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.

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 patient's 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.

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. The LEAP2 platform is a second generation LEAP system with even higher resolution energy monitoring as well as the unique ability to do per process and per application energy profiling via a dedicated high performance ASIC. This poster will demonstrate the hardware platform capabilities as well as the energy-aware software currently available for LEAP2.