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Wearable devices offer a promise of immense impact on worldwide global health by offering the potential for non-invasive, constantly vigilant, and low-cost monitoring of individual condition and fundamental advances in guiding healthcare. The urgency of this objective for its individual and societal benefits will attract an expanding community of researchers from backgrounds in nearly every field of computing. This article describes the unprecedented benefits and opportunities for computing research in wearable devices and the multidisciplinary challenges that have not been encountered individually or combined together in previous research. This article is focused on providing guidance to the new community of healthcare in computing researchers who will both create a new field and forge transformative solutions for healthcare delivery to a worldwide population.

This paper presents a novel method for automatically detecting the onset of ventricular depolarization in electrocardiogram (ECG). In order to accommodate highly variable ECG morphologies in potentially noisy ECG signals, a weighted combination of factors that are consistent with the onset of ventricular depolarization is computed. Weight parameters are optimized to maximize the detection accuracy. The proposed method is evaluated against diverse datasets, yielding a bias of 1.69 ms, standard deviation of 10.55 ms, and mean absolute error of 6.68 ms.

Detection of heart failure with preserved ejection fraction (HFpEF) by non-expert clinicians requires training and experience in operating Doppler echocardiography machines. Doppler echocardiography records were obtained for 18 patients scheduled to undergo right heart catheterization at the Oregon

Postoperative ileus (POI) can worsen outcomes, increase cost, and prolong hospitalization. We previously found that a disposable, non-invasive acoustic gastrointestinal surveillance (AGIS) biosensor distinguishes healthy controls from patients recovering from abdominal surgery. Here, we tested whether AGIS can prospectively predict which patients will develop POI in a multicenter study. AGIS is a disposable device embedded with a microphone that adheres to the abdominal wall and connects to a computer that measures acoustic intestinal rate (IR), defined as motility events/minute. We applied AGIS for 60 min before and continuously after abdominal surgery. Clinicians blinded to AGIS recordings clinically separated patients into those with vs. without POI. We used receiver operating characteristic curve analysis to calculate sensitivity, specificity, and negative predictive value (NPV) of AGIS to predict POI. There were 28 subjects; nine developed POI. Median IR was 3.01/min and 4.46/min between POI and non-POI groups, respectively (P = 0.03). AGIS predicted POI onset with a sensitivity, specificity, and NPV of 63, 72, and 81 %, respectively. Non-invasive, abdominal, acoustic monitoring prospectively predicts POI. Surgeons may use AGIS to rule out POI with over 80 % certainty; this offers added confidence to advance feeding earlier in those for whom it is safe.

© 2014 Elsevier Ltd. This study uses high-frequency appliance-level electricity consumption data for 124 apartments over 24 months to provide a better understanding of appliance-level electricity consumption behavior. We conduct our analysis in a standardized set of apartments with similar appliances, which allows us to identify behavioral differences in electricity use. The Results show that households' estimations of appliance-level consumption are inaccurate and that they overestimate lighting use by 75% and underestimate plug-load use by 29%. We find that similar households using the same major appliances exhibit substantial variation in appliance-level electricity consumption. For example, households in the 75th percentile of HVAC usage use over four times as much electricity as a user in the 25th percentile. Additionally, we show that behavior accounts for 25-58% of this variation. Lastly, we find that replacing the existing refrigerator with a more energy-efficient model leads to overall energy savings of approximately 11%. This is equivalent to results from behavioral interventions targeting all appliances but might not be as cost effective. Our findings have important implications for behavior-based energy conservation policies.

Background: The pivot-shift (PS) examination is used to demonstrate knee instability and detect anterior cruciate ligament (ACL) injury. Prior studies using inertial sensors identified the ACL-deficient knee with reasonable accuracy, but none addressed the more difficult problem of using these sensors to determine whether a subject has an ACL deficiency and to correctly assign a PS grade to a patient’s knee. Hypothesis: Inertial sensor data recorded during a PS examination can accurately predict ACL deficiency and the PS score assigned by the examining physician. Study Design: Cohort study (diagnosis); Level of evidence, 2. Methods: A total of 32 patients with unilateral ACL deficiency and 29 with intact ACLs in both knees had inertial sensor modules strapped to the tibia and femur of each limb for preoperative PS testing under anesthesia. Support vector machine (SVM) methods assessed PS grades on the basis of these data, with the examiner’s clinical grading shift used as ground truth. A fusion of regression and SVM classification techniques diagnosed ACL deficiency. Results: The clinically determined PS grades of all 122 knees were as follows: 0 (n = 69), +1 (n = 23), +2 (n = 27), and +3 (n = 3). The SVM classification analysis was 77% accurate in correctly classifying these grades, with 98% of computed PS grades falling within ±1 grade of the clinically determined value. The system fusion algorithm diagnosed ACL deficiency in an individual with an overall accuracy of 97%. This method yielded 6% false negatives and 0% false positives. Conclusion: This study used inertial sensor technology with SVM algorithms to accurately determine clinically assigned PS grades in ACL-intact and ACL-deficient knees. By extending the assessment to a separate group of patients without ACL injury, the inertial sensor data demonstrated highly accurate diagnosis of ACL deficiency.

