Continuous Daily Head and Trunk Acceleration Recording for the Assessment of Bone Stimulation in Astronauts

The acceleration of the head and hip along the x-, y-, and z-axis of 14 healthy subjects was recorded during two sessions of 12 consecutive hours. The magnitude, frequency content, and root mean square of the acceleration signals were used to determine the type of physical activity (sitting, standing, walking, etc.) during normal daily life on Earth. The acceleration signal slope (jerk) was also calculated to assess whether these activities were sufficient to maintain bone mineral density. These measurements indicated that the changes in vertical acceleration experienced by our subjects during normal daily life were presumably sufficient to maintain bone mineral density. However, these changes might not be sufficient for postmenopausal women and astronauts during long-term exposure to weightlessness during spaceflight

Application of Heart Rate Acceleration Motion Wireless Sensor Fusion in Individual Special Competitive Sports

Heart rate monitoring is becoming more and more important in the development of modern health industry. At present, wireless sensor network equipment is mainly used to realize the real-time or periodic monitoring of human heart rate, so as to realize the health management of specific people. At the same time, the monitoring and analysis technology of heart rate is also widely used in special competitive sports. Through the real-time monitoring and analysis of athletes’ heart rate, we can feedback and analyze their corresponding competitive state in real time, so as to monitor the sudden state of athletes, and also provide a basis for the improvement of athletes’ later sports level. Based on this, this paper will use a single-chip microcomputer as the central data processing unit of the monitoring system at the hardware level, and inertial sensor and heart rate sensor at the sensor level. The system will design data acquisition module, motion positioning module, low-power module, athlete heart rate acquisition module, and motion state recognition module. Aiming at the low accuracy of traditional heart rate acceleration motion wireless sensor in competitive sports athletes’ heart rate recognition and motion state recognition, this paper innovatively proposes an athlete heart rate recognition algorithm based on acceleration signal, which extracts the frequency-domain characteristics of motion signal. The time-domain and time-frequency characteristics of athletes’ acceleration signal are used to realize the recognition of athletes’ sports state, and the power spectrum cancellation technology is used to realize the accurate detection of athletes’ heart rate. In order to verify the advantages of the hardware system and algorithm in this paper, three sports with quiet, dynamic, and random dynamic characteristics are selected for experimental verification. The experimental results show that the software algorithm proposed in this paper has obvious accuracy advantages in quiet and dynamic competitive sports compared with the traditional algorithm.

Detailed analysis and comparison of different activity metrics

Actigraphic measurements are an important part of research in different disciplines, yet the procedure of determining activity values is unexpectedly not standardized in the literature. Although the measured raw acceleration signal can be diversely processed, and then the activity values can be calculated by different activity calculation methods, the documentations of them are generally incomplete or vary by manufacturer. These numerous activity metrics may require different types of preprocessing of the acceleration signal. For example, digital filtering of the acceleration signals can have various parameters; moreover, both the filter and the activity metrics can also be applied per axis or on the magnitudes of the acceleration vector. Level crossing-based activity metrics also depend on threshold level values, yet the determination of their exact values is unclear as well. Due to the serious inconsistency of determining activity values, we created a detailed and comprehensive comparison of the different available activity calculation procedures because, up to the present, it was lacking in the literature. We assessed the different methods by analysing the triaxial acceleration signals measured during a 10-day movement of 42 subjects. We calculated 148 different activity signals for each subject’s movement using the combinations of various types of preprocessing and 7 different activity metrics applied on both axial and magnitude data. We determined the strength of the linear relationship between the metrics by correlation analysis, while we also examined the effects of the preprocessing steps. Moreover, we established that the standard deviation of the data series can be used as an appropriate, adaptive and generalized threshold level for the level intersection-based metrics. On the basis of these results, our work also serves as a general guide on how to proceed if one wants to determine activity from the raw acceleration data. All of the analysed raw acceleration signals are also publicly available.

Detection of Parkinsonian Motor Symptoms Using Non-Invasive Motion Sensing Technology

