAHCR
GMM
HMM
IR
RUN
F1
AAL
Human context recognition
HCI
REC
PAMS
RF
CRF
HCR
Machine learning
DALs
SIT
ISSN:0167-739X, 1872-7115
Online Access:Get full text
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Description
Summary:In recent years, with the evolution of internet-of-things and smart sensing technologies, sensor-based physical activity recognition has gained substantial prominence, and numerous research works have been conducted in this regard. However, the accurate recognition of in-the-wild human activities and the associated contexts remains an open research challenge to be addressed. This research work presents a novel activity-aware human context recognition scheme that explicitly learns human activity patterns in diverse behavioral contexts and infers in-the-wild user contexts based on physical activity recognition. In this aspect, five daily living activities, e.g., lying, sitting, standing, walking, and running, are associated with overall fourteen different behavioral contexts, including phone positions. A public domain dataset, i.e., ExtraSensory, is used for evaluating the proposed scheme using a series of machine learning classifiers. Random Forest classifier achieves the best recognition rate of 88.4% and 89.8% in recognizing five physical activities and the associated behavioral contexts, respectively, which demonstrates the efficacy of the proposed method. •Novel scheme for activity-aware human context recognition (AAHCR) in-the-wild.•Integration of 14 diverse behavioral contexts with 05 physical activities for AAHCR.•Fusion of smartphone and watch accelerometer for inferring user activity and context.•Detailed performance analysis of position-independent and position-dependent AAHCR.•Detailed comparative analysis of a series of machine learning classifiers for AAHCR.
ISSN:0167-739X
1872-7115
DOI:10.1016/j.future.2020.01.003