|BM 1||Implementation of a Wireless ECG Acquisition SoC for IEEE 802.15.4 (ZigBee) Applications||This paper presents a wireless biosignal acquisition system-on-a-chip (WBSA-SoC) specialized for electrocardiogram (ECG) monitoring. The proposed system consists of three subsystems, namely, 1) the ECG acquisition node, 2) the protocol for standard IEEE 802.15.4 ZigBee system, and 3) the RF transmitter circuits. The ZigBee protocol is adopted for wireless communication to achieve high integration, applicability, and portability. A fully integrated CMOS RF front end containing a quadrature voltage-controlled oscillator and a 2.4-GHz low-IF (i.e., zero-IF) transmitter is employed to transmit ECG signals through wireless communication. The low-power WBSA-SoC is implemented by the TSMC 0.18-μm standard CMOS process. An ARM-based displayer with FPGA demodulation and an RF receiver with analog-to-digital mixed-mode circuits are constructed as verification platform to demonstrate the wireless ECG acquisition system. Measurement results on the human body show that the proposed SoC can effectively acquire ECG signals.||2015|
|BM 2||Low-Power Wireless ECG Acquisition and Classification System for Body Sensor Networks||A low-power biosignal acquisition and classification system for body sensor networks is proposed. The proposed system consists of three main parts: 1) a high-pass sigma delta modulatorbasedbiosignal processor (BSP) for signal acquisition and digitization, 2) a low-power, super-regenerative on–off keying transceiver for short-range wireless transmission, and 3) a digital signal processor (DSP) for electrocardiogram (ECG) classification. The BSP and transmitter circuits, which are the body-end circuits, can be operated for over 80 days using two 605 mAH zinc–air batteries as the power supply; the power consumption is 586.5 μW. As for the
radio frequency receiver and DSP, which are the receiving-end circuits
that can be integrated in smartphones or personal computers, power consumption is less than 1 mW. With a wavelet transformbased digital signal processing circuit and a diagnosis control by cardiologists, the accuracy of beat detection and ECG classification are close to 99.44% and 97.25%, respectively. All chips are fabricated in TSMC 0.18-μm standard CMOS process.
|BM 3||Fall Detection in Homes of Older Adults Using the Microsoft Kinect||A method for detecting falls in the homes of older adults using the Microsoft Kinect and a two-stage fall detection system is presented. The first stage of the detection system characterizes a person’s vertical state in individual depth image frames, and then segments on ground events from the vertical state time series obtained by tracking the person over time. The second stage uses an ensemble of decision trees to compute a confidence that a fall preceded on a ground event. Evaluation was conducted in the actual homes of older adults, using a combined nine years of continuous data collected in 13 partments. The dataset includes 454 falls, 445 falls performed by trained stunt actors and nine naturally occurring resident falls. The extensive data collection allows for characterization of system performance under real-world conditions to a degree that has not been shown in other studies. Cross validation results are included for standing, sitting, and lying down positions, near (within 4 m) versus far fall locations, and occluded versus not occluded fallers. The method is compared against five state-of-the-art fall detection algorithms and significantly better results are achieved.||2015|
|BM 4||Ambulatory Measurement of Three-Dimensional Foot Displacement During Treadmill Walking UsingWearable Wireless Ultrasonic Sensor Network||Techniques that could be used to monitor human motion precisely are helpful in various applications such as rehabilitation, gait analysis, and athletic performance analysis. This paper focuses on the 3-D foot trajectory measurements based on a wearable wireless ultrasonic sensor network. The system consists of an ultrasonic transmitter (mobile) and several receivers (anchors) with fixed known positions. In order not to restrict themovement of subjects, a radio frequency (RF) module is used for wireless data transmission. The RF module also provides the synchronization clock between mobile and anchors. The proposed system measures the time-of-arrival (TOA) of the ultrasonic signal from mobile to anchors. Together with the knowledge of the anchor’s position, the absolute distance that the signal travels can be computed. Then, the range information defines a circle centered at this anchor with radius equal to the measured distance, and the mobile resides within the intersections of several such circles. Based on the TOA-based tracking technique, the 3-D foot trajectories are validated against a camera-based motion capture system for ten healthy subjects walking on a treadmill at slow, normal, and fast speeds. The experimental results have shown that the ultrasonic system has sufficient accuracy of net root-mean-square error (4.2 cm) for 3-D displacement, especially for foot clearance with accuracy and standard deviation (0.62 ± 7.48 mm) compared to the camera-based motion capture system. The small form factor and lightweight feature of the proposed system make it easy to use. Such a system is also much lower in cost compared to the camera-based tracking system.||2015|
