During the last decades, smart tactile sensing systems based on different sensing techniques have been developed due to their high potential in industry and biomedical engineering. However, smart tactile sensing technologies and systems are still in their infancy, as many technological and system issues remain unresolved and require strong interdisciplinary efforts to address them. This paper provides an overview of smart tactile sensing systems, with a focus on signal processing technologies used to interpret the measured information from tactile sensors and/or sensors for other sensory modalities. The tactile sensing transduction and principles, fabrication and structures are also discussed with their merits and demerits. Finally, the challenges that tactile sensing technology needs to overcome are highlighted.

Electrocardiogram (ECG) electrodes are important sensors for detecting heart disease whose performance determines the validity and accuracy of the collected original ECG signals. Due to the large drawbacks (e.g., allergy, shelf life) of traditional commercial gel electrodes, textile electrodes receive widespread attention for their excellent comfortability and breathability. This work demonstrated a dry electrode for ECG monitoring fabricated by screen printing silver/silver chloride (Ag/AgCl) conductive ink on ordinary polyester fabric. The results show that the screen-printed textile electrodes have good and stable electrical and electrochemical properties and excellent ECG signal acquisition performance. Furthermore, the resistance of the screen-printed textile electrode is maintained within 0.5 Ω/cm after 5000 bending cycles or 20 washing and drying cycles, exhibiting excellent flexibility and durability. This research provides favorable support for the design and preparation of flexible and wearable electrophysiological sensing platforms.

Using the phase synchronization of EEG signals, two different phases, PLI and PLV, were used to construct brain network analysis and graph convolutional neural network, respectively, to achieve automatic identification of Alzheimer’s disease (AD) and to assist in the early diagnosis of Alzheimer’s disease. In this paper, we selected outpatients (16 AD subjects, 20 mild cognitive impairment (MCI) subjects and 21 healthy control (HC) subjects) from the outpatient clinic of Yangpu Mental Health Center in Shanghai, China, from January 2023 to December 2023, and collected resting-state EEG data. To collect resting-state EEG data, each patient was asked to sit down with eyes closed for 5 min. Firstly, the acquired EEG data were preprocessed to extract the data in the α-band at 8–13 Hz; secondly, the phase lag index (PLI) and phase-locked value (PLV) were used to construct the brain functional network, and the brain functional connectivity map was visualized by brain functional connectivity analysis. Finally, the constructed PLI and PLV were input into the graph convolutional neural network (GCN) model as node features for training and classification, respectively. Healthy controls had relatively strong mean brain functional connectivity in the PLV brain network compared to AD and MCI patients. MCI patients showed lower mean brain functional connectivity in the brain network of PLI, while all three groups showed significant differences in brain functional connectivity between parietal and occipital lobes. The GCN model improved classification accuracy by more than 10% compared to using a machine learning classifier. When PLV was used as the nodal feature in the GCN model, the model achieved an average classification accuracy of 77.80% for the three groups of AD, MCI and HC, which was an improvement over the accuracy of choosing raw EEG data and PLI as the nodal feature. The performance of the model was further validated. The experimental results show that the GCN model can effectively identify the graph structure compared with the traditional machine learning model, the GCN-PLV model can better classify AD patients, and the alpha band is proved to be more suitable for AD resting-state EEG by tenfold cross-validation. The brain network map constructed based on PLI and PLV can further capture the local features of EEG signals and the intrinsic functional relationships between brain regions, and the combination of these two models has certain reference value for the diagnosis of AD patients.

The dynamic detection of human motion is important, which is widely applied in the fields of motion state capture and rehabilitation engineering. In this study, based on multimodal information of surface electromyography (sEMG) signals of upper limb and triaxial acceleration and plantar pressure signals of lower limb, the effective virtual driving control and gait recognition methods were proposed. The effective way of wearable human posture detection was also constructed. Firstly, the moving average window and threshold comparison were used to segment the sEMG signals of the upper limb. The standard deviation and singular values of wavelet coefficients were extracted as the features. After the training and classification by optimized support vector machine (SVM) algorithm, the real-time detection and analysis of three virtual driving actions were performed. The average identification accuracy was 90.90%. Secondly, the mean, standard deviation, variance, and wavelet energy spectrum of triaxial acceleration were extracted, and these parameters were combined with plantar pressure as the gait features. The optimized SVM was selected for the gait identification, and the average accuracy was 90.48%. The experimental results showed that, through different combinations of wearable sensors on the upper and lower limbs, the motion posture information could be dynamically detected, which could be used in the design of virtual rehabilitation system and walking auxiliary system.