Mining Human Location-Routines using a Multi-Level Topic Model

AbstractIn this work we address the problem of modelingvarying time duration sequences for large-scale human routinediscovery from cellphone sensor data using a multi-level approachto probabilistic topic models. We use an unsupervised learningapproach that discovers human routines of varying durationsranging from half-hourly to several hours. Our methodology canhandle large sequence lengths based on a principled procedure todeal with potentially large routine-vocabulary sizes, and can beapplied to rather naive initial vocabularies to discover meaningfullocation-routines. We successfully apply the model to a large,real-life dataset, consisting of97cellphone users and16monthsof their location patterns, to discover routines with varying timedurations. I. I NTRODUCTION Recently, large datasets on human behaviour are beingcollected via mobile phones, potentially providing many in-sights on large-scale human communications, movements, andinteractions. Reality Mining is a name coined for this datatype [7]. The interpretation of such data, collected over hun-dreds, sometimes hundreds of thousands of users over manymonths, represents new challenges in ubiquitous computingdue to the nature of the data and depending on the particularresearch task at hand. In this work, we consider the taskof identifying human routines over time considering varyingtime durations in an unsupervised fashion. There are severaldifc ulties to this problem including various types of noise,lack of ground truth, and complexity due to the size of thedata, the multiple types of data, and the various types ofphone users. A fundamental issue in human activity modelingis that we often do not know (or cannot pre-specify) thebasic units of time for the activities in question. We do knowthat human routines have multiple timescales, however theeffective modeling of many unknown time-durations is an openproblem. Previous works always assume a x ed and preden edunit of time, limiting the timescale of routines discovered. Inthis work we propose a method to discover human routinesconsidering varying time durations, built on probabilistic topicmodels, and demonstrate its feasibility on the Reality Miningdata [7]. In several previous works addressing human activity mod-eling, a bag of words approach is used for activity discoveryfrom video, wearable devices, or mobile phones [8], [12], [14]. The advantages of a bag approach are the robustness to noise,and the compact representation. The disadvantage with mostofthese works is that the words are not simple and time durationmust be preden ed by hand [8], [12] or a supervised method isused requiring a training phase [14]. Furthermore,previouslyunknowntimescales, whether single or multiple, are not con-sidered. Effective human activity modeling has a diverse rangeof applications, ranging from epidemiology to psychology.Human activities in terms of movement and interactions mayprovide insight to the spread of disease [11], urban planning,and human behaviour understanding such as what inu enceshuman opinion change [13], weight change [3], and the spreadof happiness [9]. In this work we propose an elegant approach to constructvocabularies of increasingly largen-grams, built on a multi-level topic model. The number of consecutive words (or sizeof n in the n-gram) increases with each level of the topicmodel. Our approach is built on the probabilistic topic model,Latent Dirichlet Allocation (LDA) [2]. The maximum numberof levels determines the maximumn-gram size considered,and large n-gram sizes can easily be considered with thismethod. At a given leveln, the vocabulary consists of onlyn-grams. The output of the topic model at leveln is usedto construct the vocabulary for leveln + 1. There are manyadvantages to our technique. Firstly, the vocabulary growthis controlled and does not explode since we only considera subset of words for concatenation with the vocabulary informing groups of consecutive words as opposed to the naiven-gram approach which grows exponentially with the size ofthe vocabulary. Secondly, not only do we consider the mostfrequently occurring words, but we can capture frequently co-occurring words which is critical inn-gram discovery. Forexample, if2words co-occur alot, they are more likely to forma bigram than if each occurs very frequently, but not together.Further, the initial vocabulary can be very simple, not requiringmuch hand-craft. Also, each individual level results in uniquepatterns with routines found for the particular vocabularyatthat level. The contribution of this work is a unique approachto identify varying length human location routines and thesuccessful application of this idea to a large-scale, real-lifehuman dataset of over10118days and242800hours of data.