Background Postoperative ileus (POI) can worsen outcomes, increase cost, and prolong hospitalization. An objective marker could help identify POI patients who should not be prematurely fed. We developed a disposable, non-invasive acoustic gastrointestinalsurveillance (AGIS) biosensor. We testedwhether AGIS can distinguish healthycontrolsfrom patientsrecoveringfrom abdominal surgery. Study Design AGIS is a disposable plastic device embedded with a microphone that adheres to the abdominal wall and connects to a computer that measures acoustic event rates. We compared intestinal rates of healthy subjects using AGIS for 60 min after a standardizedmealtorecordingsoftwopostoperativegroups:(1)patientstoleratingstandardizedfeedingand(2)POIpatients.We compared intestinal rates among groups using ANOVA and t tests. Results There were 8 healthy controls, 7 patients tolerating feeding, and 25 with POI; mean intestinal rates were 0.14, 0.03, and 0.016 events per second, respectively (ANOVA p

a b s t r a c t Engage is a rapidly deployable, retrofit energy monitoring system developed for direct support of a novel energy use behavior investigation and a large-scale deployment in campus apartments. We describe the end-to-end system and report results related to web dashboard engagement during a year-long experi- ment. The objective was to determine user engagement with real-time and easily accessible information about personal energy consumption. Leveraging low-cost components, this system was designed to mea- sure separately appliance plug load, heating and cooling, and lighting electrical load in dense-occupancy building environments. We developed and used an open source technology for measurement of plug load and developed signal processing algorithms to significantly improve measurement accuracy. We also developed proxy sensors to measure heating and cooling and lighting. Our results indicate that 90% of the dashboard activity was undertaken by 50% of the participants and that website engagement was more likely in mid-day and more effective in combination with email reminders. Energy conservation was achieved when combining the dashboard with public information about energy consumption. Published by Elsevier B.V.

Human activity monitoring systems using inertial sensors have found wide applications in the field of health and wellness by providing valuable information for diagnostics and rehabilitation processes to doctors and clinicians. As the scales of studies increase, sensor orientation placement errors have become one of the most commonly seen difficulties for such systems. Assuming patients to wear sensors at the correct orientation is unrealistic and will result in a large amount of data loss or distortion. In order to tackle this problem, we propose a double layer classification model. The first layer, not assuming correct sensor orientation, uses orientation-invariant accelerometer magnitude to construct a highly conservative walking detection model. The detected walking beacons from this layer are used to compare to the training template to obtain the true sensor orientation. Then proper rotation matrix can be applied to the whole day data, and fed into the second layer of a finer classifier where orientation-variant features are used. In order to show validity of this method, we hired 7 healthy subjects and 2 stroke patients in the rehab process to wear the sensors for two days and at least 6 hours each day. Ground truth are labeled manually with a Matlab GUI tool. Precision and recall for walking detection in each day are reported and discussed.

In this paper, we present a novel linear-program formulation that yields "optimally safe" (OS) tension distributions in parallel cable-driven robots by the introduction of a slack variable. The slack variable also enables explicit computation of a near-optimal, feasible starting point. This, in turn, enables rapid computation of the OS tension distributions. The formulation also contains a parameter that can be used to steer cable tensions toward desired regions of operation. We present static results from two simulated robotic systems that demonstrate the ability of our formulation to avoid tension limits. Simulated execution of highly dynamic trajectories on both systems demonstrates rapid-computation abilities. Furthermore, we present experimental results from a real robotic system that further validate the importance of safe tension distributions.

We present the Networked InfoMechanical System for Planar Translation, which is a novel two-degree-of-freedom (2-DOF) cable-driven robot with self-calibration and online drift-correction capabilities. This system is intended for actuated sensing applications in aquatic environments. The actuation redundancy resulting from in-plane translation driven by four cables results in an infinite set of tension distributions, thus requiring real-time computation of optimal tension distributions. To this end, we have implemented a highly efficient, iterative linear programming solver, which requires a very small number of iterations to converge to the optimal value. In addition, two novel self-calibration methods have been developed that leverage the robot's actuation redundancy. The first uses an incremental displacement, or jitter method, whereas the second uses variations in cable tensions to determine end-effector location. We also propose a novel least-squares drift-detection algorithm, which enables the robot to detect long-term drift. Combined with self-calibration capabilities, this drift-monitoring algorithm enables long-term autonomous operation. To verify the performance of our algorithms, we have performed extensive experiments in simulation and on a real system.

