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Gait patterns are critical to health monitoring, gait impairment assessment, and wearable device control. Unrhythmic gait pattern detection under community-based conditions is a new frontier in this area. The present paper describes a high-accuracy gait phase estimation and prediction algorithm built on a two-stage artificial neural network. This work targets to develop an algorithm that can estimate and predict the gait cycle in real time using a portable controller with only two IMU sensors (one on each thigh) in the community setting. Our algorithm can detect the gait phase in unrhythmic conditions during walking, stair ascending, and stair descending, and classify these activities with standing. Moreover, our algorithm is able to predict both future intra- and inter-stride gait phases, offering a potential means to improve wearable device controller performance. The proposed data-driven algorithm is based on a dataset consisting of 5 able-bodied subjects and validated on 3 different able-bodied subjects. Under unrhythmic activity situations, validation shows that the algorithm can accurately identify multiple activities with 99.55% accuracy, and estimate ( rRMSE 0 : 6.3%) and predict 200-ms-ahead ( rRMSE 200 ms : 8.6%) the gait phase percentage in real time, which are on average 57.7 and 54.0% smaller than the error from the event-based method in the same conditions. This study showcases a solution to estimate and predict gait status for multiple unrhythmic activities, which may be deployed to controllers for wearable robots or health monitoring devices. [ABSTRACT FROM AUTHOR]

Stroke is the second leading cause of death and one of the leading causes of disability in the world. According to the World Health Organization, 11 million people suffer a stroke yearly. The cost of the disease is exorbitant, and the most widely used treatment is conventional physiotherapy. Therefore, assistive technology emerges to optimize rehabilitation and functional capabilities, but cost, robustness, usability, and long-term results still restrict the technology selection. This work aimed to develop a low-cost ankle orthosis, the G-Exos, a wearable exoskeleton to increase motor capability by assisting dorsiflexion, plantarflexion, and ankle stability. A hybrid system provided near-natural gait movements using active, motor, and passive assistance, elastic band. The system was validated with 10 volunteers with foot drop: seven with stroke, two with incomplete spinal cord injury (SCI), and one with acute inflammatory transverse myelitis (ATM). The G-Exos showed assistive functionality for gait movement. A Friedman test showed a significant difference in dorsiflexion amplitude with the use of the G-Exos compared to gait without the use of the G-Exos [x²(3) = 98.56, p < 0.001]. In addition, there was also a significant difference in ankle eversion and inversion comparing walking with and without the G-Exos [x²(3) = 36.12, p < 0.001]. The G-Exos is a robust, lightweight, and flexible assistive technology device to detect the gait phase accurately and provide better human-machine interaction. G-Exos training improved capability to deal with gait disorders, usability, and motor and functional recovery. Wearable assistive technologies lead to a better quality of life and contribute using in activities of daily living. [ABSTRACT FROM AUTHOR]