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Body Sensor Networks (BSN), 2013 IEEE International Conference on

Date 6-9 May 2013

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Displaying Results 1 - 25 of 77
  • [Copyright notice]

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    Freely Available from IEEE
  • Pattern classification of foot strike type using body worn accelerometers

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (258 KB) |  | HTML iconHTML  

    The automatic classification of foot strike patterns into the three basic categories forefoot, midfoot and rearfoot striking plays an important role for applications like shoe fitting with instant feedback. This paper presents methods for this classification based on body worn accelerometers that allow giving the required direct feedback to the user. For our study, we collected data from 40 runners who had a standard accelerometer in a custom-built sensor pod attached to the laces of their running shoes. The acceleration in three axes was recorded continuously while the runners conducted their runs. Data for repeated runs at two different speed levels were collected in order to have sufficient sensor data for classification. The data was analyzed using features computed for individual steps of the runners to distinguish the three foot strike pattern classes. The labels for the strike pattern classes were established using high-speed video that was concurrently collected. We could show that the classification of the strike types based on the measured accelerations and the extracted features was up to 95.3% accurate. The established classification system can be used to support runners, for example by giving running shoe recommendations that ideally match the prevailing strike type of the runner. View full abstract»

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  • Experts lift differently: Classification of weight-lifting athletes

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (453 KB) |  | HTML iconHTML  

    The process of learning a novel body movement exposes a student to multiple difficulties. Understanding the range of motion is fundamental for learning to control the involved body parts. Theory and instructions need to be mapped to body movements: a student not only needs to mimic or copy the range of motion of individual body parts, but he also needs to trigger the motion fragments in the correct order. Not only correct order is important, but also precise timing. If the movements in questions are intensified by additional load, optimality of the motion patterns becomes crucial. Sub-optimal execution of an exercise reduces the performance or can even induce failure of completion. Correct execution is a subtle interplay between the correct forces at the right times. In this paper, we present a sensor system that is able to categorize movements into multiple quality classes and athletes into two experience classes. For this work we conducted a study involving 16 athletes performing squat-presses, a simple yet non-trivial exercise requiring barbells. We calculated various features out of raw accelerometer data acquired by two inertial measurement units attached to the athletes' bodies. We evaluated exercise performances of the participants ranging from beginners to experts. We introduce the biomechanical properties of the movement and show that our system can differentiate between four quality classes (poor, fair, good, perfect) with an accuracy above 93% and discriminate between a beginner athlete and an advanced athlete with an accuracy of more than 94%. View full abstract»

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  • A long-term wearable electrocardiogram measurement system

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (166 KB) |  | HTML iconHTML  

    A low-power, wearable electrocardiogram (ECG) monitor was developed for long-term data acquisition and analysis. It was designed to maximize both comfort and ECG signal quality, and minimize obtrusiveness. The monitor consists of a central PCB that contains one electrode and most of the electronics. Two additional satellite PCBs house the remaining electrodes and buffer circuits and complete the system. It consumes 7.3 mW and can record single lead ECG for over one week under a variety of activity levels. A clinical test was performed to validate the monitor. Participants (N = 6) wore both the experimental cardiac monitor and a commercially available monitor while engaging in physical activities such as walking, stepping, and running. QRS sensitivity and QRS positive predictability were determined for each ECG waveform. The monitor performed as well or better than the commercial monitor in all interventions. It performed well even under high activity levels such as running, and may be a viable alternative to commercially available monitors. View full abstract»

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  • Smartphones for smart wheelchairs

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1334 KB) |  | HTML iconHTML  

    Individuals with limited ambulatory skills are at high risk for all physical inactivity-related diseases, such as coronary disease and diabetes. Increased physical activity can significantly lower risks of these diseases. However, quantifying recommendations for increased physical activity remain challenging for individuals who use wheelchairs for mobility. In this paper we introduce a smart wheelchair that utilizes a smartphone with its built-in sensors to capture and record physical activity of manual wheelchair users in both unstructured and structured environments. We develop algorithms for data acquisition and processing on the smartphone and implement them in an Android application called mWheelness. The application is successfully tested in laboratory and free-living experiments using several modern smartphones. View full abstract»

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  • A Hidden Markov Model of the breaststroke swimming temporal phases using wearable inertial measurement units

