Artificial Intelligence-enabled contactless sensing for medical diagnosis

Artificial Intelligence (AI) is an emerging technology which aims to make intelligent machines, especially intelligent computer programs. It can be utilized to enable human intelligence onmachines, but the ability of AI does not have to confine itself to biologically observable methods. It can identify hidden relationships, correlations, and trends that may not be apparent in traditional viewpoints. As a result, AI-enabled technologies have achieved significant achievements for medical care including diagnosis, treatment, drug discovery, and healthcare management [1]. Continuousmonitoring of patients plays a crucial role for medical diagnosis and treatment. Nonetheless, existing monitoring techniques have inherent limitations. Numerous methodsnecessitate theuse ofuncomfortable or cumbersome devices, with certain devices even being invasive. Consequently, patients have to face restrictions on their daily activities, and the duration ofmonitoring is curtailed.Moreover, some monitoring methods are cost-prohibitive, demanding specialized equipment and trained personnel for operation. To resolve this challenge, radio frequency (RF) based wireless monitoring schemes which enables contactless, noninvasive and continuous monitoring have been proposed [2]. Specifically, human activities would modulate the propagation of RF signals, which makes it possible to extract human information from the variation of RF signals. Benefiting from AI’s ability to identify hidden relationship between signal variation and human activities, contactless self-medication management, Parkinson detection, sleep monitoring and cardiac activity have been achieved the past decade. In the realm of chronic disease management, selfmedication is a prevalent practice. However, due to challenges introduced by inadequate professional guidance and medication adherence, patients often encounter issues related to medication self-administration. Errors in selfmedication can impose additional burdens on both patients and healthcare institutions. To address this, in 2021, Zhao proposed an innovative AI system that analyzes wireless signals within patients’ homes to identify errors in selfmedication [3]. Utilizing the reflection of wireless signals from the human body, the system can extract the signal corresponding to the activity of the patient. By applying AI techniques to analyze the signals received by sensors, the system can effectively track specific movements associated with self-medication. Consequently, this approach enables not only monitoring of medication timing but also assessment of whether patients adhere to the correct steps of using the medication device. This wireless sensing-based solution alleviates the burden on healthcare institutions and does not impose any inconvenience on patients’ daily lives. For medical treatment, in-depth knowledge of an individual’s health status and physiological functions is crucial for healthcare professionals to develop treatment plans and preventive measures. Continuous monitoring of physiological signals such as respiration and heart rate holds significant importance in individual health management, disease prevention, and treatment. Present monitoring approaches typically employ portable and wearable devices to record parameters like heart rate, respiration, and sleep quality. Wireless sensing utilizes reflected signals extracted from the chest and abdomen of the human body to monitor vital signs. This is achieved by analyzing the phase and amplitude variations caused by respiration and heartbeat. In 2021, Wang achieved precise monitoring of human vital signs using commercially available millimeter-wave devices [4], demonstrating median errors of 0.19 breaths per minute and 0.92 beats per minute for respiration and heart rate, respectively. The continuousmonitoring of physiological signals also holds critical significance in disease diagnosis. Parkinson’s disease, the world’s fastest-growing neurological disorder, had over 1 million patients in the United States as of 2020. Regrettably, there are currently nomedications capable of reversing the progression caused by the Parkinson’s disease. Consequently, early diagnosis of Parkinson’s disease represents a significant area of research. Traditionally, *Corresponding authors: Yan Chen and Manqing Wu, RCDC-Research Center for Data to Cyberspace, University of Science and Technology of China, Hefei 230026, Anhui Province, China; and Institute of Dataspace, Hefei Comprehensive National Science Center, Hefei 230026, Anhui Province, China, E-mail: eecyan@ustc.edu. cn (Y. Chen), wumanqing@ustc.edu. cn (M. Wu). https://orcid.org/0000-0002-32274562 (Y. Chen) Med. Rev. 2023; aop