In the present study, a recurrent neural network approach is proposed for the classification of four gait sub phases of the gait cycle (Heel contact, flat foot contact, push off and swing phase) and the consequent prediction of gait events (Heel Strike, Mid foot strike, heel rise and toe off event), considering only sEMG signals as input to the model. To this, sEMG signals from Tibialis Anterior, Gastrocnemius Lateralis, Rectus Femoris, Vastus Lateralis, and Hamstring muscles were acquired from both legs of 30 healthy subjects walking barefoot on the floor for about 5 min at their own pace following an eight-shaped path on the ground.
In the present study, a recurrent neural network approach is proposed for the classification of four gait sub phases of the gait cycle (Heel contact, flat foot contact, push off and swing phase) and the consequent prediction of gait events (Heel Strike, Mid foot strike, heel rise and toe off event), considering only sEMG signals as input to the model. To this, sEMG signals from Tibialis Anterior, Gastrocnemius Lateralis, Rectus Femoris, Vastus Lateralis, and Hamstring muscles were acquired from both legs of 30 healthy subjects walking barefoot on the floor for about 5 min at their own pace following an eight-shaped path on the ground.
A machine-learning approach to EMG-based classification of gait phases and sub-phases.
DI LELLO, ALESSIO
2020/2021
Abstract
In the present study, a recurrent neural network approach is proposed for the classification of four gait sub phases of the gait cycle (Heel contact, flat foot contact, push off and swing phase) and the consequent prediction of gait events (Heel Strike, Mid foot strike, heel rise and toe off event), considering only sEMG signals as input to the model. To this, sEMG signals from Tibialis Anterior, Gastrocnemius Lateralis, Rectus Femoris, Vastus Lateralis, and Hamstring muscles were acquired from both legs of 30 healthy subjects walking barefoot on the floor for about 5 min at their own pace following an eight-shaped path on the ground.File | Dimensione | Formato | |
---|---|---|---|
Tesi Alessio Di Lello.pdf
Open Access dal 26/10/2023
Descrizione: Tesi Alessio Di Lello
Dimensione
2.6 MB
Formato
Adobe PDF
|
2.6 MB | Adobe PDF | Visualizza/Apri |
I documenti in UNITESI sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
https://hdl.handle.net/20.500.12075/517