Highly-resilient, energy-efficient multipath routing in wireless sensor networks

Previously proposed sensor network data dissemination schemes require periodic low-rate flooding of data in order to allow recovery from failure. We consider constructing two kinds of multipaths to enable energy efficient recovery from failure of the shortest path between source and sink. Disjoint multipath has been studied in the literature. We propose a novel braided multipath scheme, which results in several partially disjoint multipath schemes. We find that braided multipaths are a viable alternative for energy-efficient recovery from isolated and patterned failures.

RisQ: Recognizing Smoking Gestures with Inertial Sensors on a Wristband

Smoking-induced diseases are known to be the leading cause of death in the United States. In this work, we design RisQ, a mobile solution that leverages a wristband containing a 9-axis inertial measurement unit to capture changes in the orientation of a person's arm, and a machine learning pipeline that processes this data to accurately detect smoking gestures and sessions in real-time. Our key innovations are fourfold: a) an arm trajectory-based method that extracts candidate hand-to-mouth gestures, b) a set of trajectory-based features to distinguish smoking gestures from confounding gestures including eating and drinking, c) a probabilistic model that analyzes sequences of hand-to-mouth gestures and infers which gestures are part of individual smoking sessions, and d) a method that leverages multiple IMUs placed on a person's body together with 3D animation of a person's arm to reduce burden of self-reports for labeled data collection. Our experiments show that our gesture recognition algorithm can detect smoking gestures with high accuracy (95.7%), precision (91%) and recall (81%). We also report a user study that demonstrates that we can accurately detect the number of smoking sessions with very few false positives over the period of a day, and that we can reliably extract the beginning and end of smoking session periods.

Dimensions

An important class of networked systems is emerging that involve very large numbers of small, low-power, wireless devices. These systems offer the ability to sense the environment densely, offering unprecedented opportunities for many scientific disciplines to observe the physical world. In this paper, we argue that a data handling architecture for these devices should incorporate their extreme resource constraints - energy, storage and processing - and spatio-temporal interpretation of the physical world in the design, cost model, and metrics of evaluation. We describe DIMENSIONS, a system that provides a unified view of data handling in sensor networks, incorporating long-term storage, multi-resolution data access and spatio-temporal pattern mining.

Center of excellence for mobile sensor data-to-knowledge (MD2K)

Mobile sensor data-to-knowledge (MD2K) was chosen as one of 11 Big Data Centers of Excellence by the National Institutes of Health, as part of its Big Data-to-Knowledge initiative. MD2K is developing innovative tools to streamline the collection, integration, management, visualization, analysis, and interpretation of health data generated by mobile and wearable sensors. The goal of the big data solutions being developed by MD2K is to reliably quantify physical, biological, behavioral, social, and environmental factors that contribute to health and disease risk. The research conducted by MD2K is targeted at improving health through early detection of adverse health events and by facilitating prevention. MD2K will make its tools, software, and training materials widely available and will also organize workshops and seminars to encourage their use by researchers and clinicians.

Coping with irregular spatio-temporal sampling in sensor networks

Wireless sensor networks have attracted attention from a diverse set of researchers, due to the unique combination of distributed, resource and data processing constraints. However, until now, the lack of real sensor network deployments have resulted in ad-hoc assumptions on a wide range of issues including topology characteristics and data distribution. As deployments of sensor networks become more widespread [1, 2], many of these assumptions need to be revisited.This paper deals with the fundamental issue of spatio-temporal irregularity in sensor networks We make the case for the existence of such irregular spatio-temporal sampling, and show that it impacts many performance issues in sensor networks. For instance, data aggregation schemes provide inaccurate results, compression efficiency is dramatically reduced, data storage skews storage load among nodes and incurs significantly greater routing overhead. To mitigate the impact of irregularity, we outline a spectrum of solutions. For data aggregation and compression, we propose the use of spatial interpolation of data (first suggested by Ganeriwal et al in [3] and temporal signal segmentation followed by alignment. To reduce the cost of data-centric storage and routing, we propose the use of virtualization, and boundary detection.

