Tam Q. Nguyen

A fall detection study on the sensors placement location and a rule-based multi-thresholds algorithm using both accelerometer and gyroscopes

Falls are dangerous among the elderly population and are a major health concern. Many investigators have reported the use of accelerometers for fall detection. In addition, the use of miniature gyroscopes has also been reported to be able to detect falls, but the effects of sensor placement on the back of a person have not been studied thoroughly. In this paper we present a simple solution for effective fall detection using both an accelerometer and two gyroscopes placed, as a single unit, on three different positions along the thoracic vertebrae (i.e., T-4, T-7, and T-10). Results indicated that T-10 was not a good location for the gyroscope placement for fall detection. However, both T-4 and T-7 were suitable, with the results for T-4 being slightly better. Using a simple rule-based multi-thresholds algorithm that utilizes the recorded resultant gravitational acceleration, angular change, angular velocity, and angular acceleration, we were able to successfully detect all 60 falls and differentiate between falls and activities of daily living (ADL) with no false positives on young volunteers. More testing data is needed, especially for backward falls, to test the robustness of our simple algorithm and to improve the sensor portability for future trial studies on geriatric populations.

A real-time fall detection system using a wearable gait analysis sensor and a Support Vector Machine (SVM) classifier

In this study, we report a custom designed wireless gait analysis sensor (WGAS) system for real-time fall detection using a Support Vector Machine (SVM) classifier. Our WGAS includes a tri-axial accelerometer, 2 gyroscopes and a MSP430 micro-controller. It was worn by the subjects at either the T4 or at the waist level for various intentional falls, Activities of Daily Living (ADL) and the Dynamic Gait Index (DGI) test. The raw data of tri-axial acceleration and angular velocity is wirelessly transmitted from the WGAS to a nearby PC, and then 6 features were extracted for fall classification using a SVM (Support Vector Machine) classifier. We achieved 98.8% and 98.7% fall classification accuracies from the data at the T4 and belt positions, respectively. Moreover, the preliminary data demonstrates an impressive overall specificity of 99.5% and an overall sensitivity of 97.0% for this WGAS real-time fall detection system.

An ultralow-power CMOS transconductor design with wide input linear range for biomedical applications

This paper presents an ultralow-power CMOS linear transconductor design operating in weak inversion for low frequency gm-C filter design for potential biomedical applications, where the transconductance should be low to reduce the capacitor size, and linear to minimize distortion. Bulk-driven and degeneration techniques are used, and we have adopted this Gm cell as a linear source degeneration resistor to achieve a 91% reduction in the transconductance value. In addition, a fourth-order Butterworth bandpass filter was designed in a proprietary 0.35-μm BCD (bipolar-CMOS-DMOS) process by Texas Instruments (TI). The SPICE simulation results indicate that the total harmonic distortion is greatly reduced to less than -71 dB at an input of 100 mV. The power consumption is only 750 nW at a 3-V supply voltage.

Engineering Challenges in Cochlear Implants Design and Practice

Since the first successful cochlear implantation in the early 1970s by the House group in Los Angeles, about 120,000 patients have received cochlear implants (CI) worldwide, with more every year [1]. The premise of using electrical stimulation on the sensory nerves, either for visual or acoustic perceptions, is not new. Attempts were made in the 19th century by several researchers with backgrounds in engineering and/or medicine. The only documented account for that period was one by Volta and it was an accidental observation when he applied an electrical current into his ear canals. When this current was applied, he reported he heard a bubbling or crackling sound. Many years later, the first human implant was performed by an engineer/physician team of Djourno and Eyries in 1937. Unfortunately, it was obscurely published, and at the brink of war was therefore largely neglected until the 1970s. The concept was then resurrected by the National Institutes of Health (NIH) with funding to support researchers in U.S. and subsequently in Australia and Europe.

A 2.4GHz Non-Contact Biosensor System for Continuous Monitoring of Vital-Signs

In this chapter, we present a novel Doppler-based vital signs biosensor that can monitor the respiration and heartbeat rates of a person remotely without the need of any obstructions like patches, cords, etc. We will discuss the sensor operation principle and present three generations of systems that were designed to accurately extract the respiration rate and heartbeat of subjects using the Doppler radar principle. The systems have been realized using discrete custom off the shelf (COTS) parts. The first generation of the biosensor system consisted of discrete RF components system and a bulky SRS560 amplifier and filter box. Later generations of sensors consisted of custom designed printed circuits boards (PCBs) for the Doppler transceiver and for performing the analog signal processing. The data obtained using these non-contact biosensor systems was processed and logged in real-time using a LabVIEW© Graphic User Interface (GUI). Digital signal processing extracts the vital signs by filtering, auto correlating and calculating the Discrete Fourier Transform (DFT) of the waveforms. A comparison of performance among the three different generations of sensors shows that a quadrature transceiver system using autocorrelation can extract the respiration rates and heartbeat rates most accurately. Our single PCB version of the biosensor system was found to perform as well as the system using bulky components and SRS560 box. Good data accuracy has been observed on the quadrature radar sensor system with mean detection errors for respiration rate within ~1 beat/min and for heart rate within ~3 beat/min. The continuous vital signs data measured from these portable sensors can also be wirelessly transferred to healthcare professionals to make life saving decisions and diagnosis of symptoms. In the future, our vision is that a continuous log of these vital signs info can also be used to remotely monitor and gauge the recovery of patients, and even for the prevention or prediction of severe illnesses and complications.

