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We deployed 72 sensors of 10 modalities in 15 wireless and wired networked sensor systems in the environment, in objects, and on the body to create a sensor-rich environment for the machine recognition of human activities. We acquired data from 12 subjects performing morning activities, yielding over 25 hours of sensor data. We report the number of activity(More)
In this work we investigate unsupervised activity discovery approaches using three topic model (TM) approaches, based on Latent Dirichlet Allocation (LDA), ngram TM (NTM), and correlated TM (CTM). While LDA structures activity primitives, NTM adds primitive sequence information, and CTM exploits co-occurring topics. We use an activity composite/primitive(More)
We present RoomSense, a new method for indoor positioning using smartphones on two resolution levels: rooms and within-rooms positions. Our technique is based on active sound fingerprinting and needs no infrastructure. Rooms and within-rooms positions are characterized by impulse response measurements. Using acoustic features of the impulse response and(More)
This paper presents design, implementation, and evaluation of AmbientSense, a real-time ambient sound recognition system on a smartphone. AmbientSense continuously recognizes user context by analyzing ambient sounds sampled from a smartphone's microphone. The phone provides a user with realtime feedback on recognised context. AmbientSense is implemented as(More)
Publicly available data sets are increasingly becoming an important research tool in context recognition. However, due to the diversity and complexity of the domain it is difficult to provide standard recordings that cover the majority of possible applications and research questions. In this paper we describe a novel data set hat combines a number of(More)
In large-scale, complex domains such as space defense and security systems, situation assessment and decision making are evolving from centralized models to high-level, net-centric models. In this context, collaboration among the many actors involved in the situation assessment process is critical in order to achieve a prompt reaction as needed in the(More)
Upcoming ambient intelligence environments will boast ever larger number of sensor nodes readily available on body, in objects, and in the user’s surroundings. We envision “Pervasive Apps”, user-centric activity-aware pervasive computing applications. They use available sensors for activity recognition. They are downloadable from application repositories,(More)
The growing ubiquity of sensors in mobile phones has opened many opportunities for personal daily activity sensing. Most context recognition systems require a cumbersome preparation by collecting and manually annotating training examples. Recently, mining online crowd-generated repositories for free annotated training data has been proposed to build context(More)
Millions of people run. Movement scientists investigate the relationship of running kinematics to fatigue, injury, or running economy mainly using optical motion capture. It was found that running kinematics are highly individual and often cannot be summarized by single variables. We thus present a data-driven analysis of running technique using wearable(More)