Sensor drift is one of the severe issues that gas sensors suffer from. To alleviate the sensor drift problem, a gas sensor drift compensation approach is proposed based on local discriminant subspace projection (LDSP). The proposed approach aims to find a subspace to reduce the distribution difference between two domains, i.e., the source and target domain. Similar to domain regularized component analysis (DRCA) which is a recently proposed sensor drift correction method, the mean distribution discrepancy is minimized in the common subspace in our approach. LDSP extends DRCA in two aspects, i.e., it not only takes the label information of the source data into consideration to reduce the possibility of the case that samples in the subspace with different class labels stay close to each other, but also borrows the idea of locality-preserving projection to deal with multimodal data. Specifically, inspired by local Fisher discriminant analysis (LFDA), the label information is utilized to maximize the local between-class variance of source data in the latent common subspace and simultaneously minimize the local within-class variance. The formulation of LDSP is a generalized eigenvalue problem that can be readily solved. The experimental results have shown the proposed method outperforms other gas sensor drift compensation methods in terms of classification accuracy on two public gas sensor drift datasets.

Multisource biosignals are being increasingly used in human–machine interaction applications. In particular, a critical problem in exoskeleton-assisted rehabilitation for patients with hemiplegia is the generation of rhythmic and symmetrical locomotion. To support lower limb rehabilitation, we propose a model for predicting contralateral joint angles using multisource biosignals. First, a vibroarthrography (VAG) sensor is attached to the affected leg, and surface electromyography sensors are attached to the sound leg. The corresponding signals are used to estimate the hip, knee, and ankle joint angles of the affected leg. Second, an algorithm based on a temporal convolution network (TCN) is introduced to predict the contralateral lower-limb joint angles during human locomotion. The TCN is compared with a long short-term memory (LSTM) network and a convolutional neural network. Experiments were conducted by 10 healthy participants. The results of the proposed model were compared with measurements from encoders in three joints mounted on an exoskeleton to verify the applicability of the proposed model. In addition, by using the outputs of a motion capture system as the ground truth, the experimental results validated the model prediction performance. The prediction root mean square error (RMSE) of the TCN was 52%–70% lower than that of the LSTM network and CNN at different paces. The prediction RMSE using VAG was 20%–24% lower than that without using VAG. Note to Practitioners—We propose a model for predicting the contralateral lower-limb joint angles of an exoskeleton. The model uses measurements from multiple biosignal sensors to support exoskeleton-assisted hemiplegia rehabilitation. A sensing system based on acoustic signals and bioelectric signals is developed to improve the joint angle prediction in the affected leg of patients with hemiplegia. A temporal convolution network is implemented to estimate joint angles, and training considering the root mean square error and Pearson correlation coefficient as evaluation indicators is performed. Unlike similar methods that include no sensory feedback from the affected leg, the proposed method incorporates VAG signals from the affected leg and sEMG signals from the sound leg to handle different degrees of hemiplegia and support the generation of rhythmic and symmetrical locomotion for gait rehabilitation using an exoskeleton.

In the lower limb exoskeleton assistance system, motion intention understanding based on biological signals is the human-computer interface’s (HMI) key technology. Due to the close coupling of human and machine, wheelchair-type control modes (combination of motion intentions and switches of different actions) are not applicable. For example, the right/left-hand motor imagery (MI) controls the exoskeleton to move forward/stop. There is a mismatch between MI-based control instructions and actual actions because the forward process alternates between left and right. Therefore, this study aims to design a reasonable HMI for a lower limb exoskeleton assistance system and to prove its rationality and effectiveness. We have designed a new HMI combining the human walking process and multimodal biosignals. Experimental data were collected from 14 subjects. We use feature-based and end-to-end analysis methods to analyze multimode signals. In a feature-based analysis, the advantage of using sEMG to distinguish between different movements does not apply to distinguishing different magnitudes of the same movement. A time-frequency subband selection method based on difference patterns is designed for EEG signals in the feature-based analysis. The fusion of EEG and sEMG is implemented to complement each other. The average recognition rate of the multimodal HMI was evaluated to be up to 89.5%, which is 12% and 7.8% higher than that of EEG and sEMG, respectively. Based on end-to-end analysis, a time-domain translation strategy was used to expand the sample and obtain a cross-subject identification rate of 83.6%. All the results indicate that the designed HMI is rational and effective, and there exists potential for practical application.

Grasp stability recognition based on tactile perception has attracted increasing attention in the robotics community. In this article, we extract tactile features from multimodal tactile signals and propose a novel ensemble approach named genetic algorithm-based ensemble hybrid sparse extreme learning machine (GA-EHSELM) for the grasp stability recognition task. In contrast to traditional ensemble extreme learning machines (ELMs), the proposed approach takes the sparsity of the random weights assigned to the connection between the input layer and the hidden layer for each base learner into consideration, in order to avoid the problem of overfitting. In addition, The diversity of base learners is increased by constructing two types of sparse ELM (SELM). The random weights of the base SELMs are sampled from two different distributions with a certain probability. Furthermore, we utilize the genetic algorithm (GA) for the optimization of tactile features and the base sparse ELMs. To be specific, GA is employed for feature selection as well as the optimization of the sparsity of the random weights associated with different types of base learners and the number of each type of base learner. The effectiveness of the proposed approach has been demonstrated on a public tactile grasp stability dataset.

Sensor drift is a well-known issue in the field of sensors and measurement. It has plagued the sensor community for many years. In this paper, we propose a sensor drift correction method to deal with the sensor drift problem. In contrast to domain regularized component analysis, the proposed method makes use of the class label of data in the source domain. Specifically, we propose a discriminative subspace projection approach for sensor drift reduction in electronic noses. The proposed method has multiple properties. (1) It inherits the merits of the subspace projection approach called domain regularized component analysis via introducing a regularization parameter to tackle the sample size imbalance problem. (2) The proposed method takes the source data label information into consideration, which minimizes the within-class variance of the projected source samples and at the same time maximizes the between-class variance. The label information is exploited to avoid overlapping of samples with different labels in the subspace. An efficient method based on generalized eigenvalue decomposition is employed to solve the optimization problem. Experiments on two sensor drift datasets have shown the effectiveness of the proposed approach.

With the advances of robot technology in the medical field, robotic palpation has been a research hot topic and attracted wide attention. Tumor recognition is considered as a significant work in robotic palpation. Especially, the determination of tumor depth is a key prerequisite for tumor resection in robot-assisted minimally invasive surgery (RMIS). However, the lack of tactile feedback in RMIS prevents the robot from accurately perceiving information about lesions such as tumor depth. To solve this problem, we investigate the recognition of the depths of hard inclusion in soft tissue phantoms. In this article, tactile array data acquired by autonomous robotic palpation are used to recognize different depths of hard inclusions. Aiming at the classification problem of tumor depth recognition, we perform various deep learning methods and present ordinal classification methods based on the 2-D convolutional neural network- long short-term memory (2-D-CNN-LSTM) architecture for high-dimensional spatial–temporal tactile array data. The methods exploit the ordinal information of labels by changing the conventional one-hot encoding to unimodal probability distribution encodings, including Gaussian distribution, Cauchy distribution, and Laplace distribution encoding. Finally, the experimental results prove the feasibility of the proposed methods. The average recognition accuracies of the methods can reach 97%. In particular, the recognition accuracies can be above 99% when using all the robotic fingers’ tactile data.