Flexible tactile sensors have broad applications in human physiological monitoring, robotic operation and human–machine interaction. However, the research of wearable and flexible tactile sensors with high sensitivity, wide sensing range and ability to detect three-dimensional (3D) force is still very challenging. Herein, a flexible tactile electronic skin sensor based on carbon nanotubes (CNTs)/polydimethylsiloxane (PDMS) nanocomposites is presented for 3D contact force detection. The 3D forces were acquired from combination of four specially designed cells in a sensing element. Contributed from the double-sided rough porous structure and specific surface morphology of nanocomposites, the piezoresistive sensor possesses high sensitivity of 12.1 kPa⁻¹ within the range of 600 Pa and 0.68 kPa⁻¹ in the regime exceeding 1 kPa for normal pressure, as well as 59.9 N⁻¹ in the scope of < 0.05 N and > 2.3 N⁻¹ in the region of < 0.6 N for tangential force with ultra-low response time of 3.1 ms. In addition, multi-functional detection in human body monitoring was employed with single sensing cell and the sensor array was integrated into a robotic arm for objects grasping control, indicating the capacities in intelligent robot applications.

Pressure sensors with high sensitivity, a wide linear range, and a quick response time are critical for building an intelligent disease diagnosis system that directly detects and recognizes pulse signals for medical and health applications. However, conventional pressure sensors have limited sensitivity and nonideal response ranges. We proposed a multichannel flexible pulse perception array based on polyimide/multiwalled carbon nanotube-polydimethylsiloxane nanocomposite/polyimide (PI/MPN/PI) sandwich-structure pressure sensor that can be applied for remote disease diagnosis. Furthermore, we established a mechanical model at the molecular level and guided the preparation of MPN. At the structural level, we achieved high sensitivity (35.02 kPa^-1) and a broad response range (0-18 kPa) based on a pyramid-like bilayer microstructure with different upper and lower surfaces. A 27-channel (3 × 9) high-density sensor array was integrated at the device level, which can extract the spatial and temporal distribution information on a pulse. Furthermore, two intelligent algorithms were developed for extracting six-dimensional pulse information and automatic pulse recognition (the recognition rate reaches 97.8%). The results indicate that intelligent disease diagnosis systems have great potential applications in wearable healthcare devices.

Flexible tactile sensors play important roles in many areas, like human-machine interface, robotic manipulation, and biomedicine. However, their flexible form factor poses challenges in their integration with wafer-based devices, commercial chips, or circuit boards. Here, we introduce manufacturing approaches, device designs, integration strategies, and biomedical applications of a set of flexible, modular tactile sensors, which overcome the above challenges and achieve cooperation with commercial electronics. The sensors exploit lithographically defined thin wires of metal or alloy as the sensing elements. Arranging these elements across three-dimensional space enables accurate, hysteresis-free, and decoupled measurements of temperature, normal force, and shear force. Assembly of such sensors on flexible printed circuit boards together with commercial electronics forms various flexible electronic systems with capabilities in wireless measurements at the skin interface, continuous monitoring of biomechanical signals, and spatial mapping of tactile information. The flexible, modular tactile sensors expand the portfolio of functional components in both microelectronics and macroelectronics.

Flexible tactile sensors have become one of the most attractive research areas because of its great application prospect in electronic skin, healthcare monitoring and artificial intelligence. However, there are still great challenges in mimicking the human skin’s strong ability of tactile perception to the changes of external physical information. Nowadays, in addition to flexible polymers and conductive materials, the microstructure of sensing units is a key factor to improve the performance of tactile sensors such as sensitivity, fast response and robustness. In recent years, a variety of novel microstructure designs have been applied to applications of tactile information perception and numerous corresponding processing methods have also been developed, which provides a wealth of enlightening solutions for the production of electronic skins. Herein, a large number of research results of tactile sensor devices are systematically reviewed according to different three-dimensional geometric shapes of the microstructures which are grouped into several categories. At the same time, relevant processing technologies employed, working principles and chief performance characteristics are also briefly described. Relative merits of different types of microstructure applications are clearly elaborated through the classification and comparison of multiple microstructure geometries in this paper to provide valuable reference for sensing microstructure design and fabrication of tactile sensors in corresponding engineering applications. Moreover, current difficulties and challenges in fabrication of flexible microstructures and future investigative directions pertaining to this field have been discussed.

The cuff-less blood pressure (BP) monitoring method based on photoplethysmo- gram (PPG) makes it possible for long-term BP monitoring to prevent and treat cardiovascular and cerebrovascular events. In this paper, a portable BP prediction system based on feature combination and artificial neural network (ANN) is implemented. The robustness of the model is improved from three aspects. Firstly, an adaptive peak extraction algorithm was used to improve the accuracy of peaks and troughs detection. Secondly, multi-dimensional features were extracted and fused, including three groups of PPG-based features and one group of demographics-based features. Finally, a two-layer feedforward artificial neural networks algorithm was used for regression. Thirty-three subjects distributed in the three BP groups were recruited. The proposed method passed the European Society of Hypertension International Protocol revision 2010 (ESP-IP2). Experimental results show that the proposed method exhibits good accuracy for a diverse population with an estimation error of −0.07 ± 4.47 mmHg for SBP and 0.00 ± 3.61 mmHg for DBP. Moreover, the model tracked the BP of two subjects for half a month, laying the foundation work for daily BP monitoring. This work will contribute to the long-term wellness management and rehabilitation process, enabling timely detection and improvement of the user's physical health.