iShadow: Design of a Wearable, Real-Time Mobile Gaze Tracker

Continuous, real-time tracking of eye gaze is valuable in a variety of scenarios including hands-free interaction with the physical world, detection of unsafe behaviors, leveraging visual context for advertising, life logging, and others. While eye tracking is commonly used in clinical trials and user studies, it has not bridged the gap to everyday consumer use. The challenge is that a real-time eye tracker is a power-hungry and computation-intensive device which requires continuous sensing of the eye using an imager running at many tens of frames per second, and continuous processing of the image stream using sophisticated gaze estimation algorithms. Our key contribution is the design of an eye tracker that dramatically reduces the sensing and computation needs for eye tracking, thereby achieving orders of magnitude reductions in power consumption and form-factor. The key idea is that eye images are extremely redundant, therefore we can estimate gaze by using a small subset of carefully chosen pixels per frame. We instantiate this idea in a prototype hardware platform equipped with a low-power image sensor that provides random access to pixel values, a low-power ARM Cortex M3 microcontroller, and a bluetooth radio to communicate with a mobile phone. The sparse pixel-based gaze estimation algorithm is a multi-layer neural network learned using a state-of-the-art sparsity-inducing regularization function that minimizes the gaze prediction error while simultaneously minimizing the number of pixels used. Our results show that we can operate at roughly 70mW of power, while continuously estimating eye gaze at the rate of 30 Hz with errors of roughly 3 degrees.

Is decreased activity of C-II activated lipoprotein lipase in type III hyperlipoproteinemia (broad-beta-disease) a cause or an effect of increased apolipoprotein E levels?

Apolipoprotein E (ApoE; "arginine-rich" polypeptide) strongly inhibited both C-I and C-II activated lipoprotein lipases but not the protamine insensitive triglyceride lipase. Inhibition of lipoprotein lipases by ApoE in contrast to inhibition by C-III was not reversed to any significant extent by either increased concentration of activator or triglyceride in the substrate. Our previous studies have shown that in a type III hyperlipoproteinemia (broad-beta-disease) a post-heparin plasma lipoprotein lipase activated by C-II polypeptide of lipoprotein C is decreased in enzyme activity and exhibits an impaired ability to hydrolyze triglycerides in very low density lipoproteins. Type III patients are characterized by elevated concentrations of ApoE in the serum. The data presented in this report suggest that the decreased C-II activated lipoprotein lipase may be further aggravated by increased ApoE levels. Since this enzyme is involved in the catabolism and removal of lipoproteins, decreased activity of C-II activativated lipoprotein lipase may presumably be responsible for increased ApoE.

Purified postheparin plasma lipoprotein lipase in primary hyperlipoproteinemias

Purified postheparin plasma lipoprotein lipase (LPL) of normolipidemic and primary hyperlipoproteinemic subjects was characterized by lipoprotein C polypeptide activation and specificity for triglycerides in chylomicrons and VLDL. Chromatography of normal LPL on Sephadex G-100 resulted in two protein peaks, LPLC-1 (activated by C-I but not C-II) and LPLC-II (activated by C-II but not C-I). LPL from type I hyperlipoproteinemic subjects was not activated by C-I and C-II activation was reduced to 40% of control. Hydrolysis of chylomicron and VLDL triglycerides was severely impaired. Although chromatography of type I LPL resulted in two protein peaks, the protein peak corresponding to LPLC-I did not exhibit lipolytic activity and LPLC-II was reduced to 50% of control in protein and enzyme specific activity. Type III LPL was normal in respect to LPLC-I while LPLC-II averaged 40% of control. Hydrolysis of chylomicron and VLDL was reduced to 50% and 10% of control, respectively. An etiological implication for LPLC-I and/or LPLC-II in type I and III hyperlipoproteinemias is suggested.

