William J. Kaiser

Wireless integrated network sensors

W ireless integrated network sensors (WINS) provide distributed network and Internet access to sensors, controls, and processors deeply embedded in equipment, facilities, and the environment. The WINS network represents a new monitoring and control capability for applications in such industries as transportation, manufacturing, health care, environmental oversight, and safety and security. WINS combine microsensor technology and low-power signal processing, computation, and low-cost wireless networking in a compact system. Recent advances in integrated circuit technology have enabled construction of far more capable yet inexpensive sensors, radios, and processors, allowing mass production of sophisticated systems linking the physical world to digital data networks [2–5]. Scales range from local to global for applications in medicine, security, factory automation, environmental monitoring, and condition-based maintenance. Compact geometry and low cost allow WINS to be embedded and distributed at a fraction of the cost of conventional wireline sensor and actuator systems. WINS opportunities depend on development of a scalable, low-cost, sensor-network architecture. Such applications require delivery of sensor information to the user at a low bit rate through low-power transceivers. Continuous sensor signal processing enables the constant monitoring of events in an environment in which short message packets would suffice. Future applications of distributed embedded processors and sensors will require vast numbers of devices. Conventional methods of sensor networking represent an impractical demand on cable installation and network bandwidth. Processing at the source would drastically reduce the financial, computational, and management burden on communication system

Wireless integrated network sensors: Low power systems on a chip

Wireless Integrated Network Sensors (WINS) now provide a new monitoring and control capability for transportation, manufacturing, health care, environmental monitoring, and safety and security. WINS combine sensing, signal processing, decision capability, and wireless networking capability in a compact, low power system. WINS systems combine microsensor technology with low power sensor interface, signal processing, and RF communication circuits. The need for low cost presents engineering challenges for implementation of these systems in conventional digital CMOS technology. This paper describes micropower data converter, digital signal processing systems, and weak inversion CMOS RF circuits. The digital signal processing system relies on a continuously operating spectrum analyzer. Finally, the weak inversion CMOS RF systems are designed to exploit the properties of high-Q inductors to enable low power operation. This paper reviews system architecture and low power circuits for WINS.

Low power systems for wireless microsensors

Low power wireless sensor networks provide a new monitoring and control capability for civil and military applications in transportation, manufacturing, biomedical, environmental management, and safety and security systems. Wireless microsensor network nodes, operating at average and peak power levels constrained by compact power sources, offer a range of important challenges for low power methods. This paper reports advances in low power systems spanning network design, through power management, low power mixed signal circuits, and highly integrated RF network interfaces. Particular attention is focused on methods for low power RF receiver systems.

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.

Reliability and Validity of Bilateral Ankle Accelerometer Algorithms for Activity Recognition and Walking Speed After Stroke

Background and Purpose— Outcome measures of mobility for large stroke trials are limited to timed walks for short distances in a laboratory, step counters and ordinal scales of disability and quality of life. Continuous monitoring and outcome measurements of the type and quantity of activity in the community would provide direct data about daily performance, including compliance with exercise and skills practice during routine care and clinical trials. Methods— Twelve adults with impaired ambulation from hemiparetic stroke and 6 healthy controls wore triaxial accelerometers on their ankles. Walking speed for repeated outdoor walks was determined by machine-learning algorithms and compared to a stopwatch calculation of speed for distances not known to the algorithm. The reliability of recognizing walking, exercise, and cycling by the algorithms was compared to activity logs. Results— A high correlation was found between stopwatch-measured outdoor walking speed and algorithm-calculated speed (Pearson coefficient, 0.98; P=0.001) and for repeated measures of algorithm-derived walking speed (P=0.01). Bouts of walking >5 steps, variations in walking speed, cycling, stair climbing, and leg exercises were correctly identified during a day in the community. Compared to healthy subjects, those with stroke were, as expected, more sedentary and slower, and their gait revealed high paretic-to-unaffected leg swing ratios. Conclusions— Test–retest reliability and concurrent and construct validity are high for activity pattern-recognition Bayesian algorithms developed from inertial sensors. This ratio scale data can provide real-world monitoring and outcome measurements of lower extremity activities and walking speed for stroke and rehabilitation studies.

Wireless integrated network sensors (WINS)

Wireless Integrated Network Systems (WINS) provide distributed network and Internet access to sensors, controls, and processors that are deeply embedded in equipment, facilities, and the environment. The WINS network is a new monitoring and control capability for applications in transportation, manufacturing, health care, environmental monitoring, and safety and security. WINS combine microsensor technology, low power signal processing, low power computation, and low power, low cost wireless networking capability in a compact system. WINS networks will provide sensing, local control, and embedded intelligent systems in structures, materials, and environments. This paper describes the WINS architecture and WINS technology components including sensor interface and WINS event recognition systems.

Wireless integrated network sensors (WINS): distributed in situ sensing for mission and flight systems

Wireless Integrated Network Sensors (WINS) form a new distributed information technology of combined sensor, actuator, and processing systems. WINS distributed nodes form autonomous, self-organized, wireless sensing and control networks. WINS nodes include microsensors, signal processing, computation and low power wireless networking. WINS enable distributed measurements for applications ranging from aerospace system condition monitoring to distributed environmental science monitoring. This paper will discuss the enabling technology advances of WINS for mission and flight system in situ sensing. This will include the complete set of technologies, from new MEMS devices through information technology. Finally, a new WINS generation, GlobalWINS, will be described. GlobalWINS has been developed for planetary-wide distribution of science instruments.

Wide-bandwidth electromechanical actuators for tunneling displacement transducers

A series of displacement transducers have been demonstrated which are based on the detection of electrons that quantum-mechanically tunnel across a narrow gap between electrodes. These transducers have important applications due to the sensitivity of the tunneling mechanism to sub-/spl Aring/ variations in the electrode gap. In this paper, we describe the recent development of wide-bandwidth electromechanical actuators and simple feedback circuitry which have been adapted for use in tunneling displacement transducers. With these actuators and circuits, we have built tunneling transducers with control bandwidths well in excess of 10 kHz. The design, fabrication, operation, and applications of these actuators are described. >

Scanning tunneling microscopy characterization of the geometric and electronic structure of hydrogen‐terminated silicon surfaces

Scanning tunneling microscopy (STM) methods are used to characterize hydrogen‐terminated Si surfaces prepared by a novel method. The surface preparation method is used to expose the Si–SiO2 interface. STM images directly reveal the topographic structure of the Si–SiO2 interface. The dependence of interface topography on oxide preparation conditions observed by STM is compared to the results of conventional surface characterization methods. Also, the electronic structure of the hydrogen‐terminated surface is studied by STM spectroscopy. The near‐ideal electronic structure of this surface enables direct tunnel spectroscopy measurements of Schottky barrier phenomena. In addition, this method enables probing of semiconductor subsurface properties by STM.

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.