Binh Q. Tran

In-home wireless monitoring of physiological data for heart failure patients

The current project proposes an integrated system (hardware and software) for real-time, wireless, remote acquisition of cardiac and other physiologic information from HF patients while in the home environment. Transducers for measurement of electrocardiogram (ECG), heart rate variability (HRV), acoustical data are embedded into patient clothing for unobtrusive monitoring for early, sensitive detection of changes in physiologic status. Sampling rate for this system is 1 kHz per channel. Signal conditioning is performed in hardware by the patient wearable system, after which information is wirelessly transmitted to a central server located elsewhere in the home for signal processing, data storage, and data trending. The dynamic frequency ranges for the ECG and heart sounds (HS) are 0.05-160 Hz and 35-1350 Hz, respectively. The range-of-operation for the current patient-wearable physiologic data capture design is 100/spl plusmn/10 feet with direct line-of-sight to the home server station. Weight measurements are obtained directly by the in-home medical server using a digital scale. Physiologic information (ECG, HRV, HS, and weight) are dynamically analyzed using a combination o the LabVIEW (National Instruments, Inc.; Austin, TX) and MATLAB (MathWorks, Inc.; Inc; Natick, MA) software strategies. Software-based algorithms detect out-of-normal or alarm conditions for HR and weight as defined by the health care provider, information critical for HF patients. Health care professionals can remotely access vital data for improved management of heart failure.

Optimization of an Accelerometer and Gyroscope-Based Fall Detection Algorithm

Falling is a common and significant cause of injury in elderly adults (>65 yrs old), often leading to disability and death. In the USA, one in three of the elderly suffers from fall injuries annually. This study’s purpose is to develop, optimize, and assess the efficacy of a falls detection algorithm based upon a wireless, wearable sensor system (WSS) comprised of a 3-axis accelerometer and gyroscope. For this study, the WSS is placed at the chest center to collect real-time motion data of various simulated daily activities (i.e., walking, running, stepping, and falling). Tests were conducted on 36 human subjects with a total of 702 different movements collected in a laboratory setting. Half of the dataset was used for development of the fall detection algorithm including investigations of critical sensor thresholds and the remaining dataset was used for assessment of algorithm sensitivity and specificity. Experimental results show that the algorithm detects falls compared to other daily movements with a sensitivity and specificity of 96.3% and 96.2%, respectively. The addition of gyroscope information enhances sensitivity dramatically from results in the literature as angular velocity changes provide further delineation of a fall event from other activities that may also experience high acceleration peaks.

Home automation to promote independent living in elderly populations

A toolkit for independent living has been developed to monitor activity and automate daily tasks and routines for elderly persons living at home. Off-the-shelf components manufactured by x10, Inc., which operate via radio frequency and power line carrier technology, can be integrated into existing living environments to promote home security, home safety, and independent living. Accompanying software has been developed to passively monitor and trend activity patterns within the home in order to predict changes in health status and early onset of chronic illness. The independent living toolkit serves to promote health maintenance and active engagement within the aging population.

Smartphone household wireless electroencephalogram hat

Rudimentary brain machine interface has existed for the gaming industry. Here, we propose a wireless, real-time, and smartphonebased electroencephalogram (EEG) system for homecare applications. The systemuses high-density dry electrodes and compressive sensing strategies to overcome conflicting requirements between spatial electrode density, temporal resolution, and spatiotemporal throughput rate. Spatial sparseness is addressed by close proximity between active electrodes and desired source locations and using an adaptive selection of N active among 10N passive electrodes to form m-organized random linear combinations of readouts, m ≪ N ≪ 10N. Temporal sparseness is addressed via parallel frame differences in hardware. During the design phase, we took tethered laboratory EEG dataset and applied fuzzy logic to compute (a) spatiotemporal average of larger magnitude EEG data centers in 0.3 second intervals and (b) inside brainwave sources by Independent Component Analysis blind deconvolution without knowing the impulse response function. Our main contributions are the fidelity of quality wireless EEG data compared to original tethered data and the speed of compressive image recovery. We have compared our recovery of ill-posed inverse data against results using Block Sparse Code. Future work includes development of strategies to filter unwanted artifact from high-density EEGs (i.e., facial muscle-related events and wireless environmental electromagnetic interferences).