|BM 5||Genetic Algorithm-Based Classifiers Fusion for Multisensor Activity Recognition of Elderly People||Activity recognition of an elderly person can be used to provide information and intelligent services to health care professionals, carers, elderly people, and their families so that the elderly people can remain at homes independently. This study investigates the use and contribution of wrist-worn multisensors for activity recognition. We found that accelerometers are the most important sensors and heart rate data can be used to boost classification of activities with diverse heart rates.We propose a genetic algorithm-based fusion weight selection (GAFW) approach which utilizes GA to find fusion weights. For all possible classifier combinations and fusion methods, the study shows that 98% of times GAFWcan achieve equal or higher accuracy than the best classifier within the group.||2015|
|BM 6||SecourHealth: A Delay-Tolerant Security Framework for Mobile Health Data Collection||Security is one of the most imperative requirements for the success of systems that deal with highly sensitive data, suchas medical information. However, many existing mobile health solutions focused on collecting patients’ data at their homes that do not include security among their main requirements. Aiming to tackle this issue, this paper presents Secour Health, a lightweight security framework focused on highly sensitive data collection applications. SecourHealth provides many security services for both stored and in-transit data, displaying interesting features such as tolerance to lack of connectivity (a common issue when promoting health in remote locations) and the ability to protect data even if the device is lost/stolen or shared by different data collection agents.
Together with the system’s description and analysis, we also show how SecourHealth can be integrated into a real data collection solution currently deployed in the city of Sao Paulo, Brazil.
|BM 7||Aerial Obstacle Detection With 3-D Mobile Devices||In this paper, we present a novel approach for aerial obstacle detection (e.g., branches or awnings) using a 3-D smartphone in the context of the visually impaired (VI) people assistance. This kind of obstacles are especially challenging because they cannot be detected by thewalking stick or the guide dog.The algorithm captures the 3-D data of the scene through stereo vision. To our knowledge, this is the first work that presents a technology able to obtain real 3-D measures with smartphones in real time. The orientation sensors of the device (magnetometer and accelerometer) are used to approximate the walking direction of the user, in order to look for the obstacles only in such a direction. The obtained 3-D data are compressed and then linearized for detecting the potential obstacles. Potential obstacles are tracked in order to accumulate enough evidence to alert the user onlywhen a real obstacle is found. In the experimental section, we show the results of the algorithm in several situations using real data and helped by VI users.||2015|
|BM 8||Wearable, Wireless EEG Solutions in Daily Life Applications: What are we Missing?||Monitoring human brain activity has great potential in helping us understand the functioning of our brain, as well as inPreventing mental disorders and cognitive decline and improve our quality of life. Noninvasive surface EEG is the dominant modality for studying brain dynamics and performance in real-life interaction of humans with their environment. To take full advantage of surface EEG recordings, EEG technology has to be advanced to a level that it can be used in daily life activities. Furthermore, users have to see it as an unobtrusive option to monitor and improve their health. To achieve this, EEG systems have to be transformed from stationary, wired, and cumbersome systems used mostly in clinical practice today, to intelligent wearable, wireless, convenient, and comfortable lifestyle solutions that provide high signal quality.
