Abstract
Advancement in smart sensing and computing technologies has provided a dynamic opportunity to develop intelligent systems for human activity monitoring and thus assisted living. Consequently, many researchers have put their efforts into implementing sensor-based activity recognition systems. However, recognizing people’s natural behavior and physical activities with diverse contexts is still a challenging problem because human physical activities are often distracted by changes in their surroundings/environments. Therefore, in addition to physical activity recognition, it is also vital to model and infer the user’s context information to realize human-environment interactions in a better way. Therefore, this research paper proposes a new idea for activity recognition in-the-wild, which entails modeling and identifying detailed human contexts (such as human activities, behavioral environments, and phone states) using portable accelerometer sensors. The proposed scheme offers a detailed/fine-grained representation of natural human activities with contexts, which is crucial for modeling human-environment interactions in context-aware applications/systems effectively. The proposed idea is validated using a series of experiments, and it achieved an average balanced accuracy of 89.43%, which proves its effectiveness.
Highlights
For all participants’ data, we counted the frequency of different context labels that occur in a pair with each of the six selected daily living activities
It can be stated that the boosted decision tree (BDT) classifier performs significantly better than the neural network (NN) classifier in recognizing activity-aware behavioral contexts
As indicated by the results presented and discussed in the previous sections, the performance of the BDT is better for all types of recognition experiments (i.e., primary physical activity recognition (PPAR), behavioral context recognition (BCR), and phone context recognition (PCR))
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The outputs from both stages are aggregated to form a triplet of information, i.e., {primary physical activity, behavioral context, and phone context} In this manner, our proposed ARW scheme offers a multi-label and fine-grained/context-aware representation of human daily living activities in-the-wild. For the AR task in the proposed scheme, six (06) PADLs, including sitting, walking, lying, standing, running, and bicycling, are chosen from the dataset, whereas for context recognition, the fourteen (14) most frequent context labels are selected for identification purpose These labels provide information regarding the phone positions (such as phone on table or phone in bag/hand/pocket) and the participants’ environmental/behavioral aspects (such as participant’s location, social context, and secondary activity) during the primary activity execution in-the-wild.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
