Gait Intention Prediction Using a Lower-Limb Musculoskeletal Model and Long Short-Term Memory Neural Networks.

The prediction of gait motion intention is essential for achieving intuitive control of assistive devices and diagnosing gait disorders. To reduce the cost associated with using multimodal signals and signal processing, we proposed a novel method that integrates machine learning with musculoskeletal modelling techniques for the prediction of time-series joint angles, using only kinematic signals. Additionally, we hypothesised that a stacked long short-term memory (LSTM) neural network architecture can perform the task without relying on any ahead-of-motion features typically provided by electromyography signals. Optical cameras and inertial measurement unit (IMU) sensors were used to track level gait kinematics. Joint angles were modelled using the musculoskeletal model. The optimal LSTM architecture in fulfilling the prediction task was determined. Joint angle predictions were performed for joints on the sagittal plane, benefiting from joint angle modelling using signals from optical cameras and IMU sensors. Our proposed method predicted the upcoming joint angles in the prediction time of 10 ms, with an averaged root mean square error of 5.3° and a coefficient of determination of 0.81. Moreover, in support of our hypothesis, the recurrent stacked LSTM network demonstrated its ability to predict intended motion accurately and efficiently in gait, outperforming two other neural network architectures: a feedforward MLP and a hybrid LSTM-MLP. The method paves the way for the development of a cost-effective, single-modal control system for assistive devices in gait rehabilitation.

A Hybrid Method Integrating A Musculoskeletal Model with Long Short-Term Memory (LSTM) for Human Motion Prediction.

So far, it shows a growing interest in the biomechanics community in the development of wearable technologies and their clinical applications, which enables the diagnosis of movement disorders and design of the rehabilitation interventions. To provide reliable feedback in the human-machine interface for advanced rehabilitation devices, methods to predict motion intention was developed which aim to generate future human motion based on the measured motion. An inertial measurement unit (IMU) is a promising device for motion tracking, with the advantages of low cost and high convenience in sensor placement to measure motion in almost every environment. However, it reveals that few contributions have been devoted to human motion prediction with pure IMU data. Thus, we propose a hybrid method integrating a musculoskeletal (MSK) model and the long short-term memory (LSTM) artificial neural network (ANN) to predict human motion. The proposed method was capable to predict motion in the daily tasks (stand-to-sit-to-stand and walking) for healthy participants: the predicted knee joint angles had an RMSE of 2.93° when compared to measured knee joint angles from the IMU data. The proposed method outperformed the methods based on the ANN/MSK model (RMSE of 31.15°) and LSTM without the integration of the MSK model (RMSE of 31.26°) in the motion prediction. Clinical Relevance- This proposed model based on IMU data alone has the great potential to become a low-cost, easy-to-use alternative in motion prediction to interact with advanced rehabilitation devices in clinical practice.

An Improved Method for Monitoring Multiscale Plant Species Diversity of Alpine Grassland Using UAV: A Case Study in the Source Region of the Yellow River, China.

Plant species diversity (PSD) is essential in evaluating the function and developing the management and conservation strategies of grassland. However, over a large region, an efficient and high precision method to monitor multiscale PSD (α-, ß-, and γ-diversity) is lacking. In this study, we proposed and improved an unmanned aerial vehicle (UAV)-based PSD monitoring method (UAVB) and tested the feasibility, and meanwhile, explored the potential relationship between multiscale PSD and precipitation on the alpine grassland of the source region of the Yellow River (SRYR), China. Our findings showed that: (1) UAVB was more representative (larger monitoring areas and more species identified with higher α- and γ-diversity) than the traditional ground-based monitoring method, though a few specific species (small in size) were difficult to identify; (2) UAVB is suitable for monitoring the multiscale PSD over a large region (the SRYR in this study), and the improvement by weighing the dominance of species improved the precision of α-diversity (higher R 2 and lower P values of the linear regressions); and (3) the species diversity indices (α- and ß-diversity) increased first and then they tended to be stable with the increase of precipitation in SRYR. These findings conclude that UAVB is suitable for monitoring multiscale PSD of an alpine grassland community over a large region, which will be useful for revealing the relationship of diversity-function, and helpful for conservation and sustainable management of the alpine grassland.