INTERNATIONAL COLLABORATIONS LEAD TO ADVANCES IN RADAR TECHNOLOGY FOR INDOOR HUMAN MONITORING
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ecent advances in machine learning and the Internet of Things have accelerated expectations and performance requirements of ubiquitous sensing technologies. Insights provided by the continuous monitoring of classified medical data and daily lifestyle activities greatly benefit health care professionals. Data obtained through fitness and cognitive activities also are strong indicators of overall well-being and health. Over the past decade, radio frequency active sensing, i.e., radar, has gained increased attention due to its demonstrated efficacy in disparate applications from automotive and smart homes to computer-human interaction and remote health monitoring. Radar systems are both low cost and low power, making them a safe sensing alternative that can operate in darkness and all weather conditions. Moreover, radar is non-invasive, and when used for monitoring, does not require an alteration in daily habits or routines. These attributes have made radar popular in motion monitoring. In over 800 publications and more than 17,800 citations (h-index 66), Moeness Amin, PhD, director of Villanova’s Center for Advanced Communications, has contributed to advances in radar, sonar, communications, satellite navigations, ultrasound imaging, radio telescopes and radio frequency identification. His research on radar monitoring of human daily activities began seven years ago.
RADAR FOR DETECTION OF ABNORMAL GAIT Clinical gait analysis plays a central role in diverse applications, such as medical diagnosis, rehabilitation and sports. Many neurodegenerative, musculoskeletal and cardiovascular diseases have been shown to alter a person’s gait. In particular, many pathological disorders lead to differences between the left and right leg motion, which is referred to as gait asymmetry. Timely detection of gait asymmetry enables early diagnosis and thus can help to ensure proper treatment. Dr. Amin’s research on abnormal
gait detection is supported by Germany’s Humboldt Research Award, which he received in 2016 in recognition of his lifetime achievements in signal processing.
RADAR FOR SMART HOMES AND MAN-MACHINE INTERFACE In collaboration with Comcast, Dr. Amin is looking at the ability of radar-based sensors to precisely detect motion, as well as describe unique characteristics of a motion activity, which provides high-fidelity sensor data to the home security/automation system. These data can be used to detect change of habits and routines and indicate significant changes in home activities, like time spent sitting versus time spent moving. Significant changes in mobility can be a precursor to depression or physical health issues. Dr. Amin’s radar algorithm also recognizes Dr. Moeness Amin, PhD student hand gestures, which can control Ann-Kathrin Seifert and Dr. Abdelhak home appliances, such as desk Zoubir from TU-Darmstadt, Germany, co-authored “Detection of gait lamps and televisions.
RADAR FOR FALL DETECTION
asymmetry using indoor doppler radar,” which won the best paper award at the 2019 IEEE Radar Conference.
The elderly will represent approximately 20% of the world population by 2030, reaching 1.4 billion worldwide. Demographic trends indicate that an overwhelming majority of these older adults will choose to receive care in the home, and Dr. Amin’s fall detection technology is an important innovation for helping seniors live more independently. Prompt fall detection saves lives, leads to timely intervention and the most effective treatment, and reduces both private and public medical expenses. Fall detection systems also reduce the burden on families that care for senior family members remotely. Dr. Amin’s fall detection research is supported by the Qatar National Research Fund.
