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.