Domain Adaptation Methods for Improving Lab-to-field Generalization of Cocaine Detection using Wearable ECG

Mobile health research on illicit drug use detection typically involves a two-stage study design where data to learn detectors is first collected in lab-based trials, followed by a deployment to subjects in a free-living environment to assess detector performance. While recent work has demonstrated the feasibility of wearable sensors for illicit drug use detection in the lab setting, several key problems can limit lab-to-field generalization performance. For example, lab-based data collection often has low ecological validity, the ground-truth event labels collected in the lab may not be available at the same level of temporal granularity in the field, and there can be significant variability between subjects. In this paper, we present domain adaptation methods for assessing and mitigating potential sources of performance loss in lab-to-field generalization and apply them to the problem of cocaine use detection from wearable electrocardiogram sensor data.

OpiTrack: A Wearable-based Clinical Opioid Use Tracker with Temporal Convolutional Attention Networks

Opioid use disorder is a medical condition with major social and economic consequences. While ubiquitous physiological sensing technologies have been widely adopted and extensively used to monitor day-to-day activities and deliver targeted interventions to improve human health, the use of these technologies to detect drug use in natural environments has been largely underexplored. The long-term goal of our work is to develop a mobile technology system that can identify high-risk opioid-related events (i.e., development of tolerance in the setting of prescription opioid use, return-to-use events in the setting of opioid use disorder) and deploy just-in-time interventions to mitigate the risk of overdose morbidity and mortality. In the current paper, we take an initial step by asking a crucial question: Can opioid use be detected using physiological signals obtained from a wrist-mounted sensor? Thirty-six individuals who were admitted to the hospital for an acute painful condition and received opioid analgesics as part of their clinical care were enrolled. Subjects wore a noninvasive wrist sensor during this time (1-14 days) that continuously measured physiological signals (heart rate, skin temperature, accelerometry, electrodermal activity, and interbeat interval). We collected a total of 2070 hours (≈ 86 days) of physiological data and observed a total of 339 opioid administrations. Our results are encouraging and show that using a Channel-Temporal Attention TCN (CTA-TCN) model, we can detect an opioid administration in a time-window with an F1-score of 0.80, a specificity of 0.77, sensitivity of 0.80, and an AUC of 0.77. We also predict the exact moment of administration in this time-window with a normalized mean absolute error of 8.6% and R ² coefficient of 0.85.

Laissez-Faire : Fully Asymmetric Backscatter Communication

Backscatter provides dual-benefits of energy harvesting and low-power communication, making it attractive to a broad class of wireless sensors. But the design of a protocol that enables extremely power-efficient radios for harvesting-based sensors as well as high-rate data transfer for data-rich sensors presents a conundrum. In this paper, we present a new fully asymmetric backscatter communication protocol where nodes blindly transmit data as and when they sense. This model enables fully flexible node designs, from extraordinarily power-efficient backscatter radios that consume barely a few micro-watts to high-throughput radios that can stream at hundreds of Kbps while consuming a paltry tens of micro-watts. The challenge, however, lies in decoding concurrent streams at the reader, which we achieve using a novel combination of time-domain separation of interleaved signal edges, and phase-domain separation of colliding transmissions. We provide an implementation of our protocol, LF-Backscatter, and show that it can achieve an order of magnitude or more improvement in throughput, latency and power over state-of-art alternatives.

Prospective study of outcomes in lumbar discectomy

Establishing standardized methods to assess outcome is needed to measure the effectiveness of surgery in relieving symptoms and improving quality of life. We prospectively studied 203 patients undergoing primary lumbar discectomy. Data was collected before surgery, at 3 months postoperatively and at long-term follow-up (12-60 months, median 24) using both disease-specific (visual analogue scores, Roland-Morris disability scales, and Hospital Anxiety and Depression scales) and generic (SF-36) instruments. Continued significant symptomatic benefit was observed to long-term assessment and the health gains in this patient group compared favourably with other elective surgical procedures. We have used this data to validate the SF-36 for use in this context and we recommend that SF36 should be used as a sole measure of outcome in routine practice, as well as in future studies.