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    The recent advances in wearable inertial sensors opened a new horizon for pervasive measurement of human locomotion even in aquatic environment. In this paper we proposed an automatic approach of detecting the key temporal events of breaststroke swimming as a tentatively explored technique due to the complexity of the stroke. We used two inertial measurement units worn on the right arm and right leg of seven swimmers to capture the kinematics of the breaststroke. The detection of the temporal phases from the inertial signals was undertaken in the framework of a Hidden Markov Model (HMM). Supervised learning of the HMM parameters was achieved using the reference data from manual video analysis by an expert. The outputs of two well-known classifiers on the inertial signals were fused to unfold the input space of the HMM for an enhanced performance. An average correct phase detection of 93.5% for the arm stroke, 94.4% for the leg stroke and the minimum precision of 67 milliseconds in detection of the key events, suggests the accuracy of the method. View full abstract»

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  • A study on instance-based learning with reduced training prototypes for device-context-independent activity recognition on a mobile phone

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (459 KB) |  | HTML iconHTML  

    This paper presents a study of two simple methods for reducing the complexity of the instance-based classification technique and demonstrates their use in device-context independent activity recognition on a mobile phone. A projection-based method for signal rectification has been implemented on an iPhone in order to handle with variation in device orientations. The transformation matrix is estimated on a ten-second dynamic data buffer. To search for a suitable set of training prototypes for iPhone implementation, an activity recognition experiment is conducted with twenty different device contexts performed by eight subjects. With the developed mobile application, the recognition results along with the user's location can be displayed on both iPhone and the web application in real time. View full abstract»

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  • A MAC protocol for implanted devices communication in the MICS band

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (434 KB) |  | HTML iconHTML  

    Wireless Body Area Network (WBAN) is well known for accessing data from in body and on body devices. Extensive research has been done on Medium Access Control (MAC) protocol for WBAN for different unlicensed ISM bands, such as 868MHz, 915MHz, 2.4MHz and 433 MHz. However, due to vast use of those frequencies for other applications, there is an unavoidable risk for healthcare application. Inductive link method is another conventional way to communicate with an implanted device. But some limitations are also there, such as low data rate and communication range is very low (few centimeters). To emphasize a patient's health safety and to evade the limitation of inductive link method, a new and different frequency band, Medical Implant Communication System (MICS) band (402–405 MHz), has been accepted worldwide for a small range (3 meters) communication between an external device and implanted devices only. But, different international communication authorities have put some rules and restrictions to use the MICS band. Therefore, it is necessary to design a MAC protocol that will comply with the rules and suitable for the traffics of implanted devices communication network. In this paper, we propose a MAC protocol for MICS band considering the proposed rules and probable traffics in the network. A categorization of all the possible traffics is done. The MAC protocol is verified based on our proposed traffic categorization and it is observed that eight patients, each having eight implants, can be monitored simultaneously. View full abstract»

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  • Functional regression for data fusion and indirect measurements of physiological variables collected by wearable sensor systems and indirect calorimetry

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (300 KB) |  | HTML iconHTML  

    The paper describes application of different types of functional regression for analysis and modeling of the data collected by wearable sensor systems. The data have been recorded from human subjects while they were staying in whole room calorimeter chamber for 48 hours. This allowed very accurate measurements of their oxygen consumption, energy expenditure and substrate oxidation. These physiological parameters are notorious for their inaccuracy when measured in field conditions. The subjects wore two types of body sensors: the Hidalgo Equivital™ (Cambridge, UK) physiological monitors with a telemetry thermometer pill and iPro Professional Continuous Glucose Monitoring System (CGMS) (Medtronic MiniMed, Inc, Northridge, CA). The data collected by these two systems and by the calorimeter chamber were subsequently analyzed off-line using the functional regression techniques. The energy expenditure, substrate oxidation, and body core temperature were used as response variables, while heart rate, respiratory rate, subcutaneous glucose concentration, and skin temperature were used as predictors. The results show that the 24-hours and instantaneous energy expenditure values can be inferred from instantaneous measurements of heart rate, respiratory rate and glucose concentrations. Also, the body core temperature can be inferred from heart rate, respiratory rate, glucose concentration, and skin temperature. The substrate oxidation was the most difficult parameter to infer and it can only be accomplished during the exercise activity. View full abstract»

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  • A wearable sensor platform to monitor sweat pH and skin temperature

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (438 KB) |  | HTML iconHTML  

    This work presents a wearable sensing system, aimed to monitor sweat pH and skin temperature in a noninvasive way. The wireless interface and the body coupling via a smart textile make it particularly comfortable and unobtrusive for the wearer; the applications extend from high risks patients hydration monitoring in a home-care environment, to fitness and wellness applications. View full abstract»

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  • Development of a wireless low-power multi-sensor network for motion tracking applications