The feasibility of operating brackish water reverse osmosis (BWRO) desalination in a feed flow reversal (FFR) mode so as to mitigate mineral scaling was evaluated with feed solutions containing calcium, sodium, sulfate and chloride ions. The premise of the FFR approach is to enable dissolution of surface mineral crystals, that form first at the BWRO plant tail elements, by periodic exposure to the undersaturated feed solution. Automatic triggering of FFR was accomplished by integrating an Ex-situ scale observation detector (EXSOD) with the BWRO plant controller. The EXSOD system consisted of a small high pressure plate-and-frame RO cell, with its membrane surface digitally imaged in real-time, to monitor the onset and development of membrane mineral scaling. During normal feed flow (NFF) operation, the EXSOD monitored a concentrate side-stream from the BWRO plant tail element, signaling the BWRO plant controller to initiate FFR when the specified scaling threshold was reached. Once the plant feed flow was reversed, the EXSOD feed stream was switched from the BWRO plant concentrate to the plant's permeate in order to dissolve mineral crystals formed on the EXSOD membrane during NFF operation. Successful operation of automated FFR, whereby scale formation was prevented, required adjustment of the EXSOD operating conditions such that the saturation index in the EXSOD RO cell was at a level that would not result in scale detection significantly before or after scaling would occur in the RO plant. Although the onset of mineral scaling was detected at the level of appearance of the first observable mineral crystal, the results suggest that it would be more practical to trigger FFR based on a threshold that considers both the mineral crystal number density and fractional surface scale coverage. Current work is continuing to demonstrate the broad applicability of FFR mode of RO mineral scale-free operation (with reduced or no antiscalant addition) over a wide range of solution compositions and operating conditions.

This paper presents a study of the five best selling Smart Phones in terms of their applicability to Wireless Health. Smart Phones are generally used as central controlling units in Wireless Health applications. We carried out our investigation by implementing a wireless health application that performs sensor communication, data processing, and data visualization. Our overarching goal is to develop a plug-and-play Wireless Health software platform. Our task begins with an in depth study of Smart Phones: the central controller of Wireless health applications.

We present NIMS3D, a novel 3-D cabled robot for actuated sensing applications. We provide a brief overview of the main hardware components. Next, we describe installation procedures, including novel calibration methods, that enable rapid in-field deployability for nonexpert end users, and provide simulations and experimental results to highlight their effectiveness. Kinematic and dynamic analysis of the system are provided, followed by a description of control methods. We provide experimental results that illustrate tracking of linear and nonlinear paths by NIMS3D. Thereafter, we briefly present an example of an actuated sensing task performed by the system. Finally, we describe methods of improving energy efficiency by leveraging nonlinear trajectories and energy-optimal tension distributions. Experimental and simulated results show that energy efficiency can be improved significantly by using optimized parabolic trajectories. Furthermore, we provide simulation results that demonstrate improved efficiency enabled by optimal, least norm tension distributions.

We consider a wireless network with distributed processing capabilities for estimation or detection applications. Due to limited communication resources, the network selects only a subset of sensor measurements for estimation or detection as long as the resulting fidelity is tolerable. We present a distributed sampling scheme based on the concept of innovations diffusion to select the sensor nodes. In the proposed scheme, sensor selection is accomplished through local communication and signal processing. In order to conserve energy and prolong system lifetime, the proposed algorithm selects a nearly minimum number of active sensors to ensure a desired fidelity for each working period. Extensive simulations illustrate the effectiveness of the proposed sampling scheme.

Platelets have long been recognized to be of central importance in haemostasis, but their participation in pathological conditions such as thrombosis, atherosclerosis and inflammation is now also well established. The platelet has therefore become a key target in therapies to combat cardiovascular disease. Anti-platelet therapies are used widely, but current approaches lack efficacy in a proportion of patients, and are associated with side effects including problem bleeding. In the last decade, substantial progress has been made in understanding the regulation of platelet function, including the characterization of new ligands, platelet-specific receptors and cell signalling pathways. It is anticipated this progress will impact positively on the future innovations towards more effective and safer anti-platelet agents. In this review, the mechanisms of platelet regulation and current anti-platelet therapies are introduced, and strong, and some more speculative, potential candidate target molecules for future anti-platelet drug development are discussed.

Objective: Presented work highlights the development and initial validation of a medical embedded device for individualized care (MEDIC), which is based on a novel software architecture, enabling sensor management and disease prediction capabilities, and commercially available microelectronic components, sensors and conventional personal digital assistant (PDA) (or a cell phone). Methods and materials: In this paper, we present a general architecture for a wearable sensor system that can be customized to an individual patient's needs. This architecture is based on embedded artificial intelligence that permits autonomous operation, sensor management and inference, and may be applied to a general purpose wearable medical diagnostics. Results: A prototype of the system has been developed based on a standard PDA and wireless sensor nodes equipped with commercially available Bluetooth radio components, permitting real-time streaming of high-bandwidth data from various physiological and contextual sensors. We also present the results of abnormal gait diagnosis using the complete system from our evaluation, and illustrate how the wearable system and its operation can be remotely configured and managed by either enterprise systems or medical personnel at centralized locations. Conclusion: By using commercially available hardware components and software architecture presented in this paper, the MEDIC system can be rapidly configured, providing medical researchers with broadband sensor data from remote patients and platform access to best adapt operation for diagnostic operation objectives.

