Obstructive sleep apnea (OSA) syndrome is a common sleep disorder. As an alternative to polysomnography (PSG) for OSA screening, the current automatic OSA detection methods mainly concentrate on feature extraction and classifier selection based on physiological signals. It has been reported that OSA is, along with autonomic nervous system (ANS) dysfunction and heart rate variability (HRV), a useful tool for ANS assessment. Therefore, in this paper, eight novel indices of short-time HRV are extracted for OSA detection, which are based on the proposed multi-bands time-frequency spectrum entropy (MTFSE) method. In the MTFSE, firstly, the power spectrum of HRV is estimated by the Burg–AR model, and the time-frequency spectrum image (TFSI) is obtained. Secondly, according to the physiological significance of HRV, the TFSI is divided into multiple sub-bands according to frequency. Last but not least, by studying the Shannon entropy of different sub-bands and the relationships among them, the eight indices are obtained. In order to validate the performance of MTFSE-based indices, the Physionet Apnea–ECG database and K-nearest neighbor (KNN), support vector machine (SVM), and decision tree (DT) classification methods are used. The SVM classification method gets the highest classification accuracy, its average accuracy is 91.89%, the average sensitivity is 88.01%, and the average specificity is 93.98%. Undeniably, the MTFSE-based indices provide a novel idea for the screening of OSA disease.
Purpose In large-scale environments and unstructured scenarios, the accuracy and robustness of traditional light detection and ranging (LiDAR) simultaneous localization and mapping (SLAM) algorithms are reduced, and the algorithms might even be completely ineffective. To overcome these problems, this study aims to propose a 3D LiDAR SLAM method for ground-based mobile robots, which uses a 3D LiDAR fusion inertial measurement unit (IMU) to establish an environment map and realize real-time localization. Design/methodology/approach First, we use a normal distributions transform (NDT) algorithm based on a local map with a corresponding motion prediction model for point cloud registration in the front-end. Next, point cloud features are tightly coupled with IMU angle constraints, ground constraints and gravity constraints for graph-based optimization in the back-end. Subsequently, the cumulative error is reduced by adding loop closure detection. Findings The algorithm is tested using a public data set containing indoor and outdoor scenarios. The results confirm that the proposed algorithm has high accuracy and robustness. Originality/value To improve the accuracy and robustness of SLAM, this method proposed in the paper introduced the NDT algorithm in the front-end and designed ground constraints and gravity constraints in the back-end. The proposed method has a satisfactory performance when applied to ground-based mobile robots in complex environments experiments.
The complex fluctuation of heart rate variability reflects the autonomous regulation function of the heart. In this paper, a novel method of measuring the heart rate variability is proposed. Firstly, the heart rate variability signal is decomposed by the improved complete ensemble empirical mode decomposition with adaptive noise method, and the multiple intrinsic mode functions are obtained, and the bubble entropy of each intrinsic mode function is calculated to obtain an entropy value vector. Then, the vector is mapped to a network based on a limited penetrable horizontal visibility graph method. By calculating various characteristic parameters of the network, the coupling relationship between the nonlinear features of heart rate variability in different time-frequency scale states are studied. The characteristic parameters include mean value of aggregation coefficient (MC), the characteristic path length (CL), the topological entropy of network (TE), the network level weighted bubble value (WB), and the pseudo mean value of node weight (PW). Firstly, the heart rate variabilities of 29 patients with congestive heart failure and 29 normal sinus heart rhythm subjects are analyzed by time domain, frequency domain and ICBN analysis method, the <i>T</i> test is used for statistical analysis, and Fisher discriminant method is used for classification. The results show that the time domain triangular index HRVTI, frequency domain index LF/HF, WB, PW and CL in ICBN have statistical differences. The accuracy rate of recognition model based on WB, CL, frequency domain index LF/HF and Fisher discriminant method is 89.66%. Secondly, the heart rate variabilities of 43 patients with atrial fibrillation arrhythmia and another 43 normal sinus heart rhythm subjects are analyzed by the same methods, including the time domain analyzed method, frequency domain analyzed method, and ICBN analyzed method. Then, the T test is also used for statistical analysis, and Fisher discriminant method is used for classification. The results show that using the time domain index pNN5 and RMSSD, frequency index LF/HF, ICBN index WB and PW as the feature vectors, and the Fisher discriminant mode as the classifier, the accuracy rate of recognition for atrial fibrillation arrhythmia is 91.86%. From these results it is concluded that the ICBN method provides a new idea for the heart rate variability measurement.
Excessive mental workload affects human health and may lead to accidents. This study is motivated by the need to assess mental workload in the process of human-robot interaction, in particular, when the robot performs a dangerous task. In this study, the use of heart rate variability (HRV) signals with different time scales in mental workload assessment was analyzed. A humanoid dual-arm robot that can perform dangerous work was used as a human-robot interaction object. Electrocardiogram (ECG) signals of six subjects were collected in two states: during the task and in a relaxed state. Multiple time-scale (1, 3, and 5 min) HRV signals were extracted from ECG signals. Then, we extracted the same linear and nonlinear features from the HRV signals at different time scales. The performance of machine learning algorithms using the different time-scale HRV signals obtained during the human-robot interaction was evaluated. The results show that for the per-subject case with a 3 min HRV signal length, the K -nearest neighbor classifier achieved the best mental workload classification performance. For the cross-subject case with a 5 min time-scale signal length, the gentle boost classifier achieved the best mental workload classification accuracy. This study provides a novel research idea for using HRV signals to measure mental workload during human-robot interaction.
Mobile robots, including ground robots, underwater robots, and unmanned aerial vehicles, play an increasingly important role in people’s work and lives. Path planning and obstacle avoidance are the core technologies for achieving autonomy in mobile robots, and they will determine the application prospects of mobile robots. This paper introduces path planning and obstacle avoidance methods for mobile robots to provide a reference for researchers in this field. In addition, it comprehensively summarizes the recent progress and breakthroughs of mobile robots in the field of path planning and discusses future directions worthy of research in this field. We focus on the path planning algorithm of a mobile robot. We divide the path planning methods of mobile robots into the following categories: graph-based search, heuristic intelligence, local obstacle avoidance, artificial intelligence, sampling-based, planner-based, constraint problem satisfaction-based, and other algorithms. In addition, we review a path planning algorithm for multi-robot systems and different robots. We describe the basic principles of each method and highlight the most relevant studies. We also provide an in-depth discussion and comparison of path planning algorithms. Finally, we propose potential research directions in this field that are worth studying in the future.
Robots instead of humans work in unstructured environments, expanding the scope of human work. The interactions between humans and robots are indirect through operating terminals. The mental workloads of human increase with the lack of direct perception to the real scenes. Thus, mental workload assessment is important, which could effectively avoid serious accidents caused by mental overloading. In this paper, the operating object is a dual-arm robot. The classification of operator’s mental workload is studied by using the heart rate variability (HRV) signal. First, two kinds of electrocardiogram (ECG) signals are collected from six subjects who performed tasks or maintained a relaxed state. Then, HRV data is obtained from ECG signals and 20 kinds of HRV features are extracted. Last, six different classifications are used for mental workload classification. Using each subject’s HRV signal to train the model, the subject’s mental workload is classified. Average classification accuracy of 98.77% is obtained using the K-Nearest Neighbor (KNN) method. By using the HRV signal of five subjects for training and that of one subject for testing with the Gentle Boost (GB) method, the highest average classification accuracy (80.56%) is obtained. This study has implications for the analysis of HRV signals characteristic of mental workload in different subjects, which could improve operators’ well-being and safety in the human-robot interaction process.
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