Detection of Activities Daily Living and Falls Using Combination Accelerometer and Gyroscope

This paper studied the detection of falls and activities of daily living (ADLs) with the objective: to automatically monitor health situation and prevent the elder out of injury from fallings. In this study, a wireless sensor system (WSS), based on accelerometer and gyroscope, is placed at the centre of the chest to collect real-time ADLs and fall data. The WSS contains a set of ADXL345 (3-axis digital accelerometer sensor), ITG3200 (3-axis digital gyroscope sensor), MCU LPC17680 (ARM 32-bit cortex M3), and Wi-Fi module RN131. Experiment protocols consisting of four types of falls such as forward fall, backward fall, and side way fall (left and right), and ADLs such as standing, walking, sitting down/ standing up, stepping, running along with normal gait involved 324 tests on 18 human subjects.

Brain order disorder 2nd group report of f-EEG

Since the Brain Order Disorder (BOD) group reported on a high density Electroencephalogram (EEG) to capture the neuronal information using EEG to wirelessly interface with a Smartphone [1,2], a larger BOD group has been assembled, including the Obama BRAIN program, CUA Brain Computer Interface Lab and the UCSD Swartz Computational Neuroscience Center. We can implement the pair-electrodes correlation functions in order to operate in a real time daily environment, which is of the computation complexity of O(N3) for N=102~3 known as functional f-EEG. The daily monitoring requires two areas of focus. Area #(1) to quantify the neuronal information flow under arbitrary daily stimuli-response sources. Approach to #1: (i) We have asserted that the sources contained in the EEG signals may be discovered by an unsupervised learning neural network called blind sources separation (BSS) of independent entropy components, based on the irreversible Boltzmann cellular thermodynamics(ΔS < 0), where the entropy is a degree of uniformity. What is the entropy? Loosely speaking, sand on the beach is more uniform at a higher entropy value than the rocks composing a mountain – the internal binding energy tells the paleontologists the existence of information. To a politician, landside voting results has only the winning information but more entropy, while a non-uniform voting distribution record has more information. For the human’s effortless brain at constant temperature, we can solve the minimum of Helmholtz free energy (H = E − TS) by computing BSS, and then their pairwise-entropy source correlation function. (i) Although the entropy itself is not the information per se, but the concurrence of the entropy sources is the information flow as a functional-EEG, sketched in this 2nd BOD report. Area #(2) applying EEG bio-feedback will improve collective decision making (TBD). Approach to #2: We introduce a novel performance quality metrics, in terms of the throughput rate of faster (Δt) & more accurate (ΔA) decision making, which applies to individual, as well as team brain dynamics. Following Nobel Laureate Daniel Kahnmen’s novel “Thinking fast and slow”, through the brainwave biofeedback we can first identify an individual’s “anchored cognitive bias sources”. This is done in order to remove the biases by means of individually tailored pre-processing. Then the training effectiveness can be maximized by the collective product Δt * ΔA. For Area #1, we compute a spatiotemporally windowed EEG in vitro average using adaptive time-window sampling. The sampling rate depends on the type of neuronal responses, which is what we seek. The averaged traditional EEG measurements and are further improved by BSS decomposition into finer stimulus-response source mixing matrix [A] having finer & faster spatial grids with rapid temporal updates. Then, the functional EEG is the second order co-variance matrix defined as the electrode-pair fluctuation correlation function C(s~, s~’) of independent thermodynamic source components. (1) We define a 1-D Space filling curve as a spiral curve without origin. This pattern is historically known as the Peano-Hilbert arc length a. By taking the most significant bits of the Cartesian product a≡ O(x * y * z), it represents the arc length in the numerical size with values that map the 3-D neighborhood proximity into a 1-D neighborhood arc length representation. (2) 1-D Fourier coefficients spectrum have no spurious high frequency contents, which typically arise in lexicographical (zig-zag scanning) discontinuity [Hsu & Szu, “Peano-Hilbert curve,” SPIE 2014]. A simple Fourier spectrum histogram fits nicely with the Compressive Sensing CRDT Mathematics. (3) Stationary power spectral density is a reasonable approximation of EEG responses in striate layers in resonance feedback loops capable of producing a 100, 000 neuronal collective Impulse Response Function (IRF). The striate brain layer architecture represents an ensemble <IRF< e.g. at V1-V4 of Brodmann areas 17-19 of the Cortex, i.e. stationary Wiener-Kintchine-Einstein Theorem. Goal#1: functional-EEG: After taking the 1-D space-filling curve, we compute the ensemble averaged 1-D Power Spectral Density (PSD) and then make use of the inverse FFT to generate f-EEG. (ii) Goal#2 individual wellness baseline (IWB): We need novel change detection, so we derive the ubiquitous fat-tail distributions for healthy brains PSD in outdoor environments (Signal=310°C; Noise=27°C: SNR=310/300; 300°K=(1/40)eV). The departure from IWB might imply stress, fever, a sports injury, an unexpected fall, or numerous midnight excursions which may signal an onset of dementia in Home Alone Senior (HAS), discovered by telemedicine care-giver networks. Aging global villagers need mental healthcare devices that are affordable, harmless, administrable (AHA) and user-friendly, situated in a clothing article such as a baseball hat and able to interface with pervasive Smartphones in daily environment.

