Abstract: A front-end system collects diagnostically relevant information or data, including pulse waveform information, and optionally timing information, symptom information, and/or diagnostic information. One or more sensors or transducers detect pulse waveforms in a consistent manner, across various subjects and practitioners. A back-end system stores diagnostically relevant information and diagnostic information, typically for a large population of subjects and practitioners. The back-end system generates system generated diagnoses and/or suggested remedial actions based on submitted inquiries or requests. The back-end system can advantageously employ machine learning techniques to identify correlations between defining characteristics or features of pulse waveforms and diagnoses, over a large number of samples submitted by a substantial number of practitioners, for instance of traditional Chinese medicine. The back-end system can also employ primary and/or secondary symptoms in identifying correlations.

Abstract: A front-end system collects diagnostically relevant information or data, including pulse waveform information, and optionally timing information, symptom information, and/or diagnostic information. One or more sensors or transducers detect pulse waveforms in a consistent manner, across various subjects and practitioners. A back-end system stores diagnostically relevant information and diagnostic information, typically for a large population of subjects and practitioners. The back-end system generates system generated diagnoses and/or suggested remedial actions based on submitted inquiries or requests. The back-end system can advantageously employ machine learning techniques to identify correlations between defining characteristics or features of pulse waveforms and diagnoses, over a large number of samples submitted by a substantial number of practitioners, for instance of traditional Chinese medicine. The back-end system can also employ primary and/or secondary symptoms in identifying correlations.

Abstract: A process is described for diagnosing mammalian patients, including human patients, based on the spatial and temporal profile of the radial arterial pulse. Pulse patterns are measured, and the patterns and matching diagnoses added to an analytic module including a database system.