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 ma...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Information Processing in Sensor Networks; Proceedings: International Conference on Information Processing in Sensor Networks, 2008: St. Louis, MO S. 537 - 538
Hauptverfasser: Au, Lawrence K., Wu, Winston H., Batalin, Maxim A., Stathopoulos, Thanos, Kaiser, William J.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: Washington, DC, USA IEEE Computer Society 22.04.2008
IEEE
Schriftenreihe:ACM Conferences
Schlagworte:
Applied computing > Life and medical sciences > Consumer health
Applied computing > Life and medical sciences > Health informatics
Classification algorithms
Data processing
Embedded computing
Gold
Hardware > Communication hardware, interfaces and storage > Signal processing systems
Information processing
Injuries
Real time systems
Senior citizens
Sensor systems
State feedback
ISBN:9780769531571, 0769531571
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
ISBN:9780769531571
0769531571
DOI:10.1109/IPSN.2008.52