This software provides the processing for a non-contact system that remotely estimates the heart rate and respiration rate of individuals as they carry on daily activities, and also enables detection of heart and respiration rate through walls.
Prior attempts have been made using microwave Doppler radar techniques to estimate heart rate and respiration remotely. However, such techniques lacked advanced signal processing to effectively “deconvolve” the heart rate and respiration signals from human body motions from the Doppler signal reflections. Therefore, competing approaches have only been successful in estimating heart rate or respiration when the individual is completely stationary.
This software remotely estimates human vital signs, such as heart rate and respiration rate, from microwave reflections from humans using high-frequency, narrow-band microwave (18 to 30 GHz). The signal processing algorithms implemented in this system allow estimation of heart rate and respiration rate from microwave reflection from the torso of an individual. It uses novel signal processing and advanced machine learning techniques to detect unique heart signatures, even in the presence of natural body motions such as moderate breathing or fidgeting.
This technique can be used to monitor the vital signs of astronauts on the International Space Station, during long space missions to the Moon or Mars, and during spacewalks, when using contact electrocardiogram and respiration belts is cumbersome and interferes with the activities of astronauts.
This work was done by Ashit Talukder and Steven P. Monacos of Caltech for NASA’s Jet Propulsion Laboratory.
This software is available for commercial licensing. Please contact Dan Broderick at
This Brief includes a Technical Support Package (TSP).
Signal Processing Software for Remote Vital Sign Monitoring
(reference NPO-47243) is currently available for download from the TSP library.
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Overview
The document outlines advancements in multimodal data fusion and reasoning systems developed by NASA's Jet Propulsion Laboratory (JPL), particularly focusing on their applications in threat assessment and remote vital sign monitoring. It emphasizes the importance of integrating diverse data sources to enhance situational awareness, improve decision-making, and reduce analyst workload.
At the core of the document is the Multimodal Front-End Reasoning System, which provides a uniform interface to various data sources. This system preprocesses data through programmable middleware and distributes it to reasoning elements via a common database. The benefits of this approach include the elimination of redundant tasks, the capture of corporate knowledge, improved safety and reliability, and cost reduction through the reuse of established interfaces and analysis functions. The system is designed to be adaptable to a wide range of applications, including both traditional and autonomous flight systems, as well as complex data analysis.
The document also details the Intelligent Multimodal Data Fusion Architecture, which consists of several key processes: observing, understanding, learning, predicting, and deciding. This architecture allows for the analysis of arbitrary data sources, leading to the generation of integrated analysis results, identification of significant events, and predictions of future occurrences based on past observations. The feedback process is crucial for refining active state models and enhancing overall system performance.
Data fusion capabilities are highlighted as essential for providing situational awareness and actionable intelligence. The system reduces the workload on analysts by identifying critical information, abstracting data, and summarizing findings. It employs a hierarchy of reasoning that includes detection of situations, contextual analysis, and the construction of higher-order data products.
The document concludes by emphasizing the significance of continuous situational awareness through the collection and analysis of existing and emerging data sources. The architecture supports critical information delivery and the tracking of significant individuals and their interrelations, which is vital for understanding complex scenarios.
Overall, the document presents a comprehensive overview of JPL's efforts in developing sophisticated data fusion and reasoning technologies that have broad implications for various fields, particularly in enhancing safety, reliability, and efficiency in data analysis and decision-making processes.
