Laboratory in a box: wearable sensors and its advantages for gait analysis.

Bijan Najafi, Tahir Khan, James Wrobel

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Until recently, many gait studies explored potential gait alteration due to various disorders in the gait lab and using camera based systems and force platforms. However, these strategies may not replicate normal outdoor walking. Using this equipment, it is more difficult to measure the variability of walking which is important for maintaining balance and responding to different walking challenges. Additionally, subjects may mask their problem or exaggerate it when they are walking in a short walking distance offered by laboratory based-technology. This study overviews some of the key advantages of wearable technology compared to laboratory-based instrument. Additionally, it explored gait patterns over ample distance of walking compared to walking distance restricted to a gait laboratory environment. Walking patterns of ten healthy young subjects were examined using a wearable sensor technology in a random order over a distance of 7 m, 14 m, and 20 m. Results suggest that participants walk significantly faster by increasing walking distance on average by 15% and 3% when walking distance was increased respectively from 7 m to 14 and from 14 m to 20 m (p<0.05). Interestingly despite a high test-retest reliability for averaged gait parameters (ICC>0.89), the test-retest reliability for gait variability was only acceptable during 20 m walking distance (ICC<0.3 for 7 m and 14 m v. ICC=0.65 for 20 m). Taken together, our findings indicate that for valid and reliable assessment of gait parameters, gait should be performed over ample walking distances. Body worn sensor technology facilitates assessing gait outside of a gait laboratory, over ample walking distance, different footwear condition, different walking surface, and in environment where mimics better true environment where the subject is active in.

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics


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