Continuous health status monitoring of infants is achieved with the development and fusion of wearable sensing technologies, wireless communication techniques and a low energy-consumption microprocessor with high performance data processing algorithms. As a clinical tool applied in the constant monitoring of physiological parameters of infants, wearable sensor systems for infants are able to transmit the information obtained inside an infant’s body to clinicians or parents. Moreover, such systems with integrated sensors can perceive external threats such as falling or drowning and warn parents immediately. Firstly, the paper reviews some available wearable sensor systems for infants; secondly, we introduce the different modules of the framework in the sensor systems; lastly, the methods and techniques applied in the wearable sensor systems are summarized and discussed. The latest research and achievements have been highlighted in this paper and the meaningful applications in healthcare and behavior analysis are also presented. Moreover, we give a lucid perspective of the development of wearable sensor systems for infants in the future.

Inertial measurement unit (IMU)-based sensing systems have received extensive attention for wearable gait measurement due to their small size, low price, and high accuracy. However, reconstructing walking dynamics usually requires a large number of sensor units attached to major body segments, which is generally not suitable for daily-life scenarios. Previous studies showed that two shank-mounted IMUs can detect gait events and estimate many important gait parameters. However, it is unclear whether this simple two-IMU system can be used to reconstruct walking dynamics. In this article, we propose a two-stage method for predicting walking dynamics from measurements of two shank-mounted IMUs. Displacements of ankle joints and shank angles are first estimated from the IMU outputs. Then, a model-based whole-step optimization approach is used to solve the gait dynamics by tracking the estimated shank motion. The proposed method is validated with both normal and asymmetric walking data, achieving a root-mean-square error of 5.3°, 6.1°, and 6.8° in the hip, knee, and ankle joint angle estimation and 3.9% and 12.2% body weight in the fore-aft and vertical ground reaction force estimation. Comparing with the reported results in the literature, the number of IMUs is significantly reduced with similar accuracy. This implies that gait dynamic information may be estimated from very limited measurements and the inherent gait characteristics can be used to reconstruct gait dynamics from the motion of a small number of segments.

Human pose estimation provides valuable information for biomedical research on human movement and applications such as entertainment and physical exercise. The fusion of visual and inertial data has been increasingly studied in the past two decades to take advantage of these two naturally complementary sensing modalities. In this paper, we systematically reviewed the advances in visual-inertial fusion-based human pose estimation with a thorough search for related studies in five mainstream literature databases. A total of 54 studies were identified and included by screening 4586 records retrieved in the review process. The estimation targets, hardware design, fusion methods, evaluation metrics, and system accuracy of these included studies were summarized and categorized for analysis. From these state-of-the-art studies, challenges in terms of mobility, calibration, real-time estimation, and evaluation methods are further discussed in depth and possible directions to overcome these issues are recommended. We expect this systematic review can provide researchers and engineers with a thorough idea of the progress and performance in visual-inertial fusion-based human pose estimation. We also hope that the discussions on challenges and possible future directions can facilitate future work to improve such systems and promote their applications in real life.

Upper-body kinematics is essential for motor function assessment and robot-assisted rehabilitation training. Wearable sensor systems such as inertial measurement units (IMUs) provide affordable solutions to replace lab-based motion capture systems for use in daily life. However, the sensor-to-segment calibration often relies on predefined posture or movements which is hard to perform accurately, particularly for patients with a limited range of motion. A visual-inertial sensor system is presented which includes three sensor modules attached to the trunk, upper arm, and forearm. Each module has an IMU and an ArUco marker which can be captured by a camera and the driftless orientation of the modules is computed from visual-inertial fusion. The sensor-to-segment transformations are calibrated from a period of arbitrary arm movements in either a 2D plane or 3D space, simulating the training process assisted by end-effector robots. Experiments were conducted to validate the feasibility and evaluate the accuracy of the proposed method. The estimated shoulder and elbow joint angles correlated well (>0.986) with the ground truth from the optical motion capture system. The joint angles presented low root mean square errors (<4°) except for the forearm pronation-supination angle (9.34°) which relied on manual alignment. The sensor system provides a simple and easy-to-use solution for movement assessment during robot-assisted training.