Gait phase detection by using a portable system and artificial neural network

Gait phases are important to evaluate the walking function and to identify the characteristics of pathological gaits. However, it is difficult to differentiate gait phases outside gait laboratories, thus, this study aimed to develop a method to detect 8 gait sub-phases using a wearable multiple sensor system and artificial neural network (ANN). Motion sensors were used to acquire the acceleration of lower limbs, and force sensitive resistors were used to detect contact state and force between the foot and the ground. Walking was recorded using a high-speed camera. Two feed forward back-propagation (BP) neural networks were developed. The resilient BP algorithm was used to train ANN. A total of 66 volunteers participated in this study. For the stance and swing phase detection, simulation of the training data showed an accuracy of 98.0 ​%. The data from the test set showed a recognition accuracy of 97.75 ​%. Because the ending point of the last phase ‘Terminal Swing’ is always 100 ​% GC, we only listed seven phases. The prediction accuracy of seven phases were: 35.9 ​%, 63.8 ​%, 93.6 ​%, 94.9 ​%, 94.8 ​%, 97.9 ​% and 98 ​% using the limb acceleration data only. The average accuracy for seven phases were 68 ​%, 91.3 ​%, 97.8 ​%, 98.9 ​%, 98.8 ​%, 99.1 ​%, and 99.5 ​% using the limb acceleration and foot pressure data for fast, normal, and slow gait speeds. This study provides a new method for eight gait sub-phases detection with high accuracy combining a wearable system and ANN, which may make gait phase analysis possible under free-living conditions. •It is difficult to differentiate gait phases outside gait laboratories.•A wearable multiple sensor system and artificial neural network (ANN) were used.•The resilient Two Feed Forward Back-propagation (BP) algorithm was used to train ANN.•This study provided a new approach for eight gait phases detection with high accuracy.•This study made gait phase analysis possible under free-living conditions.