IMPLEMENTATION OF A SMARTPHONE WIRELESS GYROSCOPE PLATFORM WITH MACHINE LEARNING FOR CLASSIFYING DISPARITY OF A HEMIPLEGIC PATELLAR TENDON REFLEX PAIR

The patellar tendon reflex response provides fundamental means of assessing a subject’s neurological health. Dysfunction regarding the characteristics of the reflex response may warrant the escalation to more advanced diagnostic techniques. Current strategies involve the manual elicitation of the patellar tendon reflex by a highly skilled clinician with subsequent interpretation according to an ordinal scale. The reliability of the ordinal scale approach is a topic of contention. Highly skilled clinicians have been in disagreement regarding even the observation of asymmetric reflex pairs. An alternative strategy incorporated the ubiquitous smartphone with a software application to function as a wireless gyroscope platform for quantifying the reflex response. Each gyroscope signal recording of the reflex response can be conveyed wirelessly through Internet connectivity as an email attachment. The reflex response is evoked through a potential energy impact pendulum that enables prescribed targeting and potential energy level. The smartphone functioning as a wireless gyroscope platform reveals an observationally representative gyroscope signal of the reflex response. Three notably distinguishable attributes of the reflex response are incorporated into a feature set for machine learning: maximum angular rate of rotation, minimum angular rate of rotation, and time disparity between maximum and minimum angular rate of rotation. Four machine learning platforms such as the J48 decision tree, K-nearest neighbors, logistic regression, and support vector machine, were applied to the patellar tendon reflex response feature set incorporating a hemiplegic patellar tendon reflex pair. The J48 decision tree attained 98% classification accuracy, and the K-nearest neighbors, logistic regression, and support vector machine achieved perfect classification accuracy for distinguishing between a hemiplegic affected leg and unaffected leg patellar tendon reflex pair. The research findings reveal the potential of machine learning for enabling advanced diagnostic acuity respective of the gyroscope signal of the patellar tendon reflex response.

ADVANCES REGARDING POWERED PROSTHESIS FOR TRANSTIBIAL AMPUTATION

The necessity for developing advanced prostheses are apparent in light of projections that the forecast for the number of people enduring amputation will double by the year 2050. The transtibial powered prosthesis that enables positive mechanical work about the ankle during the powered plantar flexion aspect of stance phase constitutes a paradigm shift in available transtibial prostheses. The objective of the review is to advocate the state of the art regarding the transtibial powered prosthesis. The historic origins of the prosthesis and motivations for amputation are clarified. The phases of gait and the compensatory mechanisms and asymmetries inherent with passive transtibial prostheses are described. The three general classes of transtibial prosthesis (passive, energy storage and return and powered prostheses) are defined. Subsystems that are integral to the powered prosthesis are explained, such as the series elastic actuator and control architecture. Gait analysis systems and their role for the test and evaluation of energy storage and return and powered prostheses are demonstrated. Future advanced concepts; such as the integration of titin into novel muscle models that account for force enhancement and force depression including their implications for cutting edge bio-inspired actuators are elucidated. The review accounts for the evolution of the prosthetic device with regards to the scope of transtibial amputation and assesses the current state-of-the-art.

Implementation of a smartphone as a wireless gyroscope application for the quantification of reflex response

The patellar tendon reflex constitutes a fundamental aspect of the conventional neurological evaluation. Dysfunctional characteristics of the reflex response can augment the diagnostic acuity of a clinician for subsequent referral to more advanced medical resources. The capacity to quantify the reflex response while alleviating the growing strain on specialized medical resources is a topic of interest. The quantification of the tendon reflex response has been successfully demonstrated with considerable accuracy and consistency through using a potential energy impact pendulum attached to a reflex hammer for evoking the tendon reflex with a smartphone, such as an iPhone, application representing a wireless accelerometer platform to quantify reflex response. Another sensor integrated into the smartphone, such as an iPhone, is the gyroscope, which measures rate of angular rotation. A smartphone application enables wireless transmission through Internet connectivity of the gyroscope signal recording of the reflex response as an email attachment. The smartphone wireless gyroscope application demonstrates considerable accuracy and consistency for the quantification of the tendon reflex response.

Use of smartphones and portable media devices for quantifying human movement characteristics of gait, tendon reflex response, and Parkinson's disease hand tremor

Smartphones and portable media devices are both equipped with sensor components, such as accelerometers. A software application enables these devices to function as a robust wireless accelerometer platform. The recorded accelerometer waveform can be transmitted wireless as an e-mail attachment through connectivity to the Internet. The implication of such devices as a wireless accelerometer platform is the experimental and post-processing locations can be placed anywhere in the world. Gait was quantified by mounting a smartphone or portable media device proximal to the lateral malleolus of the ankle joint. Attributes of the gait cycle were quantified with a considerable accuracy and reliability. The patellar tendon reflex response was quantified by using the device in tandem with a potential energy impact pendulum to evoke the patellar tendon reflex. The acceleration waveform maximum acceleration feature of the reflex response displayed considerable accuracy and reliability. By mounting the smartphone or portable media device to the dorsum of the hand through a glove, Parkinson's disease hand tremor was quantified and contrasted with significance to a non-Parkinson's disease steady hand control. With the methods advocated in this chapter, any aspect of human movement may be quantified through smartphones or portable media devices and post-processed anywhere in the world. These wearable devices are anticipated to substantially impact the biomedical and healthcare industry.

