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 [4] 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 [2] 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.