Here, we discuss state-of-the-art in wireless and wearable EEG solutions and a number of aspects where such solutions require improvements when handling electrical activity of the brain. We address personal traits and sensory inputs, brain signal generation and acquisition, brain signal analysis, and feedback generation. We provide guidelines on how these aspects can be advanced further such that we can develop intelligent wearable, wireless, lifestyle EEG solutions. We recognized the following aspects as the ones that need rapid research progress: application driven design, end-user driven development, standardization and sharing of EEG data, and development of ophisticated approaches to handle
|BM 9||Improving Compliance in Remote Healthcare Systems Through Smartphone Battery Optimization||Remote health monitoring (RHM) has emerged as a solution to help reduce the cost burden of unhealthy lifestyles andaging populations. Enhancing compliance to prescribed medical regimens is an essential challenge to many systems, even those using smartphone technology. In this paper,we provide a technique to improve smartphone battery consumption and examine the effects of smartphone battery lifetime on compliance, in an attempt to enhance users’ adherence to remote monitoring systems. We deploy
WANDA-CVD, an RHM system for patients at risk of cardiovascular disease (CVD), using a wearable smartphone for detection of physical activity. We tested the battery optimization technique in an in-lab pilot study and validated its effects on compliance in the Women’s Heart Health Study. The battery optimization technique enhanced the battery lifetime by 192% on average, resulting in a 53% increase in compliance in the study. A system like WANDA-CVD can help increase smartphone battery lifetime for RHM systems monitoring physical activity.
|BM 10||A Remotely Powered Implantable Biomedical System With Location Detector||An universal remote powering and communication system is presented for the implantable medical devices. Thesystem be interfaced with different sensors or actuators. A mobile external unit controls the operation of the implantable chip and reads the sensor’s data. A locator system is proposed to align the mobile unit with the implant unit for the efficient magnetic power transfer. The location of the implant is detected with 6 mm resolution from the rectified voltage level at the implanted side. The rectified voltage level is fedback to the mobile unit to
adjust the magnetic field strength and maximize the efficiency of the remote powering system. The sensor’s data are transmitted by using a free running oscillator modulated with on-off key
scheme. To tolerate large data carrier drifts, a custom designed receiver is implemented for the mobile unit. The circuits have been fabricated in 0.18 um CMOS technology. The remote powering
link is optimized to deliver power at 13.56 MHz. On chip voltage regulator creates 1.8 V from a 0.9 V reference voltage to supply the sensor/actuator blocks. The implantable chip dissipates 595 W and requires 1.48 V for start up.
|BM 11||A Bendable and Wearable Cardiorespiratory Monitoring Device Fusing Two Noncontact Sensor Principles||A mobile device is presented for monitoring both respiration and pulse. The device is developed as a bendable/flexible inlay that can be placed in a shirt pocket or the inside pocket of a jacket. To achieve optimum monitoring performance, the device combines two sensor principles, which work in a safe noncontact way through several layers of cotton or other textiles. One sensor, based on magnetic induction, is intended for respiratory monitoring, and the other is a reflective photoplethysmography sensor intended for pulse detection. Because each sensor signal has some dependence on both physiological parameters, fusing the sensorsignals allows enhanced signal coverage.||2015|
|BM 12||Inertial Sensor-Based Stride Parameter Calculation From Gait Sequences in Geriatric Patients||A detailed and quantitative gait analysis can provide evidence of various gait impairments in elderly people. To provide an objective decision-making basis for gait analysis, simple applicable tests analyzing a high number of strides are required. A mobile gait analysis system, which is mounted on shoes, can fulfill these requirements. This paper presents a method for computing clinically relevant temporal and spatial gait parameters. Therefore, an accelerometer and a gyroscope were positioned laterally below each ankle joint. Temporal gait events were detected by searching for characteristic features in the signals. To calculate stride length, the gravity compensated accelerometer signal was double integrated, and sensor drift was modeled using a piece-wise defined linear function. The presented method was validated using GAITRite-based gait parameters from 101 patients (average age 82.1 years). Subjects performed a normal walking test with and without a wheeled walker. The parameters stride length and stride time showed a correlation of 0.93 and 0.95 between both systems. The absolute error of stride length was 6.26 cm on normal walking test. The developed system as well as the GAITRite showed an increased stride length, when using a four-wheeled walker as walking aid. However, the walking aid interfered with the automated analysis of the GAITRite system, but not with the inertial sensorbased approach. In summary, an algorithm for the calculation of clinically relevant gait parameters derived from inertial sensors is applicable in the diagnostic workup and also during long-term monitoring approaches in the elderly population.||2015|
