Limb motion capture is essential in human motion-recognition, motor-function assessment and dexterous human-robot interaction for assistive robots. Due to highly dynamic nature of limb activities, conventional inertial methods of limb motion capture suffer from serious drift and instability problems. Here, a motion capture method with integral-free velocity detection is proposed and a wearable device is developed by incorporating micro tri-axis flow sensors with micro tri-axis inertial sensors. The device allows accurate measurement of three-dimensional motion velocity, acceleration, and attitude angle of human limbs in daily activities, strenuous, and prolonged exercises. Additionally, we verify an intra-limb coordination relationship exists between thigh and shank in human walking and running, and establish a neural network model for it. Using the intra-limb coordination model, dynamic motion capture of human lower limbs including thigh and shank is tactfully implemented by a single shank-worn device, which simplifies the capture device and reduces cost. Experiments in strenuous activities and long-time running validate excellent performance and robustness of the wearable device in dynamic motion recognition and reconstruction of human limbs.

Objective: This paper proposes a wearable human motion tracking device using micro flow sensor incorporating with micro accelerometer, which allows detecting postures of human limbs in dynamic motions. Methods: A micro flow sensor is used to detect motion velocity, from which motion acceleration can be estimated. Thereby the limb postures are figured out by using the components of the gravity acceleration that are determined by subtracting the motion accelerations from the accelerometer outputs. The motion tracking device is packaged in a watch-type case, which allows it wearable. A Kalman filter is employed to implement sensor data fusion. Results: The motion velocity, motion acceleration, and postures of human limbs can be effectively detected by the proposed motion tracking device and methodology. Experiments including human limb tracking are conducted to validate the effectiveness of the proposed device and methodology. Conclusion: The experimental results show that the measurements of human limb motion and posture are feasible and free of accumulative errors. Significance: The flow-aid motion tracking device is wearable, environmental restriction free and practicable for long-term monitoring, and provides a promising approach for physical rehabilitation, intelligent prosthesis, personal athletic training, and etc.

Micromachined fluid inertial sensors are an important class of inertial sensors, which mainly includes thermal accelerometers and fluid gyroscopes, which have now been developed since the end of the last century for about 20 years. Compared with conventional silicon or quartz inertial sensors, the fluid inertial sensors use a fluid instead of a solid proof mass as the moving and sensitive element, and thus offer advantages of simple structures, low cost, high shock resistance, and large measurement ranges while the sensitivity and bandwidth are not competitive. Many studies and various designs have been reported in the past two decades. This review firstly introduces the working principles of fluid inertial sensors, followed by the relevant research developments. The micromachined thermal accelerometers based on thermal convection have developed maturely and become commercialized. However, the micromachined fluid gyroscopes, which are based on jet flow or thermal flow, are less mature. The key issues and technologies of the thermal accelerometers, mainly including bandwidth, temperature compensation, monolithic integration of tri-axis accelerometers and strategies for high production yields are also summarized and discussed. For the micromachined fluid gyroscopes, improving integration and sensitivity, reducing thermal errors and cross coupling errors are the issues of most concern.

For personalized rehabilitation or sports training, it is necessary to monitor dynamic motions and track postures of human body and limbs. Traditional methods using micro inertial sensors usually suffer from drift problems in tracking dynamic motions. In this paper, a wearable flow-MIMU human motion capture device is proposed by incorporating micro flow sensor with micro inertial measurement unit (MIMU). Motion velocity is detected by a micro flow sensor and utilized to figure out the motion acceleration. The gravity accelerations are extracted by eliminating the motion accelerations from the accelerometer outputs. Finally, posture estimation is implemented by using a tailor-designed Kalman-based data fusion of the gyroscope outputs and the extracted gravity accelerations. The flow-MIMU device with wireless communication is designed like a watch to be wearable. Experimental results validate that the motion velocity, acceleration and posture of human limb are determined accurately and free of accumulative error in monitoring of dynamic motions using the proposed method. The wearable flow-MIMU device provides an advantageous monitoring approach for applications of personalized rehabilitation, sports training, intelligent prosthetics, etc.

Attitude estimation is an important issue in motion control and inertial navigation for spacecrafts, aircrafts, self-driving vehicles, underwater robots, and portable navigation systems. Many researchers have proposed various attitude estimation approaches, but rarely considered their applicability to high or ultra-high rotation scenarios. Conventional attitude estimation in a strap-down inertial navigation system is based on the integral calculation of rotation kinematics, which unavoidably suffers from accumulative and noncommutativity errors in high dynamics and high rotations. In this paper, an improved algorithm of multi-sample equivalent rotation vector using angular rates instead of angular increments is proposed to calculate attitude angles for enhancing accuracy. An advanced complementary filter using the angular rate-based rotation vector is further developed to implement sensor fusion for attitude determination under high or ultra-high rotations. The effectiveness and advantages of the proposed attitude estimation methodology are validated through simulation experiments.

Comprehensive and quantitative assessment of human physical activity in daily life is valuable for healthcare, especially for those who suffer from obesity and neurological disorders or are at high risk of dementia. Common wearable devices, e.g., smartwatches, are insufficient and inaccurate for monitoring highly dynamic limb movements and assessing human motion. Here, we report a new wearable leg movement monitoring system incorporating a custom-made motion sensor with machine learning algorithm to perceive human motion accurately and comprehensively during diverse walking and running actions. The system enables real-time multimodal perceptions of personal identity, motion state, locomotion speed, and energy expenditure for wearers. A general law of extracting real-time metabolic energy from leg movements is verified although individual gaits show differences. In addition, we propose a novel sensing configuration combining unilateral lower leg movement velocity with its angular rate to achieve high accuracy and good generalizability while simplifying the wearable system. Advanced performances in personal identification (accuracy of 98.7%) and motion-state recognition (accuracy of 93.7%) are demonstrated. The wearable system also exhibites high-precision real-time estimations of locomotion speed (error of 3.04% to 9.68%) and metabolic energy (error of 4.18% to 14.71%) for new subjects across various time-varying conditions. The wearable system allows reliable leg movement monitoring and quantitative assessment of bodily kinematic and kinetic behaviors during daily activities, as well as safe identity authentication by gait parameters, which would greatly facilitate smart life, personal healthcare, and rehabilitation training.