Phyjama

Unobtrusive and continuous monitoring of cardiac and respiratory rhythm, especially during sleeping, can have significant clinical utility. An exciting new possibility for such monitoring is the design of textiles that use all-textile sensors that can be woven or stitched directly into a textile or garment. Our work explores how we can make such monitoring possible by leveraging something that is already familiar, such as pyjama made of cotton/silk fabric, and imperceptibly adapt it to enable sensing of physiological signals to yield natural fitting, comfortable, and less obtrusive smart clothing.

We face several challenges in enabling this vision including requiring new sensor design to measure physiological signals via everyday textiles and new methods to deal with the inherent looseness of normal garments, particularly sleepwear like pyjamas. We design two types of textile-based sensors that obtain a ballistic signal due to cardiac and respiratory rhythm ---the first a novel resistive sensor that leverages pressure between the body and various surfaces and the second is a triboelectric sensor that leverages changes in separation between layers to measure ballistics induced by the heart. We then integrate several instances of such sensors on a pyjama and design a signal processing pipeline that fuses information from the different sensors such that we can robustly measure physiological signals across a range of sleep and stationary postures. We show that the sensor and signal processing pipeline has high accuracy by benchmarking performance both under restricted settings with twenty one users as well as more naturalistic settings with seven users.

CIDER: Enabling Robustness-Power Tradeoffs on a Computational Eyeglass

The human eye offers a fascinating window into an individual's health, cognitive attention, and decision making, but we lack the ability to continually measure these parameters in the natural environment. The challenges lie in: a) handling the complexity of continuous high-rate sensing from a camera and processing the image stream to estimate eye parameters, and b) dealing with the wide variability in illumination conditions in the natural environment. This paper explores the power-robustness tradeoffs inherent in the design of a wearable eye tracker, and proposes a novel staged architecture that enables graceful adaptation across the spectrum of real-world illumination. We propose CIDER, a system that operates in a highly optimized low-power mode under indoor settings by using a fast Search-Refine controller to track the eye, but detects when the environment switches to more challenging outdoor sunlight and switches models to operate robustly under this condition. Our design is holistic and tackles a) power consumption in digitizing pixels, estimating pupillary parameters, and illuminating the eye via near-infrared, b) error in estimating pupil center and pupil dilation, and c) model training procedures that involve zero effort from a user. We demonstrate that CIDER can estimate pupil center with error less than two pixels (0.6°), and pupil diameter with error of one pixel (0.22mm). Our end-to-end results show that we can operate at power levels of roughly 7mW at a 4Hz eye tracking rate, or roughly 32mW at rates upwards of 250Hz.

iLid: Low-power Sensing of Fatigue and Drowsiness Measures on a Computational Eyeglass

The ability to monitor eye closures and blink patterns has long been known to enable accurate assessment of fatigue and drowsiness in individuals. Many measures of the eye are known to be correlated with fatigue including coarse-grained measures like the rate of blinks as well as fine-grained measures like the duration of blinks and the extent of eye closures. Despite a plethora of research validating these measures, we lack wearable devices that can continually and reliably monitor them in the natural environment. In this work, we present a low-power system, iLid, that can continually sense fine-grained measures such as blink duration and Percentage of Eye Closures (PERCLOS) at high frame rates of 100fps. We present a complete solution including design of the sensing, signal processing, and machine learning pipeline; implementation on a prototype computational eyeglass platform; and extensive evaluation under many conditions including illumination changes, eyeglass shifts, and mobility. Our results are very encouraging, showing that we can detect blinks, blink duration, eyelid location, and fatigue-related metrics such as PERCLOS with less than a few percent error.

Melioidosis presenting as spinal epidural abscess

Central nervous system melioidosis is an unusual infection in humans. This article reports a case of melioidosis presenting as an acute spinal epidural abscess. A discussion of this case and its management together with a brief review of melioidosis of the central nervous system is presented.