|BM 13||Signal Quality Measures on Pulse Oximetry and Blood Pressure Signals Acquired from Self-Measurement in a Home Environment||Recently, decision support system (DSSs) have become more widely accepted as a support tool for use with telehealth systems, helping clinicians to summarize and digest what would otherwise be an unmanageable volume of data. One of the pillars of a home telehealth system is the performance of unsupervised physiological self-measurement by patients in their own homes. Such measurements are prone to error and noise artifact, often due to poor measurement technique and ignorance of the measurement and transduction principles at work. These errors can degrade the quality of the recorded signals and ultimately degradethe performance of the DSS system, which is aiding the clinician in their management of the patient. Developed algorithms for automated quality assessment for pulse oximetry and blood pressure (BP) signals were tested retrospectively with data acquired from a trial that recorded signals in a home environment. The trial involved four aged subjects who performed pulse oximetry and BP measurements by themselves at their home for ten days,
three times per day. This trial was set up to mimic the unsupervised physiological self-measurement as in a telehealth system. A manually annotated “gold standard” (GS) was used as the reference against which the developed algorithms were evaluated after analyzing the recordings. The assessment of pulse oximetry signals shows 95% of good sections and 67% of noisy sections were correctly detected by the developed algorithm, and a Cohen’s Kappa coefficient (κ) of 0.58 was obtained in 120 pooled signals. The BP measurement evaluation demonstrates that 75% of the actual noisy sections were correctly classified in 120 pooled signals, with 97% and 91% of the signals correctly identified as worthy of attempting systolic and/or diastolic pressure estimation, respectively, with a mean error and standard deviation of 2.53 ± 4.20 mmHg and 1.46 ± 5.29 mmHg when compared to a manually annotated GS. These results demonstrate the feasibility, and highlight the potential benefit, of incorporating automated signal quality assessment algorithms for pulse oximetry and BP recording within a DSS for telehealth patient management.
|BM 14||Complexity Index From a Personalized Wearable Monitoring System for Assessing Remissionin Mental Health||This study discusses a personalized wearable monitoring system, which provides information and communicationtechnologies to patients with mental disorders and physiciansmanaging
such diseases. The system, hereinafter called the PSYCHE system, ismainly comprised of a comfortable t-shirtwith embedded sensors, such as textile electrodes, to monitor electro cardiogramheart rate variability (HRV) series, piezoresistive sensors for respiration activity, and triaxial accelerometers for activity recognition. Moreover, on the patient-side, the PSYCHE system uses a
smartphone-based interactive platform for electronicmood agenda and clinical scale administration, whereas on the physician-side provides data visualization and support to clinical decision. The smartphone collects the physiological and behavioral data and sends the information out to a centralized server for further processing. In this study, we present experimental results gathered from ten bipolar patients, wearing the PSYCHE system, with severe symptoms who exhibited mood states among depression (DP), hypomania(HM), mixed state (MX), and euthymia (EU), i.e., the good affective balance. In analyzing more than 400 h of cardiovascular dynamics, we found that patients experiencing mood transitions
from a pathological mood state (HM, DP, or MX—where depressive and hypomanic symptoms are simultaneously present) to EU can be characterized through a commonly used measure of entropy. In particular, the SampEn estimated on long-term HRV series increases according to the patients’ clinical improvement. These results are in agreement with the current literature reporting on the complexity dynamics of physiological systems and provides a promising and viable support to clinical decision in order to improve the diagnosis and management of psychiatric disorders.
|BM 15||Home Telemonitoring of Vital Signs—Technical Challenges and Future Directions||The telemonitoring of vital signs from the home is an essential element of telehealth services for the management of patients with chronic conditions, such as congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD), diabetes,or poorly controlled hypertension. Telehealth is now being deployed widely in both rural and urban settings, and in this paper, we discuss the contribution made by biomedical instrumentation, user interfaces, and automated risk stratification algorithms in developing
a clinical diagnostic quality longitudinal health record at home. We identify technical challenges in the acquisition of highquality biometric signals fromunsupervised patients at home, identify new technical solutions and user interfaces, and propose new
measurement modalities and signal processing techniques for increasing
the quality and value of vital signs monitoring at home. We also discuss use of vital signs data for the automated risk stratification of patients, so that clinical resources can be targeted to those most at risk of unscheduled admission to hospital. New research is also proposed to integrate primary care, hospital, personal genomic, and telehealth electronic health records, and apply predictive analytics and data mining for enhancing clinical decision support.
