Bhargava Teja Nukala

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.

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.

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.

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.

Comparing nape vs. T4 placement for a mobile Wireless Gait Analysis sensor using the Dynamic Gait Index test

A comprehensive and quantified gait analysis is warranted for patients with balance disorders to prevent injury such as falls. We report here a custom-designed wireless-gait-analysis-sensor (WGAS) to perform functional gait analysis targeted for clinically evaluating balance disorders. We report here our first efforts to determine the optimal placements of the WGAS in normal subjects for movement differentiation, while minimizing outliers and data artifacts with improved sensor technology. Normal subjects performed a gold-standard Dynamic Gait Index (DGI) tests for fall risk assessment, while the WGAS was adhered to either the T4 or the nape area. Our WGAS consists of a 3-axis linear accelerometer and gyroscopes, and the real-time gait data was transmitted wirelessly to a nearby PC and statistical analysis was done on each of the 7 DGI test on 3 volunteers repeated five times for either placement. The data suggests T4 is the preferred WGAS placement for gait analysis.

Wireless power transfer (WPT) using strongly coupled magnetic resonance (SCMR) at 5.8 GHz for biosensors applications: a feasibility study by electromagnetic (EM) simulations

We report here a detailed 3-dimensional (3-D) electromagnetic (EM) simulation study on the feasibility of Wireless Power Transfer (WPT) using the strongly coupled magnetic resonance (SCMR) effect at 5.8 GHz for potential μm-scale biosensors applications. The tiny 110 μm x110 μm planar aluminum inductor coil is built on the silicon substrate as our miniaturized receiver coil, which has been designed and simulated by 3-D EM simulations and its EM data is consistent with the measured data from an advanced IBM/Global Foundries’ 0.18 μm complimentary metal-oxidesemiconductor (CMOS) silicon-on-insulator (SOI) process technology. By using small relay coils for an optimized four-coil WPT system to reach the SCMR condition, EM simulations show that one can increase the wireless power transfer between the transmitter coil to the miniature receiver coil by about 300% to 400% over the traditional 2-coil inductive resonant system at the 5.8 GHz ISM band, making SCMR quite attractive for implantable bioelectronics and biosensors applications, such as on cochlear implants, capsule endoscopy and pacemakers. Our study features the smallest receiver coil (about 330 times smaller in area) than the previously reported smallest receiver coil used in inductive coupling for wireless power transfer.

Gaits classification of normal vs. patients by wireless gait sensor and Support Vector Machine (SVM) classifier

Due to the serious concerns of fall risks for patients with balance disorders, it is desirable to be able to objectively identify these patients in real-time dynamic gait testing using inexpensive wearable sensors. In this work, we took a total of 49 gait tests from 7 human subjects (3 normal subjects and 4 patients), where each person performed 7 Dynamic Gait Index (DGI) tests by wearing a wireless gait sensor on the T4 thoracic vertebra. The raw gait data is wirelessly transmitted to a near-by PC for real-time gait data collection. To objectively identify the patients from the gait data, we used 4 different types of Support Vector Machine (SVM) classifiers based on the 6 features extracted from the raw gait data: Linear SVM, Quadratic SVM, Cubic SVM, and Gaussian SVM. The Linear SVM, Quadratic SVM and Cubic SVM all achieved impressive 98% classification accuracy, with 95.2% sensitivity and 100% specificity in this work. However, the Gaussian SVM classifier only achieved 87.8% accuracy, 71.7% sensitivity, and 100% specificity. The results obtained with this small number of human subjects indicates that in the near future, we should be able to objectively identify balance-disorder patients from normal subjects during real-time dynamic gaits testing using intelligent SVM classifiers.