Hierarchical syntactic models for human activity recognition through mobility traces

Abstract

Recognizing users’ daily life activities without disrupting their lifestyle is a key functionality to enable a broad variety of advanced services for a Smart City, from energy-efficient management of urban spaces to mobility optimization. In this paper, we propose a novel method for human activity recognition from a collection of outdoor mobility traces acquired through wearable devices. Our method exploits the regularities naturally present in human mobility patterns to construct syntactic models in the form of finite state automata, thanks to an approach known as grammatical inference. We also introduce a measure of similarity that accounts for the intrinsic hierarchical nature of such models, and allows to identify the common traits in the paths induced by different activities at various granularity levels. Our method has been validated on a dataset of real traces representing movements of users in a large metropolitan area. The experimental results show the effectiveness of our similarity measure to correctly identify a set of common coarse-grained activities, as well as their refinement at a finer level of granularity.

Development of Modify Random Waypoint Mobility Model of Routing Protocol for the Mobile Ad-Hoc Network

“…It broadly classified into two categories: trace and syntactic. Real-life mobility pattern of the node created in trace-based mobility model [19]. But to develop such mobility model and generate mobility trace become the major challenges for long observation time and the large mobile ad hoc network [20].…”

Special issue on knowledge discovery and user modeling for smart cities

“…Casella et al [2] present an approach to recognize human activities from mobility traces acquired through wearable devices, such as GPS loggers and smartphones. Their approach relies on grammatical inference to construct syntactic models in the form of finite state automata.…”

“…The second group of papers focuses on how the mobility data gathered by individual citizens can be used to improve the quality of life of these citizens. Among the novelties proposed by this group of papers, we find a method to manage tourist crowds based on real-time traffic and booking information [1], as well as methods to identify social activities in shopping malls [5] and in a larger metropolitan area [2,8].…”

A multi‐agent system for itinerary suggestion in smart environments

“…In this context, Recommender Systems (RSs) play a fundamental role in maximising users' satisfaction in situations where multiple constraints may also be subject to real-time factors [2]. In particular, smart environments, where measurements gathered by a pervasive sensory infrastructure [3] are used to provide intelligent services to users [4], represent a significant scenario - 1 which may benefit from properly designed trajectory RSs. Smart museums, for instance, are characterised by customers wishing to spend their visit time for their preferred masterpieces, and conveniently postpone the contemplation of currently over-crowded artworks.…”