In the present era of the Internet of Things, wearable sensors have been receiving considerable attention owing to their great potential in a plethora of applications. Highly sensitive chemical type wearable sensors that can conformably adhere to the epidermis or textiles for monitoring personal microenvironment have gained incredible interest. Attributable to the large surface area and excellent mechanical, chemical, physical, thermal as well as biocompatible properties, nanomaterials have become a prominent building block to develop wearable sensors. In this review, recent progress in the development of nanomaterial enabled wearable chemical environmental sensors (WCESs) is presented by focusing on the chemistry‐based transduction principles. The developments in sensor structures, selection of materials, and fabrication methods are highlighted. The recent WCESs are summarized by grouping in three major types according to their transduction principles: electrical, photochemical, and electrochemical. In addition, sensors with multimodal sensing capability as well as sensors immobilized in wireless tags are summarized. Finally, issues, challenges, and future perspectives are discussed to develop next‐generation WCESs with long life, biocompatibility, self‐healing, and real‐time communication capabilities.

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This paper presents a new methodology for detecting incipient slip for intelligent grasping. A new inductive sensor was designed and developed to achieve this task. A full description of the structure of the new sensor is included in this paper.The three elements required for a successful robotic grasping were examined using the newly designed sensor. These elements are incipient slip, grip force and contact area. The new design of the incipient slip detector is based on the summation of changes in mutual distance in all three primary inductors of a sensor element. This can be used to determine local or micro slip for a secure and optimal grasping. In addition this sensor can be also used to detect the applied force and determine the contact area between the object and the gripping device.

Nowadays, location awareness becomes the key to numerous Internet of Things (IoT) applications. Among the various methods for indoor localisation, received signal strength indicator (RSSI)-based fingerprinting attracts massive attention. However, the RSSI fingerprinting method is susceptible to lower accuracies because of the disturbance triggered by various factors from the indoors that influence the link quality of radio signals. Localisation using body-mounted wearable devices introduces an additional source of error when calculating the RSSI, leading to the deterioration of localisation performance. The broad aim of this study is to mitigate the user’s body shadowing effect on RSSI to improve localisation accuracy. Firstly, this study examines the effect of the user’s body on RSSI. Then, an angle estimation method is proposed by leveraging the concept of landmark. For precise identification of landmarks, an inertial measurement unit (IMU)-aided decision tree-based motion mode classifier is implemented. After that, a compensation model is proposed to correct the RSSI. Finally, the unknown location is estimated using the nearest neighbour method. Results demonstrated that the proposed system can significantly improve the localisation accuracy, where a median localisation accuracy of 1.46 m is achieved after compensating the body effect, which is 2.68 m before the compensation using the classical K-nearest neighbour method. Moreover, the proposed system noticeably outperformed others when comparing its performance with two other related works. The median accuracy is further improved to 0.74 m by applying a proposed weighted K-nearest neighbour algorithm.

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