Robert LeMoyne

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, Parkinsons disease hand tremor was quantified and contrasted with significance to a non-Parkinsons 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.

Implementation of Machine Learning for Classifying Hemiplegic Gait Disparity through Use of a Force Plate

The synergy of gait analysis tools with machine learning enables the capacity to classify disparity existing in hemiplegic gait. Hemiplegic gait is characterized by an affected leg and unaffected leg, which can be quantified by the measurement of a force plate. The characteristic features of the force plate recording for gait consist of a two local maxima that represent the braking phase and push off phase of stance and their associated parameters. The quantified features of a hemiplegic pair of affected leg and unaffected leg force plate recordings are intuitively disparate. Logistic regression achieves 100% classification between an affected and unaffected hemiplegic leg pair based on the feature set of the force plate data.

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.

Testing and Evaluation Strategies for the Powered Prosthesis, a Global Perspective

Testing and evaluation strategies are imperative to establish the efficacy of an experimental powered prosthesis. Biomechanical gait analysis applies the synthesis of equipment; such as force plates and optical motion capture, to acquire kinetic and kinematic parameters. These parameters derive joint characteristics, such as work and power, which can be applied to contrast novel prosthetic platforms. Other gait analysis equipment, such as electromyogram and metabolic analyzers, can determine muscle activation patterns and trends respective of metabolic efficiency while walking. New gait analysis devices function as mobile and wireless platforms. The smartphone and portable media devices are equipped with accelerometer and gyroscope sensors that can remotely monitor gait activity. Traditionally statistical significance is applied to establish a scientifically meaningful disparity between disparate sets of data. However, in the recent decade machine learning has been demonstrated to classify and distinguish between aspects of a feature set implying the eventual capacity to diagnose. Foundational to testing and evaluating transtibial prostheses are the prosthetic alignment procedure and acclimation timeframe.

Ankle Rehabilitation System with Feedback from a Smartphone Wireless Gyroscope Platform and Machine Learning Classification

With the prevalence of traumatic brain injury and associated motor function impairment, an advance in the capacity to measure the efficacy of a rehabilitation strategy is a topic of considerable interest. For example, the development of a rehabilitation system that can quantify the efficacy to an ankle dorsiflexion therapy prescription would be beneficial. An ankle rehabilitation system is presented that amalgamates multiple technologies, such as a smartphone (iPhone) wireless gyroscope platform, machine learning, and 3D printing. The ankle rehabilitation system is produced by mostly 3D printing. A smartphone wireless gyroscope platform records the ankle rehabilitation systems therapy usage with wireless transmission to the Internet as an email attachment. The gyroscope signal data is processed for machine learning. A support vector machine attains 97% classification between a hemiplegic affected ankle and unaffected ankle feature set while using the ankle rehabilitation system. The application can be readily applied to a homebound setting of the subjects convenience.

Wearable body and wireless inertial sensors for machine learning classification of gait for people with Friedreich's ataxia

The integration of wearable and wireless inertial body sensors with machine learning offers the capacity to diagnose neurological disorders involving gait. Clinical rating scales may be unable to offer precise measurement of gait dysfunction in Friedreichs ataxia compared to wearable body and inertial sensors. Using wireless inertial sensors mounted about the ankle joint of a person with Friedreichs ataxia, the accelerometer and gyroscope signal recordings can be wirelessly transmitted to a cloud computing resource for postprocessing, such as the development of a machine learning feature set. Machine learning can be applied to distinguish between the gait features of a person with Friedreichs ataxia and a person with healthy gait characteristics as a comparator through the application of a multilayer perceptron neural network. A considerable degree of classification accuracy for distinguishing between the gait feature set for the person with Friedreichs ataxia and healthy subject was achieved. The synthesis of wearable and wireless inertial body sensors with machine learning may offer the potential to enhance clinical diagnostic acuity and conceivably prognostic foresight.

Implementation of a smartphone as a wireless gyroscope platform for quantifying reduced arm swing in hemiplegie gait with machine learning classification by multilayer perceptron neural network

Natural gait consists of synchronous and rhythmic patterns for both the lower and upper limb. People with hemiplegia can experience reduced arm swing, which can negatively impact the quality of gait. Wearable and wireless sensors, such as through a smartphone, have demonstrated the ability to quantify various features of gait. With a software application the smartphone (iPhone) can function as a wireless gyroscope platform capable of conveying a gyroscope signal recording as an email attachment by wireless connectivity to the Internet. The gyroscope signal recordings of the affected hemiplegic arm with reduced arm swing arm and the unaffected arm are post-processed into a feature set for machine learning. Using a multilayer perceptron neural network a considerable degree of classification accuracy is attained to distinguish between the affected hemiplegic arm with reduced arm swing arm and the unaffected arm.

Smartphone wireless gyroscope platform for machine learning classification of hemiplegic patellar tendon reflex pair disparity through a multilayer perceptron neural network

The patellar tendon enables fundamental insight regarding neurological health status. Clinically observed dysfunction may warrant escalation to more advanced and expensive medical diagnostics. Conventionally clinicians apply an ordinal scale to quantify reflex response characteristics. However the reliability of ordinal scales is a subject of debate, and even highly skilled clinicians have disputed the observation of an asymmetric reflex pair. An alternative is the use of the wireless quantified reflex system, which features an impact pendulum attached to a reflex hammer for providing precisely targeted levels of potential energy with a smartphone (iPhone) equipped with software to function as a wireless gyroscope platform that can email a trial sample as an email attachment by wireless connectivity to the Internet. With notable attributes of the gyroscope signal recordings of the reflex response of a hemiplegic patellar tendon reflex pair observed a feature set is developed for machine learning classification. Using the multilayer perceptron neural network considerable classification accuracy is attained. The research implications reveal the potential of integrating machine learning with a wireless reflex quantification system that applies a smartphone (iPhone) as a wireless gyroscope platform.

Implementation of machine learning for classifying prosthesis type through conventional gait analysis.

Current forecasts imply a significant increase in the quantity of lower limb amputations. Synergizing the capabilities of a conventional gait analysis system and machine learning facilitates the capacity to classify disparate types of transtibial prostheses. Automated classification of prosthesis type may eventually advance rehabilitative acuity for selecting an appropriate prosthesis for a given aspect of the rehabilitation process. The presented research utilized a force plate as a conventional gait analysis device to acquire a feature set for two types of prosthesis: passive Solid Ankle Cushioned Heel (SACH) and the iWalk BiOM powered prosthesis. The feature set consists of both temporal and kinetic data with respect to the force plate signal during stance. Intuitively a passive prosthesis and powered prosthesis generate distinctively different force plate recordings. A support vector machine, which is type of machine learning application, achieves 100% classification between a passive prosthesis and powered prosthesis regarding the feature set derived from force plate recordings.