Kasthuri Jayarajah

GruMon: fast and accurate group monitoring for heterogeneous urban spaces

Real-time monitoring of groups and their rich contexts will be a key building block for futuristic, group-aware mobile services. In this paper, we propose GruMon, a fast and accurate group monitoring system for dense and complex urban spaces. GruMon meets the performance criteria of precise group detection at low latencies by overcoming two critical challenges of practical urban spaces, namely (a) the high density of crowds, and (b) the imprecise location information available indoors. Using a host of novel features extracted from commodity smartphone sensors, GruMon can detect over 80% of the groups, with 97% precision, using 10 minutes latency windows, even in venues with limited or no location information. Moreover, in venues where location information is available, GruMon improves the detection latency by up to 20% using semantic information and additional sensors to complement traditional spatio-temporal clustering approaches. We evaluated GruMon on data collected from 258 shopping episodes from 154 real participants, in two large shopping complexes in Korea and Singapore. We also tested GruMon on a large-scale dataset from an international airport (containing ≈37K+ unlabelled location traces per day) and a live deployment at our university, and showed both GruMons potential performance at scale and various scalability challenges for real-world dense environment deployments.

TODMIS: mining communities from trajectories

Existing algorithms for trajectory-based clustering usually rely on simplex representation and a single proximity-related distance (or similarity) measure. Consequently, additional information markers (e.g., social interactions or the semantics of the spatial layout) are usually ignored, leading to the inability to fully discover the communities in the trajectory database. This is especially true for human-generated trajectories, where additional fine-grained markers (e.g., movement velocity at certain locations, or the sequence of semantic spaces visited) can help capture latent relationships between cluster members. To address this limitation, we propose TODMIS: a general framework for Trajectory cOmmunity Discovery using Multiple Information Sources. TODMIS combines additional information with raw trajectory data and creates multiple similarity metrics. In our proposed approach, we first develop a novel approach for computing semantic level similarity by constructing a Markov Random Walk model from the semantically-labeled trajectory data, and then measuring similarity at the distribution level. In addition, we also extract and compute pair-wise similarity measures related to three additional markers, namely trajectory level spatial alignment (proximity), temporal patterns and multi-scale velocity statistics. Finally, after creating a single similarity metric from the weighted combination of these multiple measures, we apply dense sub-graph detection to discover the set of distinct communities. We evaluated TODMIS extensively using traces of (i) student movement data in a campus, (ii) customer trajectories in a shopping mall, and (iii) city-scale taxi movement data. Experimental results demonstrate that TODMIS correctly and efficiently discovers the real grouping behaviors in these diverse settings.

Need accurate user behaviour?: pay attention to groups!

In this paper, we show that characterizing user behaviour from location or smartphone usage traces, without accounting for the interaction of individuals in physical-world groups, can lead to erroneous results. We conducted one of the largest studies in the UbiComp domain thus far, involving indoor location traces of more than 6,000 users, collected over a 4-month period at our university campus, and further studied fine-grained App usage of a subset of 156 Android users. We apply a state-of-the-art group detection algorithm to annotate such location traces with group vs. individual context, and then show that individuals vs. groups exhibit significant differences along three behavioural traits: (1) the mobility pattern, (2) the responsiveness to calls / SMSs and (3) application usage. We show that these significant differences are robust to underlying errors in the group detection technique and that the use of such group context leads to behavioural results that differ from those reported in prior popular work.

Event Detection: Exploiting Socio-Physical Interactions in Physical Spaces

This paper investigates how digital traces of peoples movements and activities in the physical world (e.g., at college campuses and commutes) may be used to detect local, short-lived events in various urban spaces. Past work that use occupancy-related features can only identify high-intensity events (those that cause large-scale disruption in visit patterns). In this paper, we first show how longitudinal traces of the coordinated and group-based movement episodes obtained from individual-level movement data can be used to create a socio-physical network (with edges representing tie strengths among individuals based on their physical world movement & collocation behavior). We then investigate how two additional families of socio-physical features: (i) group-level interactions observed over shorter timescales and (ii) socio-physical network tie-strengths derived over longer timescales, can be used by state-of-the-art anomaly detection methods to detect a much wider set of both high & low intensity events. We utilize two distinct datasets-one capturing coarse-grained SMU campus-wide indoor location data from hundreds of students, and the other capturing commuting behavior by millions of users on Singapores public transport network-to demonstrate the promise of our approaches: the addition of group and socio-physical tie-strength based features increases recall (the percentage of events detected) more than 2-folds (to 0.77 on the SMU campus and to 0.73 at sample MRT stations), compared to pure occupancy-based approaches.

