Harry C. Powell

On-Body Inertial Sensing and Signal Processing for Clinical Assessment of Tremor

Tremor, the most common form of movement disorder, is an often debilitating condition that adversely affects an individuals ability to maintain functional independence. Efforts to study, diagnose, and treat such movement disorders are complicated by a dearth of quantitative, precise, or accurate methods for motion data collection and assessment. To address this deficiency, this paper provides two contributions: 1) the design of a body-area inertial sensing system and 2) the evaluation of postcapture, on-body signal-processing algorithms that transform sensed inertial data into clinically significant information pertaining to tremor symmetry. For the former, we present our technology that meets requirements for wearability, fidelity, battery life, and interoperability. For the latter, we demonstrate the efficacy of using filter-bank analysis and cross correlation to interpret tremor frequency and energy. We extend the previous work by presenting a wireless body-area inertial sensing technology and a method to reduce, by up to 30 times, the computational demands of cross correlation on such a resource-constrained technology. These efforts lay the foundation for real-time, on-body assessment of tremor as well as more intelligent and energy-efficient data transmission and storage decisions.

Body-coupled communication for body sensor networks

Body sensor networks (BSNs) offer a wealth of opportunities for precise, accurate, continuous, and non-invasive sensing of physiological phenomena, but their unique operating environment, the body-area, poses unique technical challenges. Popular communications solutions that utilize 2.4 GHz radio transmission suffer from significant and highly variable path loss in this setting. To compensate for such loss, radio transceivers often transmit at power levels at or above 1 mW -- a reality that limits battery life. We propose the use of body-coupled communication to address this issue, as it presents several distinct advantages over existing solutions, namely: reduced power consumption, minimal interference, and increased privacy. In this paper, we demonstrate a 23 MHz body-coupled channel that supports reliable data transfer with an average received power of 30 dBm over a 2.4 GHz radio frequency link. This scheme reduces power needed for transmission and increases battery life by up to 100%, while maintaining a favorable environment for application-specific quality of service requirements. Finally, we propose a system-level hardware architecture and explore its implications on BSN infrastructure.

Teager energy assessment of tremor severity in clinical application of wearable inertial sensors

Essential tremor is the most common form of involuntary movement disorder and is often a debilitating condition for those affected. In the most severe cases, long-term suppression is achieved by chronic thalamic stimulation. This stimulation is defined with numerous parameters, and determining the optimal patient-specific settings requires accurate and precise assessment of tremor severity during programming. We introduce a technique to provide such assessment of essential tremor severity by applying the Teager energy function to data collected with TEMPO 1.0, a custom, wearable, inertial sensing technology for continuous, non-invasive, objective measurement of movement disorder such as tremor. This approach affords an opportunity to analyze tremor at a finer level of granularity than is currently possible with the clinical rating scale. Additionally, our technology facilitates further research of general tremor presentation, treatment, and etiology. Results obtained from a post-operative pilot study of deep brain stimulation efficacy at the University of Virginias Department of Neurosurgery not only quantify tremor severity for programming enhancement, but also reveal axial tremor and ipsilateral benefit -both elusive tremor observations. This paper presents our approach and preliminary findings obtained from the clinical application of TEMPO 1.0.

Dynamic voltage-frequency scaling in body area sensor networks using COTS components

Body area sensor networks (BASNs) have implicit stringent power requirements to meet battery life and form factor expectations, especially in long-term medical monitoring applications. The largest power consumer in BASNs is typically the wireless transceiver, so recent research has focused on increasing on-node signal processing to reduce the number of bits for wireless transmission. This shift increases the importance of power efficient signal processing. Given that the processing workloads and throughput requirements can change dynamically in a BASN, dynamic voltage-frequency scaling (DVFS) becomes an attractive option for providing the necessary processing rate with the minimum power. However, commercial off the shelf (COTS) components typically used in BASN nodes are not designed for DVFS. This paper characterizes the DVFS capabilities of a COTS processor commonly used on BASN nodes -- the TI MSP430 -- and explores the usefulness of these capabilities within the context of BASN applications.