Parkinson’s Disease (PD) is characterized by impaired movement, resting tremor, and muscle rigidity. The Unified PD Rating Scale (UPDRS) is a standardized protocol used by neurologists to measure progression of disease as well as evaluation of treatments. However, this examination is subjective, time consuming, and results can be affected by stress, diet, or sleep. Our goal is to develop a non-invasive device that can record objective clinically-relevant measurements during subtasks of the UPDRS to allow for remote evaluations, which would be beneficial considering the frequency of clinical visits for medication adjustments. Five healthy individuals (ages 21-59) completed UPDRS tasks 3.6 (pronation/supination of hands) and 3.17 (rest tremor amplitude). Participants performed these tasks twice, first normally and second simulating PD patients (tremor, bradykinesia, reduction of movement amplitude) after viewing example videos. Motion data including linear and angular accelerations in 3 dimensions was acquired using a lightweight wrist-mounted motion sensor. Three features were extracted: (1) Power of higher frequency components of the linear acceleration signal (rest task), as a measure of resting tremor amplitude. (2) Power of higher frequency components of the rotational acceleration signal (pronation/supination task), as a measure of bradykinesia. (3) Standard deviation of the local maxima of the rotational acceleration (pronation/supination task), as a measure of reduction in movement speed and amplitude. These features were used to correctly differentiate trials completed with and without simulated PD symptoms, using an SVM classifier with leave-one-out cross validation accuracy of 95%. These findings suggest it is possible to capture clinical features of PD using motion sensors. Future work in PD patients will examine how these measures correlate with UPDRS evaluations and whether they will be helpful in providing a quick, objective telehealth measure of progression and treatment response that can supplement current tools.

Acceleration Anomaly Detection Method for Sensor Axle Box of Unmanned Vehicle

Detecting the anomaly acceleration of the sensor’s axle box of unmanned vehicles is very important for judging the wear condition of vehicle track and evaluating the state of the track. A capacitive accelerometer is connected with acquisition equipment to collect the information of train axle box’s acceleration change when the vehicle is running; instrument amplifier AD8250 with a digitally programmable gain is selected as system signal conversion chip to realize acceleration signal conversion; sliding variance of axle box’s acceleration of the unmanned vehicle is calculated based on sliding variance statistical analysis method, which is confirmed by time window and distance window. Fixing the width of a sliding window according to the response statistics caused by the line excitation link, the acceleration sliding variance is compared with the standard one to determine whether the acceleration is in an anomaly state. The test results show that the anomaly acceleration of the sensor axle box of the unmanned vehicle detected by the proposed method is consistent with the actual results, which provides a reliable basis for vehicle track condition assessment.

Integrating acceleration signal processing and image segmentation for condition assessment of asphalt roads

A proactive road maintenance system enables agencies to better allocate resources to manage their road networks. An inventory of the roads’ conditions is an essential component of such maintenance program. This research project proposes a hybrid system to asses the condition of the asphalt roads, which uses a dashboard-mounted smartphone to simultaneously collect the acceleration response of a vehicle and the video footage of the road surface while driving. The system analyzes acceleration data for anomalous events that could indicate a defect. Then the computer vision module of the system applies semantic segmentation in the corresponding frame to the detected anomaly to identify defects. This system demonstrated 84% recall and 88% precision rates in detection of anomalies in two road segments. Despite these promising results, the system can only detect the defects that are passed over and it could miss some defects with small acceleration responses, such as traverse cracks.

An Assembly Status Identification Method for Wedge Belt Automatic Assembly Based on Dynamic Feature

The assembly of the wedge belt joint relies on the sense and experience of human, which limit the application of automatic assembly technology. In order to implement automatic assembly for higher production quality and efficiency, an assembly status identification method based on dynamic feature is proposed. The contact status of the wedge belt in assembly is analysed and the dynamic model is formulated. The dynamic features for identification are compared and the signal of acceleration is selected. The identification method of assembly status is proposed based on the acceleration signal. An experiment is designed to verify the identification method. The experiment result shows that the amplifier of the acceleration decreases much more sharply as it fits well. The proposed identification method is effective and stable, which could be used for the automatic assembly.

Unscented Kalman-filter to manage the handling-comfort trade-off of quarter-of-vehicle

This paper investigates managing the comfort-handling trade-off of a quarter car suspension system using a Kalman filter. Using the unscented Kalman filter, the adapted feedback input signal is extracted based on the vertical acceleration signals of the chassis and wheel. Considering the chassis acceleration signal as the primary feedback to maintain a required comfort level, it is continuously adapted to keep an acceptable level of road handling. Compared with the traditional methods, which rely on the combination of the two modes of comfort and handling through an intermediate variable to manage the contradiction, this method focuses on comfort and improves the process of the road handling automatically. The proposed strategy is evaluated using simulation in MATLAB and the results show the feasibility of this method in managing the handling-comfort trade-off. In addition, mathematical relationships that allow this control strategy to be derived were shown. Moreover, the effects of road disturbances amplitudes and road quality on the performance of the proposed control strategy were investigated. Furthermore, the performance of the proposed method is compared with that of the hybrid-hook and the results show the superiority of the proposed algorithm.