LAUSR

Digitized telemedicine tools with the Internet of Things (IoT) started advancing into our daily lives and have been incorporated with commercial wearable gadgets for noninvasive remote health monitoring. The newly established tools have been steered toward a new era of decentralized healthcare. The advancement of a telemedicine wearable monitoring system has attracted enormous interest in the multimodal big data acquisition of real-time physiological and biochemical information via noninvasive methods for any health-related industries. The expectation of telemedicine wearable creation has been focused on early diagnosis of multiple diseases and minimizing the cost of high-tech and invasive treatments. However, only limited progress has been directed toward the development of telemedicine wearable sensors. This Perspective addresses the advancement of these wearable sensors that encounter multiple challenges on the forefront and technological gaps hampering the realization of health monitoring at molecular levels related to smart materials mostly limited to single use, issues of selectivity to analytes, low sensitivity to targets, miniaturization, and lack of artificial intelligence to perform multiple tasks and secure big data transfer. Sensor stability with minimized signal drift, on-body sensor reusability, and long-term continuous health monitoring provides key analytical challenges. This Perspective also focuses on, promotes, and highlights wearable sensors with a distinct capability to interconnect with telemedicine healthcare for physical sensing and multiplex sensing at deeper levels. Moreover, it points out some critical challenges in different material aspects and promotes what it will take to advance the current state-of-art wearable sensors for telemedicine healthcare. Ultimately, this Perspective is to draw attention to some potential blind spots of wearable technology development and to inspire further development of this integrated technology in mitigating multimorbidity in aging societies through health monitoring at molecular levels to identify signs of diseases.