Socio-physical analytics: challenges & opportunities

In this paper, we argue for expanded research into an area called Socio-Physical Analytics, that focuses on combining the behavioral insight gained from mobile-sensing based monitoring of physical behavior with the inter-personal relationships and preferences deduced from online social networks. We highlight some of the research challenges in combining these heterogeneous data sources and then describe some examples of our ongoing work (based on real-world data being collected at SMU) that illustrate two aspects of socio-physical analytics: (a) how additional demographic and online analytics based attributes can potentially provide better insights into the preferences and behaviors of individuals or groups (in terms of movement prediction and understanding of physical vs. online interactions), and (b) how online and physical interactions can help us discover latent characteristics of physical spaces and entities.

Candy crushing your sleep

Growing interest in quantified self has led to the popularity of lifelogging applications. In particular, health and wellness related applications have seen an upsurge with the advent of wearables such as the Fitbit. In this paper, we focus on the quality of sleep that directly impacts the overall wellness of individuals. In particular, in this work, we present a first of its kind study that (1) unobtrusively quantifies the quality of sleep and (2) seeks to identify attributing aspects of our daily lives such as an individuals usage of apps throughout the day and his/her physical environment that may affect sleep. We use real life, in-situ smartphone data from 400+ undergraduate students over an observation period of 15 months, and present our initial observations.

LiveLabs: Building In-Situ Mobile Sensing & Behavioural Experimentation TestBeds

In this paper, we present LiveLabs, a first-of-its-kind testbed that is deployed across a university campus, convention centre, and resort island and collects real-time attributes such as location, group context etc., from hundreds of opt-in participants. These venues, data, and participants are then made available for running rich human-centric behavioural experiments that could test new mobile sensing infrastructure, applications, analytics, or more social-science type hypotheses that influence and then observe actual user behaviour. We share case studies of how researchers from around the world have and are using LiveLabs, and our experiences and lessons learned from building, maintaining, and expanding Live-Labs over the last three years.

Social Signal Processing for Real-Time Situational Understanding: A Vision and Approach

The US Army Research Laboratory (ARL) and the Air Force Research Laboratory (AFRL) have established a collaborative research enterprise referred to as the Situational Understanding Research Institute (SURI). The goal is to develop an information processing framework to help the military obtain real-time situational awareness of physical events by harnessing the combined power of multiple sensing sources to obtain insights about events and their evolution. It is envisioned that one could use such information to predict behaviors of groups, be they local transient groups (e.g., Protests) or widespread, networked groups, and thus enable proactive prevention of nefarious activities. This paper presents a vision of how social media sources can be exploited in the above context to obtain insights about events, groups, and their evolution.

Exploring discriminative features for anomaly detection in public spaces

Context data, collected either from mobile devices or from user-generated social media content, can help identify abnormal behavioural patterns in public spaces (e.g., shopping malls, college campuses or downtown city areas). Spatiotemporal analysis of such data streams provides a compelling new approach towards automatically creating real-time urban situational awareness, especially about events that are unanticipated or that evolve very rapidly. In this work, we use real-life datasets collected via SMUs LiveLabs testbed or via SMUs Palanteer software, to explore various discriminative features (both spatial and temporal - e.g., occupancy volumes, rate of change in topic{specific tweets or probabilistic distribution of group sizes) for such anomaly detection. We show that such feature primitives fit into a future multi-layer sensor fusion framework that can provide valuable insights into mood and activities of crowds in public spaces.

Group analytics and insights for public spaces

Detecting the group context of an individual (i.e., whether an individual is alone or part of a group) in crowded public spaces, such as shopping malls, is an important goal with many practical applications. However, in crowded indoor spaces, understanding the group-dependent movement behavior is a non-trivial problem as: (1) detecting groups is hard as the density ensures that at any location, a large number of people are moving together, (2) location tracking in many real-world venues is either absent or not very accurate, and (3) indoor mobility models that take into account group attributes (such as group size) are rare. In this paper, we first introduce GruMon, a platform for near real-time group monitoring in dense, public spaces, and then demonstrate how the movement & residency properties of individuals are significantly affected when they are in groups.