us warm in cold climates and shielded us from the nakedness for modesty and civilization. [1,2] Cloth is also a platform for artists, designers, and tailors to advance the clothing demands with emerging materials, process, and fashion. [2] While the majority of the functionalities of clothes lie in their physical attributes, such as their softness, breathability, air/ vapor permeability, and, of course the appearance, their role in thermal management is equally, if not more, important. The primary goal of clothing is to satisfy human being's basic needs in thermal comfort, by providing cooling in the hot environment or heating in the cold environment. [1] The energy management aspect of clothing is becoming an increasingly important and attractive aspect due to our increasing awareness to energy consumption and our persisting pursuit of comfort and health. The finite availability and the associated environmental consequence of fossil fuels have motivated us to save energy from virtually all aspects of our daily lives. A suitable thermal envelop is a necessity for our living and working. To create a comfortable indoor environment, the building heating, ventilation, and air conditioning (HVAC) systems are widely used for space cooling and heating at the expense of excessive energy consumption. [3][4][5][6] According to United States In this decade, the demands of energy saving and diverse personal thermoregulation requirements along with the emergence of wearable electronics and smart textiles give rise to the resurgence of personal thermal management (PTM) technologies. PTM, including personal cooling, heating, insulation, and thermoregulation, are far more flexible and extensive than the traditional air/liquid cooling garments for the human body. Concomitantly, many new advanced materials and strategies have emerged in this decade, promoting the thermoregulation performance and the wearing comfort of PTM simultaneously. In this review, an overview is presented of the state-ofthe-art and the prospects in this burgeoning field. The emerging materials and strategies of PTM are introduced, and classed by their thermal functions. The concept of infrared-transparent visible-opaque fabric (ITVOF) is first highlighted, as it triggers the work on advanced PTM by combining it with radiative cooling, and the corresponding implementations and realizations are subsequently introduced, followed by wearable heaters, flexible thermoelectric devices, and sweat-management Janus textiles. Finally, critical considerations on the challenges and opportunities of PTM are presented and future directions are identified, including thermally conductive polymers and fibers, physiological/psychological statistical analysis, and smart PTM strategies.

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Soft actuators with accurate and real-time motion perception are of great importance for flexible machines and artificial intelligence robotics to enable an autonomic response to surroundings. To enhance the sensing-signal reliability and calibration, synchronous motion perception with multiplex feedback signals is desired but has not been sufficiently explored. Herein, we present a soft bimorph actuator that has electrical and visual dual channel signal feedback functions for real-time multiplex motion perception. Cellulose paper and polyimide tape were assembled together as bimorph actuation layers on which an MXene/graphene bilayer was coated for electrothermal function and electrical signal feedback and a thermochromic interlayer was used for real-time visual signal feedback. Based on the proposed actuators, three kinds of bionic robotics and an electro-puppetry robot, "Wu Song Fights the Tiger", with motion-programmable

Thermal camouflage, which is used to conceal objects in the infrared vision for confrontation with infrared detection in civilian or military applications, has garnered increasing attraction and interest recently. Compared with conductive thermal camouflage, that is to tune heat conduction to achieve equivalent temperature fields, radiative thermal camouflage, based on emissivity engineering, is more promising and shows much superiority in the pursuit of dynamic camouflage technology when resorting to stimuli-responsive materials. In this paper, we demonstrate the emissivity-engineered radiative metasurface to realize dynamic thermal camouflage functionality via a flying laser heat source on the metal-liquid-crystal-metal (MLCM) platform. We employ a rigorous coupled-wave algorithm to calculate the surface emissivity of Au/LC/Au microstructures, where the LC-orientation angle distribution is quantified by minimizing the emitted thermal energy standard deviation throughout the whole plate. Emissivity engineering on the MCLM platform is attributed to multiple magnetic polariton resonance, and agrees well with the equivalent electric circuit analysis. Through this electrical modulation strategy, the moving hot spot in the original temperature field is erased and a uniform temperature field is observed in the infrared camera instead, demonstrating the very good dynamic thermal camouflage functionality. The present MLCM-based radiative metasurface may open avenues for high-resolution emissivity engineering to realize novel thermal functionality and develop new applications for thermal metamaterials and meta-devices.

To solve the complexity of the traditional motion intention recognition method using a multi-mode sensor signal and the lag of the recognition process, in this paper, an inertial sensor-based motion intention recognition method for a soft exoskeleton is proposed. Compared with traditional motion recognition, in addition to the classic five kinds of terrain, the recognition of transformed terrain is also added. In the mode acquisition, the sensors’ data in the thigh and calf in different motion modes are collected. After a series of data preprocessing, such as data filtering and normalization, the sliding window is used to enhance the data, so that each frame of inertial measurement unit (IMU) data keeps the last half of the previous frame’s historical information. Finally, we designed a deep convolution neural network which can learn to extract discriminant features from temporal gait period to classify different terrain. The experimental results show that the proposed method can recognize the pose of the soft exoskeleton in different terrain, including walking on flat ground, going up and downstairs, and up and down slopes. The recognition accuracy rate can reach 97.64%. In addition, the recognition delay of the conversion pattern, which is converted between the five modes, only accounts for 23.97% of a gait cycle. Finally, the oxygen consumption was measured by the wearable metabolic system (COSMED K5, The Metabolic Company, Rome, Italy), and compared with that without an identification method; the net metabolism was reduced by 5.79%. The method in this paper can greatly improve the control performance of the flexible lower extremity exoskeleton system and realize the natural and seamless state switching of the exoskeleton between multiple motion modes according to the human motion intention.