Implementation of an iPhone wireless accelerometer application for the quantification of reflex response

The patellar tendon reflex represents an inherent aspect of the standard neurological evaluation. The features of the reflex response provide initial perspective regarding the status of the nervous system. An iPhone wireless accelerometer application integrated with a potential energy impact pendulum attached to a reflex hammer has been successfully developed, tested, and evaluated for quantifying the patellar tendon reflex. The iPhone functions as a wireless accelerometer platform. The wide coverage range of the iPhone enables the quantification of reflex response samples in rural and remote settings. The iPhone has the capacity to transmit the reflex response acceleration waveform by wireless transmission through email. Automated post-processing of the acceleration waveform provides feature extraction of the maximum acceleration of the reflex response ascertained after evoking the patellar tendon reflex. The iPhone wireless accelerometer application demonstrated the utility of the smartphone as a biomedical device, while providing accurate and consistent quantification of the reflex response.

AUTOMATED IDENTIFICATION OF EPILEPTIC AND ALCOHOLIC EEG SIGNALS USING RECURRENCE QUANTIFICATION ANALYSIS

Epilepsy is a common neurological disorder characterized by recurrence seizures. Alcoholism causes organic changes in the brain, resulting in seizure attacks similar to epileptic fits. Hence, it is challenging to differentiate the cause of fits as epileptic or alcoholism, which is important for deciding on the treatment in the neurology ward. The focus of this paper is to automatically differentiate epileptic, normal, and alcoholic electroencephalogram (EEG) signals. As the EEG signals are non-linear and dynamic in nature, it is difficult to tell the subtle changes in these signals with the help of linear techniques or by the naked eye. Therefore, to analyze the normal (control), epileptic, and alcoholic EEG signals, two non-linear methods, such as recurrence plots (RPs) and then recurrence quantification analysis (RQA) are adopted. Approximately 10 RQA parameters have been used to classify the EEG signals into three distinct classes, i.e., normal, epileptic, and alcoholic. Six classifiers, such as support vector machine (SVM), radial basis probabilistic neural network (RBPNN), decision tree (DT), Gaussian mixture model (GMM), k-nearest neighbor (kNN), and fuzzy Sugeno classifiers have been developed to accomplish this task. Results show that the GMM classifier outperformed the other classifiers with a classification sensitivity of 99.6%, specificity of 98.3%, and accuracy of 98.6%.

Quantified reflex strategy using an iPod as a wireless accelerometer application

A primary aspect of a neurological evaluation is the deep tendon reflex, frequently observed through the patellar tendon reflex. The reflex response provides preliminary insight as to the status of the nervous system. A quantified reflex strategy has been developed, tested, and evaluated though the use of an iPod as a wireless accelerometer application integrated with a potential energy device to evoke the patellar tendon reflex. The iPod functions as a wireless accelerometer equipped with robust software, data storage, and the capacity to transmit the recorded accelerometer waveform of the reflex response wirelessly through email for post-processing. The primary feature of the reflex response acceleration waveform is the maximum acceleration achieved subsequent to evoking the patellar tendon reflex. The quantified reflex strategy using an iPod as a wireless accelerometer application yields accurate and consistent quantification of the reflex response.

TENDON REFLEX AND STRATEGIES FOR QUANTIFICATION, WITH NOVEL METHODS INCORPORATING WIRELESS ACCELEROMETER REFLEX QUANTIFICATION DEVICES, A PERSPECTIVE REVIEW

The deep tendon reflex is a fundamental aspect of a neurological examination. The two major parameters of the tendon reflex are response and latency, which are presently evaluated qualitatively during a neurological examination. The reflex loop is capable of providing insight into the status and therapy response of both upper and lower motor neuron syndromes. Attempts have been made to ascertain reflex response and latency; however, these systems are relatively complex, resource intensive, with issues of consistent and reliable accuracy. The solution presented is a wireless quantified reflex device using tandem three-dimensional (3D) wireless accelerometers to obtain response based on acceleration waveform amplitude and latency derived from temporal acceleration waveform disparity. Three specific aims have been established for the proposed wireless quantified reflex device:

(1) Demonstrate the wireless quantified reflex device is reliably capable of ascertaining quantified reflex response and latency using a quantified input.

(2) Evaluate the precision of the device using an artificial reflex system.

(3) Conduct a longitudinal study respective of subjects with healthy patellar tendon reflexes, using the wireless quantified reflex evaluation device to obtain quantified reflex response and latency.

Aim 1 has led to a steady evolution of the wireless quantified reflex device from a singular 2D wireless accelerometer capable of measuring reflex response to a tandem 3D wireless accelerometer capable of reliably measuring reflex response and latency. The hypothesis for aim 1 is that a reflex quantification device can be established for reliably measuring reflex response and latency for the patellar tendon reflex, comprised of an integrated system of wireless 3D MEMS accelerometers. Aim 2 further emphasized the reliability of the wireless quantified reflex device by evaluating an artificial reflex system. The hypothesis for aim 2 is that the wireless quantified reflex device can obtain reliable reflex parameters (response and latency) from an artificial reflex device. Aim 3 synthesizes the findings relevant to aim 1 and 2, while applying the wireless accelerometer reflex quantification device to a longitudinal study of healthy patellar tendon reflexes. The hypothesis for aim 3 is that during a longitudinal evaluation of the deep tendon reflex the parameters for reflex response and latency can be measured with a considerable degree of accuracy, reliability, and reproducibility. Enclosed is a detailed description of a wireless quantified reflex device with research findings and potential utility of the system, inclusive of a comprehensive description of tendon reflexes, prior reflex quantification systems, and correlated applications.