Winston Wu

MEDIC: Medical embedded device for individualized care

OBJECTIVE Presented work highlights the development and initial validation of a medical embedded device for individualized care (MEDIC), which is based on a novel software architecture, enabling sensor management and disease prediction capabilities, and commercially available microelectronic components, sensors and conventional personal digital assistant (PDA) (or a cell phone). METHODS AND MATERIALS In this paper, we present a general architecture for a wearable sensor system that can be customized to an individual patients needs. This architecture is based on embedded artificial intelligence that permits autonomous operation, sensor management and inference, and may be applied to a general purpose wearable medical diagnostics. RESULTS A prototype of the system has been developed based on a standard PDA and wireless sensor nodes equipped with commercially available Bluetooth radio components, permitting real-time streaming of high-bandwidth data from various physiological and contextual sensors. We also present the results of abnormal gait diagnosis using the complete system from our evaluation, and illustrate how the wearable system and its operation can be remotely configured and managed by either enterprise systems or medical personnel at centralized locations. CONCLUSION By using commercially available hardware components and software architecture presented in this paper, the MEDIC system can be rapidly configured, providing medical researchers with broadband sensor data from remote patients and platform access to best adapt operation for diagnostic operation objectives.

The low power energy aware processing (LEAP)embedded networked sensor system

A broad range of embedded networked sensor (ENS) systems for critical environmental monitoring applications now require complex, high peak power dissipating sensor devices, as well as on-demand high performance computing and high bandwidth communication. Embedded computing demands for these new platforms include support for computationally intensive image and signal processing as well as optimization and statistical computing. To meet these new requirements while maintaining critical support for low energy operation, a new multiprocessor node hardware and software architecture, low power energy aware processing (LEAP), has been developed. The LEAP architecture integrates fine-grained energy dissipation monitoring and sophisticated power control scheduling for all subsystems including sensor subsystems. This paper also describes a new distributed node testbed demonstrating that by exploiting high high energy efficiency components and enabling proper on-demand scheduling, the LEAP architecture may meet both sensing performance and energy dissipation objectives for a broad class of applications

The SmartCane system: an assistive device for geriatrics

Falls are currently a leading cause of death from injury in the elderly. The usage of the conventional assistive cane devices is critical in reducing the risk of falls and is relied upon by over 4 million patients in the U.S.. While canes provide physical support as well as supplementary sensing feedback to patients, at the same time, these conventional aids also exhibit serious adverse effects that contribute to falls. The falls due to the improper usage of the canes are particularly acute in the elderly and disabled where reduced cognitive capacity accompanied by the burden of managing cane motion leads to increased risk. This paper describes the development of the SmartCane assistive system that encompasses broad engineering challenges that will impact general development of individualized, robust assistive and prosthetic devices. The SmartCane system combines advances in signal processing, embedded computing, and wireless networking technology to provide capabilities for remote monitoring, local signal processing, and real-time feedback on the cane usage. This system aims to reduce risks of injuries and falls by enabling training and guidance of patients in proper usage of assistive devices.

Incremental Diagnosis Method for Intelligent Wearable Sensor Systems

This paper presents an incremental diagnosis method (IDM) to detect a medical condition with the minimum wearable sensor usage by dynamically adjusting the sensor set based on the patients state in his/her natural environment. The IDM, comprised of a naive Bayes classifier generated by supervised training with Gaussian clustering, is developed to classify patient motion in- context (due to a medical condition) and in real-time using a wearable sensor system. The IDM also incorporates a utility function, which is a simple form of expert knowledge and user preferences in sensor selection. Upon initial in-context detection, the utility function decides which sensor is to be activated next. High-resolution in-context detection with minimum sensor usage is possible because the necessary sensor can be activated or requested at the appropriate time. As a case study, the IDM is demonstrated in detecting different severity levels of a limp with minimum usage of high diagnostic resolution sensors.

MicroLEAP: Energy-aware Wireless Sensor Platform for Biomedical Sensing Applications

Extended system lifetime is a critical requirement for wearable sensor platforms. However, these platforms must also accommodate local data processing, data storage, and broadband wireless communications. Since compact battery storage capacity is constrained, there exists a fundamental tradeoff between energy optimization and performance. Furthermore, biomedical transducers may also demand high peak power dissipation during active operations. Energy management, therefore, must be introduced through new hardware architecture and enabled through software in the overall system design. To effectively optimize energy dissipation for biomedical sensing applications, a new wearable sensor platform, MicroLEAP, has been developed. The MicroLEAP platform supports per-task real-time energy profiling to permit adaptive applications that select platform components to best match dynamically-varying measurement requirements. MicroLEAP design, implementation, and example of energy-aware operation are demonstrated.