A phased array non-contact vital signs sensor with automatic beam steering

Doppler-based non-contact vital signs (NCVS) sensor systems have the ability to monitor heart and respiration rates of patients without physical contacts. Because the accuracy of a NCVS sensor can deteriorate quickly in a noisy or cluttered environment, and that patients confined on their beds have different physical sizes and microwave signatures and will still move naturally (though not frequently), continuous NCVS monitoring that can work well for all individuals is very difficult. Therefore, we have developed a highly directive phased-array antenna NCVS system that can perform automatic electronic beam steering for continuous NCVS monitoring with considerably improved monitoring accuracy over that obtained from the Doppler radar with a fixed beam. Our NCVS system includes an automatic beam steering algorithm, and has achieved heart rate measurement accuracy of nearly 95% within 5 beat-per-minute (BPM) vs. reference at our engineering lab.

Accurate and continuous non-contact vital signs monitoring using phased array antennas in a clutter-free anechoic chamber

Continuous and accurate monitoring of human vital signs is an important part of the healthcare industry, as it is the basic means by which the clinicians can determine the instantaneous status of their patients. Doppler-based noncontact vital signs (NCVS) sensor systems can monitor the heart and respiration rates without touching the patient, but it has been observed that that the accuracy of these NCVS sensors can be diminished by reflections from background clutters in the measurement environment, and that high directivity antennas can increase the sensing accuracy. Therefore, this work explores a NCVS sensor with continuous data taken inside an anechoic chamber where the background cluttering is negligible. In addition, a high directivity custom-made beam-steerable phased array antenna system is used to improve the performance and functionality of the 2.4GHz NCVS sensor we have built. We believe this work is the 1st systematic study using Doppler-based phased array systems for NCVS sensing performed in a clutter-free anechoic chamber.

A real-time robust fall detection system using a wireless gait analysis sensor and an Artificial Neural Network

This paper describes our custom-designed wireless gait analysis sensor (WGAS) system developed and tested for real-time fall detection. The WGAS is capable of differentiating falls vs. Activities of Daily Living (ADL) and the Dynamic Gait Index (DGI) performed by young volunteers using a Back Propagation Artificial Neural Network (BP ANN) algorithm. The WGAS, which includes a tri-axial accelerometer, 2 gyroscopes, and a MSP430 microcontroller is worn by the subjects at either T4 (at back) or the belt-clip positions (in front of the waist) for the various falls, ADL, and Dynamic Gait Index (DGI) tests. The raw data is wirelessly transmitted from the WGAS to a nearby PC for real-time fall classification, where six features were extracted for the BP ANN. Overall fall classification accuracies of 97.0% and 97.4% have been achieved for the data taken at the T4 and at the belt position, respectively. The preliminary data demonstrates an overall sensitivity of 97.0% and overall specificity of 97.2% for this WGAS fall detection system, showing good promise as a real-time low-cost and effective fall detection device for wireless acute care and wireless assisted living.

An ultra-low power interface CMOS IC design for biosensor applications

This paper presents a design example of an ultra-low power single-channel analog front-end (AFE) integrated circuits (IC) and system for biomedical sensing applications. The 0.18-μm CMOS AFE IC design includes a low noise instrumentation amplifier (INA), a low-pass filter (LPF), a variable gain amplifier (VGA), and a successive approximation register (SAR) analog-to-digital converter (ADC). The AFE IC architecture is analyzed on the system level to minimize total power consumption with high integration and optimized for an ECG sensing system. SPICE simulations of the AFE IC channel validate the ultra-low power IC design methodology for heartbeat detection with less than 1 μA/channel.

Real-Time Classification of Patients with Balance Disorders vs. Normal Subjects Using a Low-Cost Small Wireless Wearable Gait Sensor

Gait analysis using wearable wireless sensors can be an economical, convenient and effective way to provide diagnostic and clinical information for various health-related issues. In this work, our custom designed low-cost wireless gait analysis sensor that contains a basic inertial measurement unit (IMU) was used to collect the gait data for four patients diagnosed with balance disorders and additionally three normal subjects, each performing the Dynamic Gait Index (DGI) tests while wearing the custom wireless gait analysis sensor (WGAS). The small WGAS includes a tri-axial accelerometer integrated circuit (IC), two gyroscopes ICs and a Texas Instruments (TI) MSP430 microcontroller and is worn by each subject at the T4 position during the DGI tests. The raw gait data are wirelessly transmitted from the WGAS to a near-by PC for real-time gait data collection and analysis. In order to perform successful classification of patients vs. normal subjects, we used several different classification algorithms, such as the back propagation artificial neural network (BP-ANN), support vector machine (SVM), k-nearest neighbors (KNN) and binary decision trees (BDT), based on features extracted from the raw gait data of the gyroscopes and accelerometers. When the range was used as the input feature, the overall classification accuracy obtained is 100% with BP-ANN, 98% with SVM, 96% with KNN and 94% using BDT. Similar high classification accuracy results were also achieved when the standard deviation or other values were used as input features to these classifiers. These results show that gait data collected from our very low-cost wearable wireless gait sensor can effectively differentiate patients with balance disorders from normal subjects in real time using various classifiers, the success of which may eventually lead to accurate and objective diagnosis of abnormal human gaits and their underlying etiologies in the future, as more patient data are being collected.