Name
Abstract | ||
|---|---|---|
This paper describes the classification of walking patterns on ascending and descending slopes based on features extracted from data recorded using a single waist-mounted tri-axial accelerometer. A 19-dimensional set of salient features representing the hill walking patterns were obtained based on gait cydle analysis related to the acceleration data in the anterior-posterior (AP), medio-lateral (ML), and vertical (V) directions. A Gaussian Mixture Model (GMM) classifier was used to perform a four way classification task, discriminating between two inclines and two declines. An overall classification accuracy of 90.9% was achieved for the four different human gait patterns referring to four different paved gradients (up or down 4.8% and 17.3% gradients). |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICDSP.2009.5201202 | DSP'09 Proceedings of the 16th international conference on Digital Signal Processing |
Keywords | DocType | Citations |
salient feature,gaussian mixture model,classification task,different paved gradient,inclined surface,accelerometry data,overall classification accuracy,different human gait pattern,gait cydle analysis,19-dimensional set,acceleration data,single waist-mounted tri-axial accelerometer,gaussian processes,pattern analysis,sensors,accuracy,acceleration,feature extraction,data mining,accelerometers,accelerometry,gait analysis | Conference | 5 |
PageRank | References | Authors |
1.19 | 1 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ning Wang | 1 | 13 | 2.76 |
| Eliathamby Ambikairajah | 2 | 493 | 64.55 |
| Stephen J. Redmond | 3 | 131 | 13.81 |
| Branko G. Celler | 4 | 502 | 81.99 |
| nigel h lovell | 5 | 618 | 118.68 |