Platelets play an important role in hemostasis, with inappropriate platelet activation being a major contributor to debilitating and often fatal thrombosis by causing myocardial infarction and stroke. Although current antithrombotic treatment is generally well tolerated and effective, many patients still experience cardiovascular problems, which may reflect the existence of alternative underlying regulatory mechanisms in platelets to those targeted by existing drugs. In this study, we define a role for peripherally distributed members of the tachykinin family of peptides, namely substance P and the newly discovered endokinins A and B that are present in platelets, in the activation of platelet function and thrombus formation. We have reported previously that the preferred pharmacologically characterized receptor for these peptides, the NK1 receptor, is present on platelets. Inhibition or deficiency of the NK1 receptor, or SP agonist activity, resulted in substantially reduced thrombus formation in vitro under arterial flow conditions, increased bleeding time in mice, and a decrease in experimentally induced thromboembolism. Inhibition of the NK1 receptor may therefore provide benefit in patients vulnerable to thrombosis and may offer an alternative therapeutic target.

Large-scale environmental sensing, e.g., understanding microbial processes in an aquatic ecosystem, requires coordination across a multidisciplinary team of experts working closely with a robotic sensing and sampling system. We describe a human-robot team that conducted an aquatic sampling campaign in Lake Fulmor, San Jacinto Mountains Reserve, California during three consecutive site visits (May 9–11, June 19–22, and August 28–31, 2006). The goal of the campaign was to study the behavior of phytoplankton in the lake and their relationship to the underlying physical, chemical, and biological parameters. Phytoplankton form the largest source of oxygen and the foundation of the food web in most aquatic ecosystems. The reported campaign consisted of three system deployments spanning four months. The robotic system consisted of two subsystems—NAMOS (networked aquatic microbial observing systems) comprised of a robotic boat and static buoys, and NIMS-RD (rapidly deployable networked infomechanical systems) comprised of an infrastructure-supported tethered robotic system capable of high-resolution sampling in a two-dimensional cross section (vertical plane) of the lake. The multidisciplinary human team consisted of 25 investigators from robotics, computer science, engineering, biology, and statistics.We describe the lake profiling campaign requirements, the robotic systems assisted by a human team to perform high fidelity sampling, and the sensing devices used during the campaign to observe several environmental parameters. We discuss measures taken to ensure system robustness and quality of the collected data. Finally, we present an analysis of the data collected by iteratively adapting our experiment design to the observations in the sampled environment. We conclude with the plans for future deployments.

Wireless sensor network systems are now being applied by an international community for critical applications in commerce, healthcare, and security. These systems have unique characteristics and face many implementation challenges. Among all, the requirement of long operating life for a wireless sensor node under limited energy supply imposes the most severe design constraints. This calls for innovative design methodologies to address this rigorous requirement. This article first provides an overview of wireless technologies for sensor networks. It then describes communication system, circuit design, and system packaging considerations. The selection of radio architectures and circuit techniques is discussed with an emphasis on the low-power implementation and operating characteristics that match requirements of sensor network application. Finally, the design, implementation, and performance of the most challenging component, a complete low-power CMOS receiver system, is presented to demonstrate these design principles.

In distributed wireless sensing applications such as unattended ground sensor systems, remote planetary exploration, and condition-based maintenance, where the deployment site is remote and/or the scale of the network is large, individual emplacement and configuration of the sensor nodes is difficult. Hence, network self-assembly and continuous network self-organization during the lifetime of the network in a reliable, efficient, and scalable manner are crucial for successful deployment and operation of such networks. This paper provides an overview of the concept of network self-assembly for ad hoc wireless sensor networks at the link layer, with descriptions of results from implementation of a novel network formation mechanism for wireless unattended ground sensor applications using a multicluster hierarchical topology and a novel dual-radio architecture.

Profiling the daily activity of a physically disabled person in the community would enable healthcare professionals to monitor the type, quantity, and quality of their patients’ compliance with recommendations for exercise, fitness, and practice of skilled movements, as well as enable feedback about performance in real-world situations. Based on our early research in in-community activity profiling, we present in this paper an end-to-end system capable of reporting a patient's daily activity at multiple levels of granularity: 1) at the highest level, information on the location categories a patient is able to visit; 2) within each location category, information on the activities a patient is able to perform; and 3) at the lowest level, motion trajectory, visualization, and metrics computation of each activity. Our methodology is built upon a physical activity prescription model coupled with MEMS inertial sensors and mobile device kits that can be sent to a patient at home. A novel context-guided activity-monitoring concept with categorical location context is used to achieve enhanced classification accuracy and throughput. The methodology is then seamlessly integrated with motion reconstruction and metrics computation to provide comprehensive layered reporting of a patient's daily life. We also present an implementation of the methodology featuring a novel location context detection algorithm using WiFi augmented GPS and overlays, with motion reconstruction and visualization algorithms for practical in-community deployment. Finally, we use a series of experimental field evaluations to confirm the accuracy of the system.

