Fast and accurate gait phase detection is essential to achieve effective powered lower-limb prostheses and exoskeletons. As the versatility but also the complexity of these robotic devices increases, the research on how to make gait detection algorithms more performant and their sensing devices smaller and more wearable gains interest. A functional gait detection algorithm will improve the precision, stability, and safety of prostheses, and other rehabilitation devices. In the past years the state-of-the-art has advanced significantly in terms of sensors, signal processing, and gait detection algorithms. In this review, we investigate studies and developments in the field of gait event detection methods, more precisely applied to prosthetic devices. We compared advantages and limitations between all the proposed methods and extracted the relevant questions and recommendations about gait detection methods for future developments.

Throughout the last decade, a whole new generation of powered transtibial prostheses and exoskeletons has been developed. However, these technologies are limited by a gait phase detection which controls the wearable device as a function of the activities of the wearer. Consequently, gait phase detection is considered to be of great importance, as achieving high detection accuracy will produce a more precise, stable, and safe rehabilitation device. In this paper, we propose a novel gait percent detection algorithm that can predict a full gait cycle discretised within a 1% interval. We called this algorithm an exponentially delayed fully connected neural network (ED-FNN). A dataset was obtained from seven healthy subjects that performed daily walking activities on the flat ground and a 15-degree slope. The signals were taken from only one inertial measurement unit (IMU) attached to the lower shank. The dataset was divided into training and validation datasets for every subject, and the mean square error (MSE) error between the model prediction and the real percentage of the gait was computed. An average MSE of 0.00522 was obtained for every subject in both training and validation sets, and an average MSE of 0.006 for the training set and 0.0116 for the validation set was obtained when combining all subjects’ signals together. Although our experiments were conducted in an offline setting, due to the forecasting capabilities of the ED-FNN, our system provides an opportunity to eliminate detection delays for real-time applications.

Locomotion mode recognition provides the prosthesis control with the information on when to switch between different walking modes, whereas the gait phase detection indicates where we are in the gait cycle. But powered prostheses often implement a different control strategy for each locomotion mode to improve the functionality of the prosthesis. Existing studies employed several classical machine learning methods for locomotion mode recognition. However, these methods were less effective for data with complex decision boundaries and resulted in misclassifications of motion recognition. Deep learning-based methods potentially resolve these limitations as it is a special type of machine learning method with more sophistication. Therefore, this study evaluated three deep learning-based models for locomotion mode recognition, namely recurrent neural network (RNN), long short-term memory (LSTM) neural network, and convolutional neural network (CNN), and compared the recognition performance of deep learning models to the machine learning model with random forest classifier (RFC). The models are trained from data of one inertial measurement unit (IMU) placed on the lower shanks of four able-bodied subjects to perform four walking modes, including level ground walking (LW), standing (ST), and stair ascent/stair descent (SA/SD). The results indicated that CNN and LSTM models outperformed other models, and these models were promising for applying locomotion mode recognition in real-time for robotic prostheses.

Purpose: COVID-19 hits every country’s health-care system and economy. There is a trend toward using automation technology in response to the COVID-19 crisis not only in developed countries but also in those with lower levels of technology development. However, current studies mainly focus on the world level, and only a few ones report deployments at the country level. The purpose of this paper is to investigate the use of automation solutions in Vietnam with locally available materials mainly in the first wave from January to July 2020. Design/methodology/approach: The authors collected COVID-related automation solutions during the first wave of COVID-19 in Vietnam from January to July 2020 through a search process. The analysis and insights of a panel consisting of various disciplines (i.e. academia, health care, government, entrepreneur and media) aim at providing a clear picture of how and to what extent these solutions have been deployed. Findings: The authors found seven groups of solutions from low to high research and development (R&D) levels deployed across the country with various funding sources. Low R&D solutions were widely spread owing to simplicity and affordability. High R&D solutions were mainly deployed in big cities. Most of the solutions were deployed during the first phases when international supply chains were limited with a significant contribution of the media. Higher R&D solutions have opportunities to be deployed in the reopening phase. However, challenges can be listed as limited interdisciplinary research teams, market demand, the local supporting industry, end-user validation and social-ethical issues. Originality/value: To the authors’ best knowledge, this is the first study analyzing the use of automation technology in response to COVID-19 in Vietnam and also in a country in Southeast Asia. Lessons learned from these current deployments are useful for future emerging infectious diseases. The reality of Vietnam’s automation solutions in response to COVID-19 might be a reference for other developing countries with similar social-economic circumstances and contributes to the global picture of how different countries adopt technology to combat COVID-19.