Standards and Guidelines for Personal Health Records in the United States: Finding Consensus in a Rapidly Evolving and Divided Environment

Health care spending in the U.S. over the past decade has dramatically increased over the past decade and is expected to rise to 20% of GDP in the United States by the end of the decade. In 2010, the U.S. Congress approved the Health Care Reform Act with a critical component of this legislation being the adoption of health information technologies (i.e. EMRs, EHRs, PHRs, etc.) aimed at transforming the health care system. Consumer-driven health care models are seen as essential to control the escalating costs of health care. The objective of this research is to canvas the landscape of existing standards and guidelines relating to electronic personal health records (PHRs) and to evaluate the level of adoption of these standards and guidelines amongst current vendors. Through this effort, we propose a consensus standard for PHRs consisting of 14 data components, 11 of which should be essential for all PHRs and 3 additional recommended data components. Through a survey of existing PHR vendors, we observe a low level of adoption of existing standards in commercially available PHR products and note a wide variation (36-73%) of inclusion of critical and desired data components. Further, we propose 4 key features and services based upon a review of the existing literature to facilitate consumer adoption and to improve usefulness of PHRs. By proposing a consensus standard for PHR data components and features, we seek to provide clarity to developers and vendors of HIT products to facilitate product development, interoperability, and integration and data exchange with existing EMR/EHR products

Home care technologies for promoting successful aging in elderly populations

Advancements in sensor, telecommunications, computer, and information technologies present opportunities for providing at-home health care and other services required by Americas rapidly aging population. The HomeCare & Telerehabilitation Technology Center at The Catholic University of America has been investigating and evaluating current and emerging technologies for applications to care for elderly populations. This paper discusses a spectrum of technologies for interactive communications, physiologic monitoring, health and safety monitoring, health data collection, as well as storage and archiving. An integrated approach to combine and implement these technologies in the home environment may serve to promote successful aging in the elderly by avoiding disease, by maintaining cognitive and physical function, and by enabling lifelong engagement.

Training Future Providers in Home Care and Telehealth Technologies: A Collaborative Effort between Nursing and Biomedical Engineering

Trends in telehealth technologies have promoted a unique collaboration between nursing and biomedical engineering faculty in a training program at the Catholic University of America. The goalof the program is to prepare future nurses and biomedical engineers to collaborate in providing improved health care services for the elderly living at home. Nursing faculty provide graduate biomedical engineering students with information on nursing assessment, clinical foundations, and care of the elderly as a knowledge base to improve their designs of technology for this population. In return, undergraduate nursing students are given the opportunity to obtain a hands-on experience with new and emerging telehealth technologies in home care and to expand their therapeutic communication skills.

Utility of mass-market technologies to enable care provided by laypersons in the home environment

Tele-consultation systems for evaluating support of the caregivers of stroke patients are currently being evaluated by a team of engineers and clinicians at our institution. One proposed system utilizes low-cost commercial tele-conferencing and vital signs monitoring solutions to improve access to care for in-home stroke patients and to provide educational and training support for their caregivers. Preliminary assessment of existing equipment indicates that quality of care for patients and caregiver support may be limited by the bandwidth of conventional telephone lines, but can be improved with increasing data transmission speeds.