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (443 KB) |  | HTML iconHTML  

    This work presents a novel wireless and low power Attitude and Heading Reference Systems network based on low-cost MEMS (Micro Electro-Mechanical System) sensors, developed for motion tracking systems. Biomedical and rehabilitation purposes as well as gaming and consumer electronics may be the potential applications of this network. The paper aims to describe the hardware architecture, the embedded sensor fusion algorithm and the motion tracking system. View full abstract»

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  • Towards estimation of front-crawl energy expenditure using the wearable aquatic movement analysis system (WAMAS)

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (454 KB) |  | HTML iconHTML  

    Inertial measurement unit (IMU) is a promising tool in the quantification of energy expenditure for human on-land activities, though has never been deployed before to calculate the aquatic activities energy expenditure. Investigating the factors that influence the required energy in aquatic locomotion can help the biomecanicians to better understand the biophysics of swimming. We used a set of three waterproofed IMUs worn on the forearms and sacrum of twelve swimmers to estimate the front-crawl energy expenditure. The swimmers performed three 300-m trials at 70%, 80% and 90% of their 400-m personal best time. At the end of each 300-m the reference value of energy expenditure was measured based on indirect calorimetry and blood lactate concentration. The three IMUs were used to extract the main spatio-temporal determinants of the front-crawl energy expenditure. Extraction of these parameters using IMU was previously validated. We used a combination of a linear estimator and kernel smoother on the residuals of the linear part to derive the mapping between the spatio-temporal inputs and reference energy expenditure. The algorithm validation on test data shows a strong association between the estimated and reference energy expenditure (Spearman's rho = 0.97, p-value <0.001) and a high relative precision of 9.7%. View full abstract»

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  • Macroscopic porosity generation in outer hydrogel membranes to offset sensitivity loss in implantable glucose sensors

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (822 KB) |  | HTML iconHTML  

    The function of implantable glucose sensor is hindered by post-implantation effects such as biofouling and negative tissue responses both of which lead to permeability reducing fibrous encapsulation. Utilization of drug-eluting composite coatings based on dexamethasone-containing poly (lactic-co-glycolic) acid (PLGA) microspheres and poly (vinyl alcohol) (PVA) hydrogel matrix has been shown to suppress inflammation over a period of 1–3 months. Herein, it is shown that these coatings provide another auxiliary venue to offset the negative effects of protein adsorption through generation of macroscopic porosity following microsphere degradation. Long-term studies in serum have indicated that, while biofouling clogs the microporosity of the hydrogel, it has been offset by the generated macroscopic porosity following microsphere degradation. This resulted in a two-fold recovery in sensor sensitivity as compared to controls. These findings suggest that the use of macroscopic porosity can reduce biofouling-induced sensitivity losses, an approach synergistic with drug-delivery based methodologies to mitigate negative tissue responses. View full abstract»

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  • A low power miniaturized CMOS-based continuous glucose monitoring system

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (345 KB) |  | HTML iconHTML  

    This paper presents the design and fabrication of a highly-miniaturized system for continuous glucose monitoring which holds great promise for patients inflicted with diabetes mellitus. To achieve the realization of a truly implantable system, a variety of issues such as robust electrochemical sensor design, miniaturization of the electronic components and counteracting biofouling and negative tissue response need to be addressed. In this report, we present a highly-miniaturized transcutaneous continuous glucose monitoring system which holistically addresses the aforementioned tribulations associated with implantable devices. Specifically, a high performance amperometric electrochemical glucose sensor is integrated with custom designed complementary metal-oxide-semiconductor electronics. The fabricated electrochemical sensor is Clark-based, and employs stratification of five functional layers to achieve a linear response within the physiological range of glucose concentration (2–22 mM). Furthermore, the sensor is encased with a thick polyvinyl alcohol (PVA) hydrogel containing poly(lactic-co-glycolic acid) (PLGA) microspheres which provides continuous, localized delivery of dexamethasone utilized to combat inflammation and fibrosis. Such miniature size (0.665 mm2) and low power operation (140 μW) of the electronic system render it ideal for continuous glucose monitoring devices and other metabolic sensing systems. View full abstract»

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  • A wearable piezoelectric rotational energy harvester