A 900-MHz phase-locked loop frequency synthesizer implemented in a 0.6-/spl mu/m CMOS technology is developed for the wireless integrated network sensors applications. It incorporates an automatic switched-capacitor (SC) discrete-tuning loop to extend the overall frequency tuning range to 20%, while the VCO gain (K/sub VCO/) resulting from the CMOS varactor continuous-tuning is kept low at only 20 MHz/V in order to improve the reference spurs and noise performance. This frequency synthesizer achieves a phase noise of -102 dBc/Hz at 100 kHz offset frequency and reference spurs below -55 dBc. The synthesizer, including an on-chip VCO, dissipates only 2.5 mA from a 3-V supply.

Budd-Chiari syndrome (BCS) is an uncommon condition. It may vary in its presentation from very mild symptomatology, suggestive of a viral illness, to a very acute form with a fulminant course. Early diagnosis and treatment can make the difference between the development of hepatic failure requiring a transplant, and the sparing of hepatic function. The case presented here is that of a patient with a mild initial presentation but with an acute progression. The presentation as well as the management of this case is discussed.

We describe a micromachined magnetic-field sensor that is based on an electron tunneling transducer. This tunnel sensor is small, very sensitive, operates at ambient temperature and requires very little power. The measured resolution of the sensor is 0.3 nT/√Hz at 1 Hz. The limiting resolution, calculated based on fundamental noise sources, is 0.002 nT/√Hz at 1 Hz. The dominant source of the observed noise in the present device is low frequency air pressure fluctuations.

Interface circuits determine the performance of microsensors in diverse automotive, medical, and process-control applications. Microsensor systems for acceleration, rotation, pressure, and other signals most typically rely on capacitive displacement measurement in microstructures. Displacement sensitivity requirements are typically 0.1 pm with sensor capacitor values of 100 fF or less. Conventional architectures for capacitive displacement measurement have required bias control with either large value off-chip or large-area integrated resistive circuit components. Switched-capacitor measurement methods may provide a fully integrated solution with compact geometry but have been limited by severe charge injection error signals. The active charge cancellation system (ACCS), reported here, provides the first direct compensation of switched-capacitor charge-injection error at the measurement system input. The ACCS, implemented in a 0.8-/spl mu/m digital CMOS technology, uses a novel feedback network to directly cancel charge injection and reduce error by over two orders of magnitude without need for component matching.

The pivot shift has been correlated with patient-reported outcomes and knee function following ACL injury and reconstruction. Tibial rotation has been recognized as an important component to the pivot shift motion path. However, few methodologies exist to quantify tibial rotation in the clinical setting. The purpose of this study was to validate the use of a wireless gyroscopic sensor to measure axial rotation of the tibia during a manually simulated pivot shift manoeuvre in cadaveric specimens. We hypothesized that integrated gyroscopic measurements of tibial rotation velocity (tibial rotation) would be highly correlated with tibial rotations simultaneously recorded with a rotary potentiometer during a simulated pivot shift motion under intact and ACL-deficient conditions. Gyroscopic measurements of rotational velocity were integrated and calibrated to a known arc of rotation. The gyroscope was mounted on the distal tibia with its axis aligned to the tibial shaft. Ten simulations of a pivot shift motion pathway were performed on nine cadaveric knees under intact and ACL-deficient conditions. Logistic regression was used to compare gyroscopic and potentiometer measurements of tibial rotation for both test conditions. Gyroscopic measurements of maximum external tibial rotation during the simulated pivot shift motion pathway were strongly correlated with potentiometer measurements of external tibial rotation in both the intact and ACL-deficient states (R 2 = 0.984). The gyroscope evaluated in this cadaveric study was capable of accurately recording tibial rotation during a simulated pivot shift motion pathway.

Measuring the energy consumed by modern secondary storage devices and attributing it to the causative application's processes can guide optimization of application energy efficiency. Prior work has focused on hard disk drive or file system energy management using custom-built measurement systems and models, but suffers from limitations that prohibit the attribution of the hardware energy consumption to the causative processes. This paper presents a novel process-level disk drive energy estimation system that monitors the operating system kernel to predict the storage power consumption created by individual processes of a software application. Evaluation reveals that the system is accurate with prediction errors around 7% even for workloads involving simultaneous file system operations from multiple processes. The system creates virtually no power consumption overhead in the CPU and about 4% overhead in the RAM module. Results using I/O benchmarks demonstrate that a large portion of the energy usage (up to 100% in some cases) in modern disk drives can occur after the causative processes have already completed execution. Hence, results obtained using power meters or direct energy measurement systems based solely on a benchmark's execution lifetime can drastically underestimate the benchmark application's storage energy demands.

