Abstract
Gait phase detection, which detects foot-contact and foot-off states during walking, is important for various applications, such as synchronous robotic assistance and health monitoring. Gait phase detection systems have been proposed with various wearable devices, sensing inertial, electromyography, or force myography information. In this paper, we present a novel gait phase detection system with static standing-based calibration using muscle deformation information. The gait phase detection algorithm can be calibrated within a short time using muscle deformation data by standing in several postures; it is not necessary to collect data while walking for calibration. A logistic regression algorithm is used as the machine learning algorithm, and the probability output is adjusted based on the angular velocity of the sensor. An experiment is performed with 10 subjects, and the detection accuracy of foot-contact and foot-off states is evaluated using video data for each subject. The median accuracy is approximately 90% during walking based on calibration for 60 s, which shows the feasibility of the static standing-based calibration method using muscle deformation information for foot-contact and foot-off state detection.
Highlights
We present a detection system for foot-contact/foot-off based on logistic regression (LR), the probability of which is adjusted using the leg kinematic information with static standing-based calibration using muscle deformation information
Even if the position of the foot of a certain posture is slightly different, the resulting change in muscle deformation status may be sufficiently large to affect the accuracy of gait phase detection
A logistic regression algorithm was used as the machine learning algorithm, and the probability output was adjusted based on the angular velocity of the sensor
Summary
Chronic diseases caused by ageing are associated with a decline in walking ability [1,2]. Gait-assistance devices have been developed for mitigating the impairments due to the decline in walking ability [5,6]. Gait phase detection is essential for monitoring human health conditions and for synchronous gait assistance with devices. Gait can be classified into two phases: stance and swing. Footcontact and foot-off states during the gait cycle are defined as the stance phase and swing phase, respectively [7]. The system cannot be used outdoors for gait phase detection because of the lack of portability. This is crucial for widespread daily use. Wearable sensors for the classification of gait phases have been proposed for monitoring human gait motions in daily life
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