Gait phase analysis is widely used in disease diagnosis, rehabilitation training, and other fields by studying the characteristics of human gait. It systematically evaluates human body’s skeletal muscles and nerves with combined disciplines. Microelectromechanical system (MEMS) inertial sensors are extensively used in attitude detection because of its high-precision, portability, and good real-time performance in time-domain analysis. In this article, we presented a multi-degree-of-freedom MEMS gait detection method, which resolved the problems of single sensor and limited gait phase. We designed a sensor-based gait signal acquisition system, in which gait data acquisition program and feature analysis algorithm were compiled to verify the feasibility of the proposed method. We performed coordinate transformation and corrected position information to eliminate the gait phase detection error caused by random noise interference. Acceleration and angular velocity information were collected from 20 experimenters. We applied an adaptive threshold gait phase detection algorithm to classify the gait information collected by single sensor. To improve the results of gait phase classification, we used multisensor redundant measurement to analyze characteristics of five gait phases. The acceleration and angular velocity information collected by the three sensors placed at instep, ankle, and thigh were input into support vector machine (SVM). The classification results of the five gait phases are approximately 90%. Lastly, we built a human body structure model to simulate human motion in real time, realizing the real-time gait phase detection, which proves effectiveness of the proposed algorithm.

Gait has been widely used in the fields of elderly care, posture correction, and identity recognition. By analyzing and processing the motion parameters and attitude data collected by the sensor, the current gait was determined. In order to pointedly prevent falling in daily life, we proposed a gait pattern classification method based on multisensor. Falling and the gait patterns (standing, sitting/rising, squatting/rising, walking, and running) that falling is likely to happen in daily life were distinguished. Besides, we integrated pressure signals with acceleration signals to compensate for the insufficient data provided by single sensor that cannot fully reflect the complex human motion and solved the problem that falling detection based on acceleration signals is prone to misclassify because certain postures are of similar acceleration changes with falling. For further data analysis, the collected gait data were then transmitted to upper machine for signal processing through the designed wireless network. Combined with the characteristics of gait patterns, we analyzed the corresponding pressure signals, acceleration signals, and resultant acceleration signals. Wavelet energy entropy features and wavelet packet energy features were subsequently extracted from the collected gait data. Finally, we input the randomly selected test data into the established extreme learning machine (ELM) and $K$ -nearest neighbor (KNN) model to test gait pattern recognition effects. The performance of ELM algorithm was better in terms of processing time and classification results, with the highest average identification accuracy, precision, and recall rate of 0.974, 0.937, and 0.936, respectively. Besides, precision–recall (PR) curve was optimal, with the largest area of 0.973. Our presented algorithm responded rapidly and prevented falling in daily lifetime, which can be applied to health monitoring systems to detect daily activities of the elderly promptly.

Accurate and reliable prediction of bearing remaining useful life (RUL) is crucial to the prognostics and health management of rotation machinery. Despite the rapid progress of data-driven methods, the generalizability of data-driven models remains an open issue to be addressed. In this article, we tackle this challenge by resolving the feature misalignment problem that arises in extracting features from the raw vibration signals. Toward this goal, we introduce a logarithmic cumulative transformation (LCT) operator consisting of cumulative, logarithmic, and another cumulative transformation for feature extraction. In addition, we propose a novel method to estimate the reliability associated with each RUL prediction by integrating a linear regression model and an auxiliary exponential model. The linear regression model rectifies bias from neural network's point predictions while the auxiliary exponential model fits the differential slopes of the linear models and generates the upper and lower bounds for building the reliability indicator. The proposed approach comprised of LCT, an attention GRU-based encoder–decoder network, and reliability evaluation is validated on the FEMETO-ST dataset. Computational results demonstrate the superior performance of the proposed approach several other state-of-the-art methods.

Respiratory diseases including apnea are often accompanied by abnormal respiratory depth, frequency, and rhythm. If different abnormal respiratory patterns can be detected and recorded, with their depth, frequency, and rhythm analyzed, the detection and diagnosis of respiratory diseases can be achieved. High-frequency millimeter-wave radar (76–81 GHz) has low environmental impact, high accuracy, and small volume, which is more suitable for respiratory signal detection and recognition compared with other contact equipment. In this paper, the experimental platform of frequency-modulated continuous wave (FMCW) radar was built at first, realizing the noncontact measurement of vital signs. Secondly, the energy intensity and threshold of respiration signal during each period were calculated by using the rectangular window, and the accurate judgment of apnea was realized via numerical comparison. Thirdly, the features of respiratory and heart rate signals, the number of peaks and valleys, the difference between peaks and valleys, the average and the standard deviation of normalized short-term energy, and the average and the standard deviation and the minimum of instantaneous frequency, were extracted and analyzed. Finally, support vector machine (SVM) and K-nearest neighbor (KNN) were used to classify the extracted features, and the accuracy was 98.25% and 88.75%, respectively. The classification and recognition of respiratory patterns have been successfully realized.

Surface electromyography (sEMG) based hand gesture recognition has garnered widespread attention in rehabilitation robotics due to its non-invasive measurement and intuitive motion decoding. However, affected by various factors such as individual differences, achieving cross-user adaptability and longterm reliability for sEMG classification poses a significant challenge. Existing domain adaptation methods primarily focus on global distribution alignment to mitigate statistical distribution discrepancies across domains, yielding significant achievements. Nevertheless, these methods often overlook fine-grained category-level subdomain distribution alignment, leading to discriminative structure confusion and subdomain misalignment, which hinder cross-subject and cross-session gesture recognition. To tackle these issues, this paper proposes a plug-and-play subdomain adaptation method called PPSDA to enhance the classification performance and generalization ability for gesture recognition across domains. Specifically, handcrafted features are extracted and utilized for source domain supervised training to preserve discriminative structures. Subsequently, source and target domains co-training is performed, wherein the local Maximum Mean Discrepancy (LMMD) is minimized to capture fine-grained information on relevant subdomains for subdomain distribution alignment. To validate the performance of the proposed PPSDA, we recruited 12 healthy subjects and developed an sEMG-driven exoskeleton rehabilitation glove for cross-subject and cross-session evaluations. Extensive experimental results demonstrate the effectiveness and superiority of the proposed PPSDA over existing domain adaptation approaches.

To address the limitations of existing wearable gait recognition, such as drift in static actions and difficulty in recognizing transition states, this paper proposed a gait recognition system based on the data fusion of MEMS Inertial Measurement Units (IMUs) and flexible plantar pressure sensors. A low-power wearable device comprising four inertial and two pressure sensing nodes was developed to achieve synchronized multi-source data collection. Regarding the algorithm, a sensor-characteristic-based two-stage hierarchical framework was constructed. The first stage utilized plantar pressure features to efficiently decouple static postures from dynamic gaits. The second stage employed a lightweight Support Vector Machine combined with a Finite State Machine for static and transitional actions, while an ensemble learning model based on Soft Voting was used for complex dynamic gaits. Experimental results under Leave-One-Out Cross-Validation demonstrate a comprehensive recognition accuracy of 96.17%, with 100% accuracy for standing and 97% for sit-to-stand transitions. These findings validate the significant advantages of the multi-modal fusion approach in enhancing the robustness and generalization capabilities of gait recognition.