In this paper, we present a new gait analysis method using 3D body motion reconstruction with an activity-specific tracking protocol. A kinematic chain modeling the movement of lower extremities was constructed for general lower body activity monitoring. By exploring the nature of walking, a constrained forward-backward statistical linearized sigma-point Kalman Smoother with periodic state vector resetting was developed. This tracks the dynamic joint configuration during walking. Direct experimental evaluation was provided by step length computation as well as complete motion reconstruction. This method has demonstrated stable long term tracking of walking and yields greater than 95% accuracy for step length estimation.

Rapidly emerging applications for microaccelerometer and microgyroscope position sensors require a major advance in stability and resolution. A highly sensitive, stable, and robust sensor interface circuit that can be applied to inertial navigation sensors is presented in this paper. The circuit can be applied to any fully differential capacitive sensor or to a differential piezoresistive one. The high sensitivity is achieved by a double-sampling technique, compensating switch, and feedback method with a high loop gain. The charge injection by the MOS switches is reduced below the thermal noise level of the operational amplifier (opamp). Resolution and stability levels of 10 −8 with a supply variation of 10% are achieved.

Direct measurement of event and component resolved energy dissipation in computing systems is critical for energy optimization of computing and networking applications. Prior research focuses on development of energy consumption models and custom-built energy measurement systems, but suffers from critical drawbacks. This paper addresses these limitations and presents solutions using DEEP (Decision-support for Energy Efficient Processing). DEEP is a rapidly-deployed open-source energy measurement platform architecture. The platform provides an unprecedented ability to non-intrusively measure the energy consumption associated with execution of software application code. DEEP is implemented as both an online and offline version. Evaluation demonstrates processing and energy overheads less than 1% for offline and about 5% for the online implementation. The DEEP implementation investigates the impact of data compression on network data transport. An intelligent data compression and transport algorithm is developed using the decision-support capabilities of DEEP. The algorithm creates significant energy savings (38%) in network data transport using dynamic selection of compression schemes to adapt to varying system and wireless network conditions.

Energy efficiency has been a longstanding design challenge for wearable sensor systems. It is especially crucial in continuous subject state monitoring due to the ongoing need for compact sizes and better sensors. This paper presents an energy-efficient classification algorithm, based on partially observable Markov decision process (POMDP). In every time step, POMDP dynamically selects sensors for classification via a sensor selection policy. The sensor selection problem is formalized as an optimization problem, where the objective is to minimize misclassification cost given some energy budget. State transitions are modeled as a hidden Markov model (HMM), and the corresponding sensor selection policy is represented using a finite-state controller (FSC). To evaluate this framework, sensor data were collected from multiple subjects in their free-living conditions. Relative accuracies and energy reductions from the proposed method are compared against naive Bayes (always-on) and simple random strategies to validate the relative performance of the algorithm. When the objective is to maintain the same classification accuracy, significant energy reduction is achieved.

Advancement in wireless health sensor systems has triggered rapidly expanding research in continuous activity monitoring for chronic disease management or promotion and assessment of physical rehabilitation. Wireless motion sensing is increasingly important in treatments where remote collection of sensor measurements can provide an in-field objective evaluation of physical activity patterns. The well-known challenge of limited operating lifetime of energy-constrained wireless health sensor systems continues to present a primary limitation for these applications. This paper introduces CARER, a software system that supports a novel algorithm that exploits knowledge of context and dynamically schedules sensor measurement episodes within an energy consumption budget while ensuring classification accuracy. The sensor selection algorithm in the CARER system is based on Partially Observable Markov Decision Process (POMDP). The parameters for the POMDP algorithm can be obtained through standard maximum likelihood estimation. Sensor data are also collected from multiple locations of the subjects body, providing estimation of an individual's daily activity patterns.

Advances in micromachining technology in many defense and civilian programs make it feasible to design miniature sensors and instruments of the order of 10 2 –10 3 and 10 3 –10 4 g, respectively. By applying new signal detection principles, we have designed microspectrometers, microseismometers, microsamplers and other devices, which are now in varying phases of development and testing. Microspacecraft of the order of 10 kg, employing miniature sensors and instruments, are uniquely suited for three types of space science missions: missions that require multiple simultaneous measurements in different locations, missions that require very high launch energy and high risk missions where risk may be reduced by replacing a single large and expensive spacecraft with numerous independent small crafts. Applications examined include seismological and meteorological measurements on planetary surfaces and atmospheres and investigation of the mesoscale plasma structure around the Earth.

This paper describes the successful fabrication and demonstration of a new dual-element micromachined silicon tunnel accelerometer that extends the operational bandwidth beyond the resonant frequency of the proof mass.