Context-aware Sensing of Physiological Signals

Recent advancement in microsensor technology permits miniaturization of conventional physiological sensors. Combined with low-power, energy-aware embedded systems and low power wireless interfaces, these sensors now enable patient monitoring in home and workplace environments in addition to the clinic. Low energy operation is critical for meeting typical long operating lifetime requirements. Some of these physiological sensors, such as electrocardiographs (ECG), introduce large energy demand because of the need for high sampling rate and resolution, and also introduce limitations due to reduced user wearability. In this paper, we show how context-aware sensing can provide the required monitoring capability while eliminating the need for energy-intensive continuous ECG signal acquisition. We have implemented a wearable system based on standard widely-used handheld computing hardware components. This system relies on a new software architecture and an embedded inference engine developed for these standard platforms. The performance of the system is evaluated using experimental data sets acquired for subjects wearing this system during an exercise sequence. This same approach can be used in context-aware monitoring of diverse physiological signals in a patients daily life.

Virtual proprioception with real-time step detection and processing

Virtual proprioception is a novel device for providing near autonomous biofeedback of hemiparetic gait disparity in real time. With virtual proprioception a user may modify gait dynamics to develop a more suitable gait in tandem with real time feedback. Accelerometers are fundamental to the operation of the device, and a thorough consideration of the accelerometry technology space for locomotion quantification is included. The role of traumatic brain injury and respective decrements to gait quality and proprioceptive feedback are addressed. Virtual proprioception conceptual test and evaluation yielded positive results. The active ‘on’ status of the virtual proprioception biofeedback for alternative gait strategy was bounded by a 90% confidence level with a 10% margin of error.

Active guidance towards proper cane usage

The usage of conventional assistive cane devices is critical in reducing the risk of falls, which are particularly detrimental to the elderly and disabled. Individuals that experience the greatest risks rely on cane devices for support of ambulation. Results of many studies, however, have shown that incorrect cane usage is prevalent among cane users. The original SmartCane assistive system has been developed to provide a method for acquiring detailed motion data from cane usage. The cane itself, however, lacks any type of programmability as well as real-time data processing algorithms to provide feedback to the cane user. This paper describes the development of a real-time sensor information processing algorithm that provides direct detection of cane usage characteristics. Specifically, it supports direct feedback to the cane user, permitting guidance for proper cane usage and reducing the risk of falls. This paper also aims to improve upon the existing system by incorporating MicroLEAP, an energy-aware embedded computing platform. The new system provides local data processing capability by classifying whether an individual is executing a stride with proper cane motion and applied forces.

Demonstration of Active Guidance with SmartCane

The usage of conventional assistive cane devices is critical in reducing the risk of falls, which are particularly detrimental for the elderly and disabled. Many of the individuals that experience the greatest risk of falling rely on cane devices for support of ambulation. However, the results of many studies have shown that incorrect cane usage is prevalent among cane users. The original SmartCane assistive system has been developed to provide a method for acquiring detailed motion data from cane usage. The cane itself, however, lacks any type of programmability as well as real-time data processing algorithms to provide feedback to the cane user. In this demonstration, we have incorporated an embedded computing platform into SmartCane and developed a real-time sensor information processing algorithm that provides direct detection of cane usage characteristics. The new system provides local data processing capability by classifying whether an individual is executing a stride with proper cane motion and applied forces. It also provides direct feedback information to the individual, thereby guiding the subject towards proper cane usage and reducing the risk of falls.

Approximate Data Collection using Resolution Control based on Context

Approximate data collection is an important mechanism for real-time and high sampling rate monitoring applications in body sensor networks, especially when there are multiple sensor sources. Unlike traditional approaches that utilize temporal or spatio-temporal correlations among the measurements of the multiple sensors observing a physical process to reduce the communication cost, in this paper we explore the idea of assigning different context-dependent priorities to the various sensors, and allocating communication resources according to data from a sensor according to its priorities. Specifically, a higher number of bits per sample is allocated to sensors that are of higher priority in the current context. We demonstrate that the proposed approach provides accurate inference results while effectively reducing the communication load.