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (517 KB) |  | HTML iconHTML  

    This paper discusses the operating principle of a rotational energy harvester for body motion with an eccentric proof mass. A mathematical analysis for the rotor motion under different excitations is performed and the gravitational and inertial operation explained. The transducing mechanism works on the principle of frequency up-conversion that is now widely used to harvest low frequency vibration more efficiently, and uses a piezoelectric beam and magnetic coupling. A miniaturized device with an overall size similar to that of a wristwatch is introduced. The fabrication is entirely done using standard milling and turning processes. Experimental results for this device show significant improvement in the attachment of the piezoelectric beam compared to a previous prototype. Furthermore, there is a good match between the magnetic forces and the proof mass for the tested excitations. A disadvantage of the miniaturized prototype is the higher stiffness of the piezo beam, preventing free oscillation after actuation. Modifications to counteract this problem are provided and experimentally validated. View full abstract»

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  • Application of a pedometer in a clinical setting: Is the number of walking steps predictive of changes in blood pressure?: Prediction of blood pressure changes in blod presure by a peadmeter

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    A pedometer is a popular wearable sensor used to enumerate walking steps taken per day and in this way determines the approximate distance traveled. In this study, we used blood pressure and walking step data, obtained from 48 patients in a home healthcare system, to investigate the effectiveness of the pedometer in a clinical setting. Changes in blood pressure and walking steps per day were compared. Our results indicate that walking, as a regular form of exercise, contributed to lowering of blood pressure. Thus the pedometer is useful for improving the quality of life of patients in the home healthcare setting. View full abstract»

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  • D2MAC: Dynamic delayed Medium Access Control (MAC) protocol with fuzzy technique for Wireless Body Area Networks

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (570 KB) |  | HTML iconHTML  

    Wireless Body Area Networks (WBAN) have emerged as an extension to conventional wireless sensor networks in recent years to comply with the needs in providing timely and effective response in healthcare as one of the many target applications such networks have. The traffic of a WBAN is diverse due to different monitoring tasks carried on by sensor nodes. It brings difficulty in how to efficiently organize the access to the medium for the dynamic and various generated traffic. This paper analyses the traffic diversity problem in WBAN for healthcare applications and proposes a dynamic delayed Medium Access Control (MAC) algorithm. A fuzzy logic system is used to incorporate both application and protocol related parameters of the real time traffic to make the backoff time produced in IEEE 802.15.4 MAC protocol traffic adaptive. The simulation results demonstrate a significant reliability in packet transmissions and decrease in the latency with no change in energy consumption level. View full abstract»

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  • Estimation of prosthetic knee angles via data fusion of implantable and wearable sensors

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (505 KB) |  | HTML iconHTML  

    In this work, we studied a combination of embedded magnetic measurement system in a knee prosthesis and wearable inertial sensors to estimate two knee joint rotations namely flexion-extension and internal-external rotations. The near optimal sensor configuration was designed for implantable measurement system, and linear estimators were used to estimate the mentioned angles. This system was separately evaluated in a mechanical knee simulator and the effect of the imposed Abduction-Adduction rotation was also studied on the angle estimations. To reduce the power consumption of the internal system, we reduced the sampling rate and duty cycled the implantable sensors. Then we compensated the lack of information via use of kinematic information from wearable sensors to provide accurate angle estimations. As long as this smart prosthesis is not implanted yet on a subject, the angles estimations from implantable sensors and wearable sensors are realistically simulated for four subjects. The simulated angle estimations were fed to the designed data fusion algorithms to boost the estimation performance. The results were considerably improved via use of Maximum Entropy Ordered Weighted Averaging (MEOWA) fusion for flexion angles, but not for internal-external angle estimations. View full abstract»

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  • On-bed monitoring for range of motion exercises with a pressure sensitive bedsheet

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (407 KB) |  | HTML iconHTML  

    This paper presents the design of an on-bed rehabilitation exercise monitoring system that utilizes a high density sensor bedsheet to evaluate active range of motion exercises. We propose and develop a novel framework to analyze the progression of pressure image sequences using manifold learning. The image sequences are reduced to a low dimensional subspace that can be measured against expected prior data for each of the rehabilitation exercises. We also present a metric to compare manifold similarities. Our experimental results on five on-bed exercises show that this system can accurately track compliance of patients to prescribed treatment programs. It allows physical therapists to evaluate how well patients adhere to the rehabilitation exercises. The system is convenient to setup, unobtrusive, and can be used for reliable, long term monitoring. View full abstract»

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  • On-bed monitoring for range of motion exercises with a pressure sensitive bedsheet

