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The rapid development of electronic material and sensing technology has enabled research to be conducted on liquid metal-based soft sensors. The application of soft sensors is widespread and has many applications in soft robotics, smart prosthetics, and human-machine interfaces, where these sensors can be integrated for precise and sensitive monitoring. Soft sensors can be easily integrated for soft robotic applications, where traditional sensors are incompatible with robotic applications as these types of sensors show large deformation and very flexible. These liquid-metal-based sensors have been widely used for biomedical, agricultural and underwater applications. In this research, we have designed and fabricated a novel soft sensor that yields microfluidic channel arrays embedded with liquid metal Galinstan alloy. First of all, the article presents different fabrication steps such as 3D modeling, printing, and liquid metal injection. Different sensing performances such as stretchability, linearity, and durability results are measured and characterized. The fabricated soft sensor demonstrated excellent stability and reliability and exhibited promising sensitivity with respect to different pressures and conditions. 

Biomimicr y is a i eld of study that involves imitating the designs and processes of nature to solve problems using man- made systems. Biomimicr y offer s an empathetic, interconnected under standing of how life wor ks and where we i t in. In bio- inspired designs, the main challenge is to develop a sustainable and effective fr amewor k that can be used in the real wor ld. M ost of the soft robotic designs are state-of-the-ar t models that cannot be used in real sensing applications. In this research, we propose a sustainable sensor-integr ated modular soft robot model that can be used for locomotion and gr ipping applications. This wor k presents the steps involved in modeling, designing, and fabr icating soft and l exible end effector s that can be used for soft robots with integr ated soft stretchable sensor s. We demonstr ate the design methodology involved in modeling and simulating the proposed model for robots that would require effector s with increased functionalities. 

Wearable devices are ubiquitous and Internet of Things (IoT) devices have made it possible by connecting real-time devices to virtual cloud. There are also a tremendous number of IoT-enabled consumer products for various healthcare applications. Mostly, IoT devices are used for health monitoring systems, though other business and service communities are customizing the IoT technology for greater opportunity and long-term benefit. Wearable health devices have been used for better health monitoring and exchanging more data with the physician to get the guidance of treatment or earlier diagnostic. Health monitoring in athletes is one of the multifaceted applications of wearable IoT devices whereas these devices collect and store data on their performance and progression. This technology can protect athletes by detecting any adverse health problem that occurs during the training period or at the time of the game. In this paper, we investigate the real-time monitoring of physiological parameters of the athlete during game time and performance analysis from the stored data. Continuous health monitoring during game time and off-days will reduce sports-related risks,stress and injuries of an athlete even sometimes it can save them from life-risk fatal accidents. This research integrates an IoT-based framework to develop a stress index for athletes that can be used as an indicator for monitoring athlete’s health. The proposed framework helps in monitoring the variability of the sensor information for the long-term analysis.