A series of displacement transducers have been demonstrated which are based on the detection of electrons that quantum-mechanically tunnel across a narrow gap between electrodes. These transducers have important applications due to the sensitivity of the tunneling mechanism to sub-/spl Aring/ variations in the electrode gap. In this paper, we describe the recent development of wide-bandwidth electromechanical actuators and simple feedback circuitry which have been adapted for use in tunneling displacement transducers. With these actuators and circuits, we have built tunneling transducers with control bandwidths well in excess of 10 kHz. The design, fabrication, operation, and applications of these actuators are described. >

Human motion monitoring and activity classification, specifically in the free-living conditions, are becoming increasingly important as preventative and rehabilitative measures in health and wellness applications. In contrast to gate analysis, wearable sensor-based evaluation of the upper body activities is not well studied. The work in this paper describes a novel system for upper body activity monitoring and classification. This paper focuses specifically on the application of motion classification to a complex task of automating rehabilitation evaluation, such as a Wolf Motor Function Test. The presented system consists of a novel wearable motion sensor platform and classification algorithms that convert motion data to an alphabet representation to form strings of primitives. A general string expression is then derived for each task and a regular expression-based searching method is developed. We present results from the successful application of the proposed system to upper extremity activity characterization.

Pressure Ulcer (PU) incidence leads to considerable risk, in particular for the frail elderly, and a large national healthcare treatment cost. Evidence-based methods for assuring the health and safety of patients are urgently needed. Recent clinical trials have demonstrated that sub-epidermal moisture (SEM) present in tissue may be measured by interrogation of tissue dielectric properties and are associated with the presence of erythema and development of early stage PU conditions. A novel wireless handheld device has been developed and will be demonstrated that introduces a series of advances including automated measurement, automated measurement method assurance including application of proper measurement applied pressure, and wireless energy recharge capability. This advances previous successful prototype development to now include a complete point-of-care usage product. This device, termed the SEM Scanner, was successfully verified in trials with 30 subjects and is currently deployed in large clinical trials in nursing homes in Los Angeles. This manuscript and the planned demonstration describe a set of significant technology advances over previous technology based on both new Wireless Health hardware and software system solutions. In addition to the demonstration to be provided of a novel end-to-end system including data acquisition, data archiving, reporting, and compliance verification, data from trials will also be presented.

Continued rapid progress in the development of embedded motion sensing enables wearable devices that provide fundamental advances in the capability to monitor and classify human motion, detect movement disorders, and estimate energy expenditure. With this progress, it is becoming possible to provide, for the first time, evaluation of outcomes of rehabilitation interventions and direct guidance for advancement of subject health, wellness, and safety. The progress in motion classification relies on both the performance of new sensor fusion methods that provide inference, and the energy efficiency of energy-constrained monitoring sensors. As will be described here, both of these objectives require advances in the capability of detecting and classifying the location and environmental context. Context directly enables both enhanced motion classification accuracy and speed through reduction in search space, and reduced energy demand through context-aware optimization of sensor sampling and operation schedules. There have been attempts to introduce context awareness into activity monitoring with limited success, due to the ambiguity in the definition of context, and the lack of a system architecture that enables the adaptation of signal processing and sensor fusion algorithms specific to the task of personalized activity monitoring. In this paper we present a novel end-to-end system that provides context guided personalized activity classification. With a refined concept of context, the system introduces interface models that feature a context classification committee, the concept of context specific activity classification, the ability to manage sensors given context, and the ability to operate in real time through web services. We also present an implementation that demonstrates accurate context classification, accurate activity classification using context specific models with improved accuracy and speed, and extended operating life through sensor energy management.

Background and Purpose— Outcome measures of mobility for large stroke trials are limited to timed walks for short distances in a laboratory, step counters and ordinal scales of disability and quality of life. Continuous monitoring and outcome measurements of the type and quantity of activity in the community would provide direct data about daily performance, including compliance with exercise and skills practice during routine care and clinical trials. Methods— Twelve adults with impaired ambulation from hemiparetic stroke and 6 healthy controls wore triaxial accelerometers on their ankles. Walking speed for repeated outdoor walks was determined by machine-learning algorithms and compared to a stopwatch calculation of speed for distances not known to the algorithm. The reliability of recognizing walking, exercise, and cycling by the algorithms was compared to activity logs. Results— A high correlation was found between stopwatch-measured outdoor walking speed and algorithm-calculated speed (Pearson coefficient, 0.98; P =0.001) and for repeated measures of algorithm-derived walking speed ( P =0.01). Bouts of walking >5 steps, variations in walking speed, cycling, stair climbing, and leg exercises were correctly identified during a day in the community. Compared to healthy subjects, those with stroke were, as expected, more sedentary and slower, and their gait revealed high paretic-to-unaffected leg swing ratios. Conclusions— Test–retest reliability and concurrent and construct validity are high for activity pattern-recognition Bayesian algorithms developed from inertial sensors. This ratio scale data can provide real-world monitoring and outcome measurements of lower extremity activities and walking speed for stroke and rehabilitation studies.

