TY - JOUR
T1 - Identification of footstrike pattern using accelerometry and machine learning
AU - Mahoney, Joseph M.
AU - Rhudy, Matthew B.
AU - Outerleys, Jereme
AU - Davis, Irene S.
AU - Altman-Singles, Allison R.
N1 - Publisher Copyright:
© 2024 Elsevier Ltd
PY - 2024/9
Y1 - 2024/9
N2 - Recent reports have suggested that there may be a relationship between footstrike pattern and overuse injury incidence and type. With the recent increase in wearable sensors, it is important to identify paradigms where the footstrike pattern can be detected in real-time from minimal data. Machine learning was used to classify tibial acceleration data into three distinct footstrike patterns: rearfoot, midfoot, or forefoot. Tibial accelerometry data were collected during treadmill running from 58 participants who each ran with rearfoot, midfoot, and forefoot strike patterns. These data were used as inputs into an artificial neural network classifier. Models were created by using three distinct acceleration data sets, using the first 100%, 75%, and 40% of stance phase. All models were able to predict the footstrike pattern with up to 89.9% average accuracy. The highest error was associated with the identification of the midfoot versus forefoot strike pattern. This technique required no pre-selection of features or filtering of the data and may be easily incorporated into a wearable device to aid with real-time footstrike pattern detection.
AB - Recent reports have suggested that there may be a relationship between footstrike pattern and overuse injury incidence and type. With the recent increase in wearable sensors, it is important to identify paradigms where the footstrike pattern can be detected in real-time from minimal data. Machine learning was used to classify tibial acceleration data into three distinct footstrike patterns: rearfoot, midfoot, or forefoot. Tibial accelerometry data were collected during treadmill running from 58 participants who each ran with rearfoot, midfoot, and forefoot strike patterns. These data were used as inputs into an artificial neural network classifier. Models were created by using three distinct acceleration data sets, using the first 100%, 75%, and 40% of stance phase. All models were able to predict the footstrike pattern with up to 89.9% average accuracy. The highest error was associated with the identification of the midfoot versus forefoot strike pattern. This technique required no pre-selection of features or filtering of the data and may be easily incorporated into a wearable device to aid with real-time footstrike pattern detection.
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U2 - 10.1016/j.jbiomech.2024.112255
DO - 10.1016/j.jbiomech.2024.112255
M3 - Article
C2 - 39159584
AN - SCOPUS:85201448523
SN - 0021-9290
VL - 174
JO - Journal of Biomechanics
JF - Journal of Biomechanics
M1 - 112255
ER -