A Wearable Inertial Sensing Technology for Clinical Assessment of Tremor

TEMPO (Technology-Enabled Medical Precision Observation) 1.0 is a novel, first-generation, wearable data collection and analysis platform for assessment of a variety of human movement disorders, including tremor. It enables quantitative, objective, and continuous measurement of movement with minimal invasiveness and inconvenience to the patient and clinician, respectively. This system meets requirements for wearability, data storage, sampling rate, number of sensors, interface methods, and form factor, which are necessary for applications on person. In addition to the design and development of a basic data acquisition device, various circuits and systems were engineered to interface wearable, triaxial MEMS inertial sensors. Furthermore, custom data analysis software that processes datasets collected from the device and sensors, was created, and has demonstrated clinical utility in the analysis of tremor. Data processing techniques include a unique filtering scheme and a novel application of cross-correlation. The analysis was conducted pre- and post-operatively, in conjunction with the University of Virginias Department of Neurosurgery, for a study of deep brain stimulation efficacy. This paper presents the engineering of and experimental results obtained with TEMPO 1.0 technology in the clinical assessment of tremor.

Longitudinal high-fidelity gait analysis with wireless inertial body sensors

Gait analysis has long been used for various medical and healthcare assessments [1]. In orthopedics and prosthetics, gait analysis is essential for identifying the pathology and assessing the efficacy of the orthopedic assistants or prosthetics prescribed. For example, the efficacy of ankle-foot orthoses (AFOs), usually prescribed to patients with muscle disorders, (e.g., cerebral palsy, spinal cord injury, muscular dystrophy, etc.) to prevent contractures [2], remains unclear. Studies on recovery and rehabilitation from knee surgery have shown that gait analysis focusing on knee joint angles is the key to evaluating the efficacy of treatment. In elderly healthcare, gait analysis has also played an important role in studies of fall risks and fall prevention [3]. Even in cognitive and neuropsychology studies, gait analysis becomes an important parameter because of the close relationship between human cognitive skills and motor function. For example, [4] and [5] have shown the research value of gait analysis in Parkinsons disease and early childhood autism diagnosis, respectively.

Application-Focused Energy-Fidelity Scalability for Wireless Motion-Based Health Assessment

Energy-fidelity trade-offs are central to the performance of many technologies, but they are essential in wireless body area sensor networks (BASNs) due to severe energy and processing constraints and the critical nature of certain healthcare applications. On-node signal processing and compression techniques can save energy by greatly reducing the amount of data transmitted over the wireless channel, but lossy techniques, capable of high compression ratios, can incur a reduction in application fidelity. In order to maximize system performance, these trade-offs must be considered at runtime due to the dynamic nature of BASN applications, including sensed data, operating environments, user actuation, etc. BASNs therefore require energy-fidelity scalability, so automated and user-initiated trade-offs can be made dynamically. This article presents a data rate scalability framework within a motion-based health application context which demonstrates the design of efficient and efficacious wireless health systems.

Design of Multiple Bandpass Filters with Integer Coefficients for a Microcontroller Environment with an Emphiasis on Applications in Wearable Tremor Analysis

Wearable computing devices are becoming an important technology for medical diagnostic and treatment assessment. Many such devices have strict power and size requirements and are therefore often limited to small, integer- only signal processing microcontrollers. It is therefore a significant challenge to develop signal processing techniques that provide the necessary application fidelity with strict implementation limitations. One of our research projects involves the use of wearable devices to assess the efficacy of treatments for Essential Tremor and Parkinsons Disease patients. Accelerometers are used to measure the tremor, and the resultant signal spectra are analyzed using digital bandpass filters to measure the amount of tremor in each of several frequency bands. This paper presents a design procedure for integer-only filters (suitable for microcontroller implementation) using repeated convolution and frequency shifting that is shown to produce superior filters than those designed by commercial filter design tools.

Systematic estimation of ANN classification performance employing synthetic data

The use of artificial neural network (ANN) classifiers as a signal processing element in resource constrained embedded computing systems has been restricted due to the difficulty of predicting performance and execution requirements on the deployed platform. In this paper, techniques are presented which provide a means of efficiently estimating data complexity, generating meaningful synthetic data, and evaluating ANN classifiers in terms of achievable performance.