Continued rapid progress in cost reduction, energy efficiency, and new data transport architectures for body worn sensors enables remote monitoring of patient activity with critical focus and impact on successful outcomes in healthcare. Monitoring systems, composed of both sensor and signal processing systems, seek to provide the capability to classify subject motion state and characteristics. Monitoring system progress has currently enabled classification of normal gait or abnormal gait within constrained laboratory operating conditions. However, monitoring of subjects in the community (specifically in residential environments remote from the laboratory or urban outdoor environments) has introduced fundamental challenges that have not been solved in the past. These challenges become profoundly more severe when monitoring subjects suffering from impaired gait due to conditions including stroke and other neurological disorders. One of the most important measures required in neurological rehabilitation is the accurate classification of walking speed in the community. Changes in absolute speed directly indicate rehabilitation progress and also directly determine whether an individual may remain safe and functional. Healthcare delivery practice requires that characterization of walking parameters and speed must be provided with reliance only on limited system training data acquisition and time. This paper reports on a primary advance in this capability through development of a novel architecture delivering required high rate, continuous sampling at low cost, with compact sensors and with rapidly deployable systems. Most importantly, this paper introduces a new hierarchical classification system applicable to subjects afflicted with hemi paresis due to stroke and disorders including multiple sclerosis. This system provides accurate classification and characterization of walking mobility invariant to other activities performed at the same time and in the presence of interfering signals induced by gait changes. Continued rapid progress in cost reduction, energy efficiency, and new data transport architectures for body worn sensors enables remote monitoring of patient activity with critical focus and impact on successful outcomes in healthcare. Monitoring systems, composed of both sensor and signal processing systems, seek to provide the capability to classify subject motion state and characteristics. Monitoring system progress has currently enabled classification of normal gait or abnormal gait within constrained laboratory operating conditions. However, monitoring of subjects in the community (specifically in residential environments remote from the laboratory or urban outdoor environments) has introduced fundamental challenges that have not been solved in the past. These challenges become profoundly more severe when monitoring subjects suffering from impaired gait due to conditions including stroke and other neurological disorders. One of the most important measures required in neurological rehabilitation is the accurate classification of walking speed in the community. Changes in absolute speed directly indicate rehabilitation progress and also directly determine whether an individual may remain safe and functional. Healthcare delivery practice requires that characterization of walking parameters and speed must be provided with reliance only on limited system training data acquisition and time. This paper reports on a primary advance in this capability through development of a novel architecture delivering required high rate, continuous sampling at low cost, with compact sensors and with rapidly deployable systems. Most importantly, this paper introduces a new hierarchical classification system applicable to subjects afflicted with hemi paresis due to stroke and disorders including multiple sclerosis. This system provides accurate classification and characterization of walking mobility invariant to other activities performed at the same time and in the presence of interfering signals induced by gait changes.

In this paper, we describe a physical activity classification system using a body sensor network (BSN) consisting of cost-sensitive tri-axial accelerometers. We focus on workspace activities (different motions and sitting postures). We use a Naive Bayes classifier and show that we can train the system simply and systematically. For each task, we find a set of features that separate the corresponding activities.

Wireless embedded platforms play a significant role in Wireless Health: For many hidden medical conditions, symptoms may not reveal during traditional clinical visits. Cumulative, free-living monitoring, where individuals are monitored continuously with the use of wireless electronics and sensors, is considered a potential solution for capturing additional physiological data (Bonato 2003). From chronic disease management to physical rehabilitation, these platforms have demonstrated their potential uses (Moy et al. 2003; Bonato 2005).

Physical rehabilitation has become increasingly important for subjects recovering from treatment in clinics and from disease conditions including stroke [6]. A particularly critical requirement is the support of individuals who suffer from impaired mobility or may be confined to beds, yet must be provided with an assured fitness promotion regimen. We will demonstrate a portable cycle restorator that continuously measures arm and leg cycling activities through an Android-based device in real time. Both cadence and torque can be measured and logged continuously via a wireless interface. Sensor data are stored and relayed to a remote server. This system enables a dramatic cost reduction and increased deployment capability.

AutoGait is a mobile platform that autonomously discovers a user's walking profile and accurately estimates the distance walked. The discovery is made by utilizing the GPS in the user's mobile device when the user is walking outdoors. This profile can then be used both indoors and outdoors to estimate the distance walked. To model the person's walking profile, we take advantage of the fact that a linear relationship exists between step frequency and stride length, which is unique to individuals and applies to everyone regardless of age. Autonomous calibration invisible to users allows the system to maintain a high level of accuracy under changing conditions. AutoGait can be integrated into any pedometer or indoor navigation software on handheld devices as long as they are equipped with GPS. The main contribution of this paper is two fold: (1) we propose an auto-calibration method that trains a person's walking profile by effectively processing noisy GPS readings, and (2) we build a prototype system and validate its performance by performing extensive experiments. Our experimental results confirm that the proposed auto-calibration method can accurately estimate a person's walking profile and thus significantly reduce the error rate.

We have used the extreme sensitivity of electron tunneling to variations in electrode separation to construct a novel, compact displacement transducer. Electrostatic forces are used to control the separation between the tunneling electrodes, thereby eliminating the need for piezoelectric actuators. The entire structure is composed of micromachined silicon single crystals. Applications of the tunneling displacement transducer to the measurement of acceleration and infrared signals are discussed. Measurements of acceleration and infrared sensitivity are reported.