HeelContact Gait Phase Detection Based on Specific Poses with Muscle Deformation Gait phase detection and quantitative evaluation are significant for synchronous robotic assistance of human walking, rehabilitation training, or diagnosis of human motion state. Especially, accurate heelcontact detection in a gait cycle is a key requirement for gait analysis applications. Some techniques have been proposed by utilizing wearable devices, however, existing systems typically require precise and continuous timeseries data at every single timestep for calibration, which largely increases the burden to users. Therefore, we propose a novel posingbased detection method through measuring muscle deformation, which only requires arbitrary and discrete posture data for calibration without walking. In this study, we firstly collected the posing data as the training set and gait data as the test set from participants through a FirstVR device. Then the Support Vector Machine was trained to be a twoclass classifier of heelcontact and nonheelcontact phases by using the collected muscle deformation data during posing. Finally we propose an efficient evaluation system by taking advantage of OpenPose to automatically label our continuous gait data. Experimental results demonstrate the muscle deformation sensor could correctly detect heelcontact with approximately 80% accuracy during walking, which shows the feasibility of posingbased method with muscle deformation information for heelcontact detection.

Gait Segmentation Method Using a Plantar Pressure Measurement System with CustomMade Capacitive Sensors Gait analysis has been widely studied by researchers due to the impact in clinical fields. It provides relevant information on the condition of a patientxe2x80x99s pathologies. In the last decades, different gait measurement methods have been developed in order to identify parameters that can contribute to gait cycles. Analyzing those parameters, it is possible to segment and identify different phases of gait cycles, making these studies easier and more accurate. This paper proposes a simple gait segmentation method based on plantar pressure measurement. Current methods used by researchers and clinicians are based on multiple sensing devices (e.g., multiple cameras, multiple inertial measurement units (IMUs)). Our proposal uses plantar pressure information from only two sensorized insoles that were designed and implemented with eight custommade flexible capacitive sensors. An algorithm was implemented to calculate gait parameters and segment gait cycle phases and subphases. Functional tests were performed in six healthy volunteers in a 10 m walking test. The designed inshoe insole presented an average power consumption of 44 mA under operation. The system segmented the gait phases and subphases in all subjects. The calculated percentile distribution between stance phase time and swing phase time was almost 60%40%, which is aligned with literature reports on healthy subjects. Our results show that the system achieves a successful segmentation of gait phases and subphases, is capable of reporting COP velocity, double support time, cadence, stance phase time percentage, swing phase time percentage, and double support time percentage. The proposed system allows for the simplification of the assessment method in the recovery process for both patients and clinicians.