Mobile devices have the potential to continuously monitor health by collecting movement data including walking speed during natural walking. Natural walking is walking without artificial speed constraints present in both treadmill and nurse-assisted walking. Fitness trackers have become popular which record steps taken and distance, typically using a fixed stride length. While useful for everyday purposes, medical monitoring requires precise accuracy and testing on real patients with a scientifically valid measure. Walking speed is closely linked to morbidity in patients and widely used for medical assessment via measured walking. The 6-min walk test (6MWT) is a standard assessment for chronic obstructive pulmonary disease and congestive heart failure. Current generation smartphone hardware contains similar sensor chips as in medical devices and popular fitness devices. We developed a middleware software, MoveSense, which runs on standalone smartphones while providing comparable readings to medical accelerometers. We evaluate six machine learning methods to obtain gait speed during natural walking training models to predict natural walking speed and distance during a 6MWT with 28 pulmonary patients and ten subjects without pulmonary condition. We also compare our model's accuracy to popular fitness devices. Our universally trained support vector machine models produce 6MWT distance with 3.23% error during a controlled 6MWT and 11.2% during natural free walking. Furthermore, our model attains 7.9% error when tested on five subjects for distance estimation compared to the 50-400% error seen in fitness devices during natural walking.

We have developed GaitTrack, a phone application to detect health status while the smartphone is carried normally. GaitTrack software monitors walking patterns, using only accelerometers embedded in phones to record spatiotemporal motion, without the need for sensors external to the phone. Our software transforms smartphones into health monitors, using eight parameters of phone motion transformed into body motion by the gait model. GaitTrack is designed to detect health status while the smartphone is carried during normal activities, namely, free-living walking. The current method for assessing free-living walking is medical accelerometers, so we present evidence that mobile phones running our software are more accurate. We then show our gait model is more accurate than medical pedometers for counting steps of patients with chronic disease. Our gait model was evaluated in a pilot study involving 30 patients with chronic lung disease. The six-minute walk test (6MWT) is a major assessment for chronic heart and lung disease, including congestive heart failure and especially chronic obstructive pulmonary disease (COPD), affecting millions of persons. The 6MWT consists of walking back and forth along a measured distance for 6 minutes. The gait model using linear regression performed with 94.13% accuracy in measuring walk distance, compared with the established standard of direct observation. We also evaluated a different statistical model using the same gait parameters to predict health status through lung function. This gait model has high accuracy when applied to demographic cohorts, for example, 89.22% accuracy testing the cohort of 12 female patients with ages 50-64 years.

Anonymity systems such as Tor aim to enable users to communicate in a manner that is untraceable by adversaries that control a small number of machines. To provide efficient service to users, these anonymity systems make full use of forwarding capacity when sending traffic between intermediate relays. In this paper, we show that doing this leaks information about the set of Tor relays in a circuit (path). We present attacks that, with high confidence and based solely on throughput information, can (a) reduce the attacker's uncertainty about the bottleneck relay of any Tor circuit whose throughput can be observed, (b) exactly identify the guard relay(s) of a Tor user when circuit throughput can be observed over multiple connections, and (c) identify whether two concurrent TCP connections belong to the same Tor user, breaking unlinkability. Our attacks are stealthy, and cannot be readily detected by a user or by Tor relays. We validate our attacks using experiments over the live Tor network. We find that the attacker can substantially reduce the entropy of a bottleneck relay distribution of a Tor circuit whose throughput can be observed-the entropy gets reduced by a factor of 2 in the median case.Such information leaks from a single Tor circuit can be combined over multiple connections to exactly identify a user's guard relay(s). Finally, we are also able to link two connections from the same initiator with a crossover error rate of less than 1.5% in under 5 minutes. Our attacks are also more accurate and require fewer resources than previous attacks on Tor.

Introduction: Smartphones are ubiquitous, but it is unknown what physiological functions can be monitored at clinical quality. Pulmonary function is a standard measure of health status for cardiopulmonary patients. We have shown phone sensors can accurately measure walking patterns. Here we show that improved classification models can accurately predict pulmonary function, with sole inputs being motion sensors from carried phones.Subjects and Methods: Twenty-five cardiopulmonary patients performed 6-minute walk tests in pulmonary rehabilitation at a regional hospital. They carried smartphones running custom software recording phone motion. Each patient's pulmonary function was measured by spirometry. A universal model, based on support vector machine, then computed the category of function with input from signal processing features and patient demographic features.Results: All but a few of every 10-second interval for every patient was correctly predicted. The trained model perfectly computed the GOLD (Global Initiative for Chronic Obstructive Lung Disease) level 1/2/3, which is a standard classification of pulmonary function. Each level was determined to have a characteristic motion, which could be recognized from the sensor features. In addition, longitudinal changes were detected for 10 patients with multiple walk tests, except for cases with clinical instability.Conclusions: These results are encouraging toward clinical validation of passive monitors running continuously in the background, for patients in homes during daily activities. Initial testing indicates the same high accuracy as with active monitors, for patients in hospitals during walk tests. We expect patients can simply carry their phones during everyday living, while models support automatic prediction of pulmonary function for health monitoring.

Abstract:The Tor anonymity network has been shown vulnerable to traffic analysis attacks by autonomous systems (ASes) and Internet exchanges (IXes), which can observe different overlay hops belonging to the same circuit. We evaluate whether network path prediction techniques provide an accurate picture of the threat from such adversaries, and whether they can be used to avoid this threat. We perform a measurement study by collecting 17.2 million traceroutes from Tor relays to destinations around the Internet. We compare the collected traceroute paths to predicted paths using state-of-the-art path inference techniques. We find that traceroutes present a very different picture, with the set of ASes seen in the traceroute path differing from the predicted path 80% of the time. We also consider the impact that prediction errors have on Tor security. Using a simulator to choose paths over a week, our traceroutes indicate a user has nearly a 100% chance of at least one compromise in a week with 11% of total paths containing an AS compromise and less than 1% containing an IX compromise when using default Tor selection. We find modifying the path selection to choose paths predicted to be safe lowers total paths with an AS compromise to 0.14% but still presents a 5-11% chance of at least one compromise in a week while making 5% of paths fail, with 96% of failures due to false positives in path inferences. Our results demonstrate more measurement and better path prediction is necessary to mitigate the risk of AS and IX adversaries to Tor.