IEEE Transactions on Information Technology in Biomedicine
Towards ubiquitous acquisition and processing of gait parameters
MICAI'10 Proceedings of the 9th Mexican international conference on Advances in artificial intelligence: Part I
A method with triaxial acceleration sensor for fall detection of the elderly in daily activities
UAHCI'11 Proceedings of the 6th international conference on Universal access in human-computer interaction: context diversity - Volume Part III
Mixture modeling of gait patterns from sensor data
Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments
Step count algorithm adapted to indoor localization
Proceedings of the International C* Conference on Computer Science and Software Engineering
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A portable and wireless activity monitoring system was developed for the estimation of temporal gait parameters. The new system was built using three-axis accelerometers to automatically detect walking steps with various walking speeds. The accuracy of walking step-peak detection algorithm was assessed by using a running machine with variable speeds. To assess the consistency of gait parameter analysis system, estimated parameters, such as heel-contact and toe-off time based on accelerometers and footswitches were compared for consecutive 20 steps from 19 individual healthy subjects. Accelerometers and footswitches had high consistency in the temporal gait parameters. The stance, swing, single-limb support, and double-limb support time of gait cycle revealed ICCs values of 0.95, 0.93, 0.86, and 0.75 on the right and 0.96, 0.86, 0.93, 0.84 on the left, respectively. And the walking step-peak detection accuracy was 99.15% (卤0.007) for the proposed method compared to 87.48% (卤0.033) for a pedometer. Therefore, the proposed activity monitoring system proved to be a reliable and useful tool for identification of temporal gait parameters and walking pattern classification.