Recently, the estimation of joint kinetics such as joint force and moment using wearable inertial sensors has received great attention in biomechanics. Generally, the joint force and moment are calculated though inverse dynamics using segment kinematic data, ground reaction force, and moment. However, this approach has problems such as estimation error of kinematic data and soft tissue artifacts, which can lead to inaccuracy of joint forces and moments in inverse dynamics. This study aimed to apply a recurrent neural network (RNN) instead of inverse dynamics to joint force and moment estimation. The proposed RNN could receive signals from inertial sensors and force plate as input vector and output lower extremity joints forces and moments. As the proposed method does not depend on inverse dynamics, it is independent of the inaccuracy problem of the conventional method. Experimental results showed that the estimation performance of hip joint moment of the proposed RNN was improved by 66.4% compared to that of the inverse dynamics-based method.

Inertial measurement unit (IMU)-based 3D joint angle estimation have a wide range of important applications, among them, in gait analysis and exoskeleton robot control. Conventionally, the joint angle was determined via the estimation of 3D orientation of each body segment using 9-axis IMUs including 3-axis magnetometers. However, a magnetometer is limited by magnetic disturbance in the vicinity of the sensor, which highly affects the accuracy of the joint angle. Accordingly, this study aims to estimate the joint angle using the 6-axis IMU signals composed of a 3-axis accelerometer and a 3-axis gyroscope without a magnetometer. This paper proposes a recurrent neural network (RNN) model, which indirectly utilizes the joint kinematic constraint and thus estimates joint angles based on 6-axis IMUs without using a magnetometer signal. The performance of the proposed model was validated for a mechanical joint and human elbow joint, under magnetically disturbed environments. Experimental results showed that the proposed RNN approach outperformed the conventional approach based on a Kalman filter (KF), i.e., RNN 3.48° vs. KF 10.01° for the mechanical joint and RNN 7.39° vs. KF 21.27° for the elbow joint.

The objective of this study is to investigate a novel temperature and humidity prediction algorithm for smart greenhouse based on the machine learning method. The smart greenhouse is known to increase farm production by automatically controlling temperature and humidity and other factors. However, maintaining constant inside temperature and humidity in the conventional smart greenhouse system is still a problem because of the multiple time delay elements. To solve the problems, prediction control scheme is required. But, since the system is highly nonlinear with the lack of sensory data, predicting accurate temperature and humidity is very challenging. In this paper, the multi-dimensional Long Short-Term Memory networks (LSTMs) is being applied to deal with the unstructured greenhouse environmental data. The designed LSTMs learning model is trained with the 27 dimensional data which comprises of all the greenhouse control parameter and environmental sensory data. The prediction performance was evaluated using the short, mid and long term experiments. Also, the comparison with the conventional recurrent neural networks (RNNs) based prediction algorithm was done using the experimental results and later on discussions.