Steven Zupancic

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.

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 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.

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.

Evidence indicates that cochlear implantation in children with auditory neuropathy spectrum disorder results in favorable outcomes; however, the quality of evidence is weak

This review provides a summary and appraisal commentary on the treatment review by Humphriss, R., Hall, A., Maddocks, J., Macleod, J., Sawaya, K., & Midgley, E. (2013). Does cochlear implantation improve speech recognition in children with auditory neuropathy spectrum disorder? A systematic review. International Journal of Audiology, 52, 442–454. Source of funding and declaration of interests: Authors (RH, JM, and EM) declared association within a cochlear implant program in the United Kingdom.