We describe and evaluate two methods for device pose classification and walking speed estimation that generalize well to new users, compared to previous work. These machine learning based methods are designed for the general case of a person holding a mobile device in an unknown location and require only a single low-cost, low-power sensor: a triaxial accelerometer. We evaluate our methods in straight-path indoor walking experiments as well as in natural indoor walking settings. Experiments with 14 human participants to test user generalization show that our pose classifier correctly selects among four device poses with 94% accuracy compared to 82% for previous work, and our walking speed estimates are within 12-15% (straight/indoor walk) of ground truth compared to 17-22% for previous work. Implementation on a mobile phone demonstrates that both methods can run efficiently online.

Many indoor localization methods are based on the association of 802.11 wireless RF signals from wireless access points (WAPs) with location labels. An “organic” RF positioning system relies on regular users, not dedicated surveyors, to build the map of RF fingerprints to location labels. However, signal variation due to device heterogeneity may degrade localization performance. We analyze the diversity of those signal characteristics pertinent to indoor localization - signal strength and AP detection - as measured by a variety of 802.11 devices. We first analyze signal strength diversity, and show that pairwise linear transformation alone does not solve the problem. We propose kernel estimation with a wide kernel width to reduce the difference in probability estimates. We also investigate diversity in access point detection. We demonstrate that localization performance may degrade significantly when AP detection rate is used as a feature for localization, and correlate the loss of performance to a device dissimilarity measure captured by Kullback-Leibler divergence. Based on this analysis, we show that using only signal strength, without incorporating negative evidence, achieves good localization performance when devices are heterogeneous.

We describe the design, implementation, and evaluation of Molé, a mobile organic localization engine. Unlike previous work on crowd-sourced WiFi positioning, Mole uses a hierarchical name space. By not relying on a map and by being more strict than uninterpreted names for places, Molé aims for a more flexible and scalable point in the design space of localization systems. Molé employs several new techniques, including a new statistical positioning algorithm to differentiate between neighboring places, a motion detector to reduce update lag, and a scalable “cloud”-based fingerprint distribution system. Molé's localization algorithm, called Maximum Overlap (MAO), accounts for temporal variations in a place's fingerprint in a principled manner. It also allows for aggregation of fingerprints from many users and is compact enough for on-device storage. We show through end-to-end experiments in two deployments that MAO is significantly more accurate than state-of-the-art Bayesian-based localizers. We also show that non-experts can use Molé to quickly survey a building, enabling room-grained location-based services for themselves and others.

Most current methods for 802.11-based indoor localization depend on surveys conducted by experts or skilled technicians. Some recent systems have incorporated surveying by users. Structuring localization systems "organically," however, introduces its own set of challenges: conveying uncertainty, determining when user input is actually required, and discounting erroneous and stale data. Through deployment of an organic location system in our nine-story building, which contains nearly 1,400 distinct spaces, we evaluate new algorithms for addressing these challenges. We describe the use of Voronoi regions for conveying uncertainty and reasoning about gaps in coverage, and a clustering method for identifying potentially erroneous user data. Our algorithms facilitate rapid coverage while maintaining positioning accuracy comparable to that achievable with survey-driven indoor deployments.

Monitoring vital signs and locations of certain classes of ambulatory patients can be useful in overcrowded emergency departments and at disaster scenes, both on-site and during transportation. To be useful, such monitoring needs to be portable and low cost, and have minimal adverse impact on emergency personnel, e.g., by not raising an excessive number of alarms. The SMART (Scalable Medical Alert Response Technology) system integrates wireless patient monitoring (ECG, SpO(2)), geo-positioning, signal processing, targeted alerting, and a wireless interface for caregivers. A prototype implementation of SMART was piloted in the waiting area of an emergency department and evaluated with 145 post-triage patients. System deployment aspects were also evaluated during a small-scale disaster-drill exercise.

The process of interpreting heart sounds is restricted by human auditory limitations. Shortcomings such as insensitivity to frequency changes, slow responses to rapidly occurring changes in acoustic signals and an inability to discriminate the presence of soft pathological sounds are the source of inaccuracies and persist even with experience. This restricts both the practice and teaching of auscultation. In this paper we propose and evaluate a suite of presentation tools for computer-assisted auscultation. We explore the use of digital signal processing techniques to slow down heart sounds while preserving frequency content, differential enhancement across frequency scales to amplify pathological disease signatures, visualization of the signal to measure changes in signal energy across time and presentation of a representative prototypical signal for the patient

No abstract available.

Integrating notification with shared memory applications is an interesting problem. This paper looks at implementing notification in a shared memory, object-oriented, distributed transaction environment.

With the advent of mobile computers, new challenges arise for software designers. The paper focuses on disconnected operation: making mobile computers work well on shared data whether the network is available or not. Initially the shared data is cached on the mobile computer. Modifications and additions to this cached data will be reconciled with the shared data when the mobile computer is reconnected to the network. Conflict resolution will be used to reconcile conflicting changes. We examine these issues by adding support for disconnected operation to Thor an object-oriented database.

Argus is a programming language and system developed to support the construction and execution of distributed programs. This paper describes the implementation of Argus, with particular emphasis on the way we implement atomic actions, because this is where Argus differs most from other implemented systems. The paper also discusses the performance of Argus. The cost of actions is quite reasonable, indicating that action systems like Argus are practical.