|BM 16||Mimo Pillow—An Intelligent Cushion Designed With Maternal Heart Beat Vibrations for Comforting Newborn Infants||Premature infants are subject to numerous interventions ranging froma simple diaper change to surgery while residing in neonatal intensive care units. These neonates often suffer from pain, distress, and discomfort during the first weeks of their lives. Although pharmacological pain treatment often is available, it cannot always be applied to relieve a neonate from pain or discomfort. This paper describes a nonpharmacological solution, calledMimo, which provides comfort through mediation of a parent’s physiological features to the distressed neonate via an intelligent pillowsystem embeddedwith sensing and actuating functions.We present the design, the implementation, and the evaluation of the prototype. Clinical tests at M´axima Medical Center in the Netherlands show that among the nine of ten infants who showed discomfort following diaper change, a shorter recovery time to baseline skin conductance analgesimeter values could be measured when the maternal heartbeat vibration in the Mimo was switched ON and in seven of these ten a shorter crying time was measured.|
|BM 17||Performance-Power Consumption Tradeoff in Wearable Epilepsy Monitoring Systems||Automated seizure detection methods can be used to reduce time and costs associated with analyzing large volumes of ambulatory EEG recordings. These methods however have to rely on very complex, power hungry algorithms, implemented on the system backend, in order to achieve acceptable levels of accuracy. In size, and therefore power-constrained EEG systems, an alternativeapproach to the problem of data reduction is online data selection, in which simpler algorithms select potential epileptiform activity for discontinuous recording but accurate analysis is still left to a medical practitioner. Such a diagnostic decision support system would still provide doctors with information relevant for diagnosis while reducing the time taken to analyze the EEG. For wearable systems with limited power budgets, data selection algorithm must be of sufficiently low complexity in order to reduce the amount of data transmitted and the overall power consumption. In this paper, we present a low-power hardware implementation of an online epileptic seizure data selection algorithm with encryption and data transmission and demonstrate the tradeoffs between its
accuracy and the overall system power consumption. We demonstrate
that overall power savings by data selection can be achieved by transmitting less than 40% of the data. We also show a 29% power reduction when selecting and transmitting94%of all seizure events and only 10% of background EEG.
|BM 18||Development of a Wireless Oral-Feeding Monitoring System for Preterm Infants||Oral-feeding disorder is common in preterm infants. It not only shows the adverse effect for growth and neurodevelopment in clinical but also becomes one of the important indicatorsof high-risk group for neurodevelopment delay in preterm infants. Preterm infants must coordinate the motor patterns of sucking, swallowing, and respiration skillfully to avoid choking, aspiration,
oxygen desaturation, bradycardia, or apnea episodes. However, up to now, the judgment and classification severity in preterm infants are mostly subjective and phasic evaluations. Directly monitoring
the coordination of sucking–swallowing–breathing during oral feeding simultaneously is difficult for preterm infants. In this study, we proposed a wireless oral-feeding monitoring system for preterm infants to quantitatively monitor the sucking pressure via a designed sucking pressure sensing device, swallowing activity
via a microphone to detect swallowing sound, and diaphragmatic breathing movement via surface electro myogram. Moreover, a sucking–swallowing–breathing detection algorithm is also proposed to evaluate the events of sucking–swallowing–breathing activities. Furthermore, verification of the accuracy and rationality of oral-feeding parameters with clinical findings including sucking,
swallowing, and breathing in term and preterm infants had proved the practicality and value of the proposed system.
|BM 19||Imitation of Dynamic Walking With BSN for Humanoid Robot.||Humanoid robots have been used in a wide range of applications including entertainment, healthcare, and assistiveliving. In these applications, the robots are expected to perform a range of natural body motions, which can be either preprogrammed or learnt from human demonstration. This paper proposes a strategy for imitating dynamic walking gait for a humanoid robot by formulating the problem as an optimization process. The human motion data are recorded with an inertial sensor-based motion tracking system (Biomotion+). Joint angle trajectories are obtained from the transformation of the estimated posture. Key locomotion frames corresponding to gait events are chosen from the trajectories. Due to differences in joint structures of the human and robot, the joint angles at these frames need to be optimized to satisfy the physical constraints of the robot while preserving
robot stability. Interpolation among the optimized angles is needed to generate continuous angle trajectories. The method is validated using a NAO humanoid robot, with results demonstrating the ffectiveness
of the proposed strategy for dynamic walking.