A4 Vertaisarvioitu artikkeli konferenssijulkaisussa
Gait Estimation and Analysis from Noisy Observations
Tekijät: Hafsa Ismail, Ibrahim Radwan, Hanna Suominen, Roland Goecke
Konferenssin vakiintunut nimi: International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Kustantaja: IEEE
Julkaisuvuosi: 2019
Journal: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Kokoomateoksen nimi: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Tietokannassa oleva lehden nimi: Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
Lehden akronyymi: Conf Proc IEEE Eng Med Biol Soc
Aloitussivu: 2707
Lopetussivu: 2712
ISBN: 978-1-5386-1312-2
eISBN: 978-1-5386-1311-5
ISSN: 1557-170X
eISSN: 1558-4615
DOI: https://doi.org/10.1109/EMBC.2019.8857156
People's walking style - their gait - can be an indicator of their health as it is affected by pain, illness, weakness, and aging. Gait analysis aims to detect gait variations. It is usually performed by an experienced observer with the help of different devices, such as cameras, sensors, and/or force plates. Frequent gait analysis, to observe changes over time, is costly and impractical. This paper initiates an inexpensive gait analysis based on recorded video. Our methodology first discusses estimating gait movements from predicted 2D joint locations that represent selected body parts from videos. Then, using a long-short-term memory (LSTM) regression model to predict 3D (Vicon) data, which was recorded simultaneously with the videos as ground truth. Feet movements estimated from video are highly correlated with the Vicon data, enabling gait analysis by measuring selected spatial gait parameters (step and cadence length, and walk base) from estimated movements. Using inexpensive and reliable cameras to record, estimate and analyse a person's gait can be helpful; early detection of its changes facilitates early intervention.