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (394 KB) |  | HTML iconHTML  

    This paper presents the design of an on-bed rehabilitation exercise monitoring system that utilizes a high density sensor bedsheet to evaluate active range of motion exercises. We propose and develop a novel framework to analyze the progression of pressure image sequences using manifold learning. The image sequences are reduced to a low dimensional subspace that can be measured against expected prior data for each of the rehabilitation exercises. We also present a metric to compare manifold similarities. Our experimental results on five on-bed exercises show that this system can accurately track compliance of patients to prescribed treatment programs. It allows physical therapists to evaluate how well patients adhere to the rehabilitation exercises. The system is convenient to setup, unobtrusive, and can be used for reliable, long term monitoring. View full abstract»

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  • Emerging spectrum regulation for Medical Body Area Network

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (118 KB) |  | HTML iconHTML  

    Medical Body Area Network (MBAN) technology is a promising solution to improve patient care outcomes and lower healthcare costs. To foster MBAN innovations, the FCC recently allocated dedicated spectrum for MBAN services. In this paper, we present a brief summary of the FCC MBAN rules. Then, we discuss the technical rationale for the selection of key parameters, such as frequency band selection, emission bandwidth limit, and transmission power limit. Finally, we provide link budget analysis and MBAN coexistence simulations to demonstrate that the FCC rules can meet the requirements of MBAN applications. View full abstract»

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  • Battery-less microdevices for Body Sensor/Actuator networks

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (809 KB) |  | HTML iconHTML  

    In this paper we discuss a novel approach to delivering wireless power to remote microdevices within Body Sensor/Actuator Networks. With higher energy budgets such devices could extent their functionality from purely diagnostic to therapeutic, and perform such operations as implant mechanical adjustment, drug release, microsurgery, or control of microfluidic valves and pumps. The method is based on ultrasonic power delivery, the novelty being that actuation is powered by ultrasound directly rather than via electrical form. The paper focuses on the main part of the system — a coupled mechanical oscillator driven by acoustic waves — and presents the first experimental results. Several issues related to the biomedical application of the system are also discussed. These include estimating acoustic power levels to avoid adverse bioeffects and tissue damage, as well as studying how the source-receiver misalignment (lateral and angular) affects the system performance. View full abstract»

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  • Quantifying Timed-Up-and-Go test: A smartphone implementation

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    Timed-Up-and-Go (TUG) is a simple, easy to administer, and frequently used test for assessing balance and mobility in elderly and people with Parkinson's disease. An instrumented version of the test (iTUG) has been recently introduced to better quantify subject's movements during the test. The subject is typically instrumented by a dedicated device designed to capture signals from inertial sensors that are later analyzed by healthcare professionals. In this paper we introduce a smartphone application called sTUG that completely automates the iTUG test so it can be performed at home. sTUG captures the subject's movements utilizing smartphone's built-in accelerometer and gyroscope sensors, determines the beginning and the end of the test and quantifies its individual phases, and optionally uploads test descriptors into a medical database. We describe the parameters used to quantify the iTUG test and algorithms to extract the parameters from signals captured by the smartphone sensors. View full abstract»

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  • Recognition of sleep dependent memory consolidation with multi-modal sensor data

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (934 KB) |  | HTML iconHTML  

    This paper presents the possibility of recognizing sleep dependent memory consolidation using multi-modal sensor data. We collected visual discrimination task (VDT) performance before and after sleep at laboratory, hospital and home for N=24 participants while recording EEG (electroencepharogram), EDA (electrodermal activity) and ACC (accelerometer) or actigraphy data during sleep. We extracted features and applied machine learning techniques (discriminant analysis, support vector machine and k-nearest neighbor) from the sleep data to classify whether the participants showed improvement in the memory task. Our results showed 60–70% accuracy in a binary classification of task performance using EDA or EDA+ACC features, which provided an improvement over the more traditional use of sleep stages (the percentages of slow wave sleep (SWS) in the 1st quarter and rapid eye movement (REM) in the 4th quarter of the night) to predict VDT improvement. View full abstract»

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  • Design considerations for a wearable sensor network that measures accelerations during Water-Ski jumping

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (546 KB) |  | HTML iconHTML  

    A remarkably high number of water-skiers suffer from injuries on the lower back and the lower extremity as a result of jumping. A possible explanation for this is the vertical forces that occur on the body during landing, caused by the large amount of deceleration at the moment the skier hits the water surface. The amplitude of the accelerations might be a reason for concern for juveniles participating in this type of sport, due to the vulnerability to high loads during growth. A wearable sensor system could inform both the skier and coach about the impact level encountered by young water-skiers. Pilot testing showed decelerations occurred far above those measured by a 5 g accelerometer system. High-frequency camera data and modeling showed multiples of 10 g can be expected during landing. Therefore, it is suggested that 100 g accelerometers are integrated into the proposed body sensor network design. View full abstract»

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