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Authors have utilized the distance feature vector for each of the joints with respect to other joints in the gait cycle. After mean and variance features are extracted from the distance feature vector, the KNN algorithm is used for classification purpose. The classification accuracy of the authors' approach is 93.33%. Experimental results show that the proposed approach achieves better recognition accuracy then other state-of-the-art approaches. Incorporating gait biometric in a situation awareness system for identification of a mental state is one of the future directions of this research.<\/jats:p>","DOI":"10.4018\/ijssci.2017100102","type":"journal-article","created":{"date-parts":[[2018,1,23]],"date-time":"2018-01-23T07:49:05Z","timestamp":1516693745000},"page":"19-33","source":"Crossref","is-referenced-by-count":2,"title":["Human Identification Using Gait Skeletal Joint Distance Features"],"prefix":"10.4018","volume":"9","author":[{"given":"Md Wasiur","family":"Rahman","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Calgary, Calgary, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marina L.","family":"Gavrilova","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Calgary, Calgary, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"IJSSCI.2017100102-0","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-015-1092-0"},{"key":"IJSSCI.2017100102-1","doi-asserted-by":"publisher","DOI":"10.1145\/2915926.2915937"},{"key":"IJSSCI.2017100102-2","doi-asserted-by":"crossref","unstructured":"Azam S., Gavrilova M. 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