Contactless Predictive Health Alert System (CPHAS)

The project aims to develop an automated contactless   \*_clinical screening and risk‑prediction system_\* for use in critical care, with applications in construction, transport and aviation. Developing Ultrawide band radar (UWB) but using JintzMed ’heart‑rate variability monitor and additional physiological, demographic, and lifestyle data. Clinical experts and data scientists will develop unique AI algorithms based on validated national clinical guidelines and tools e.g. Acutely ill adults in hospital: recognising and responding to deterioration  \[[www.nice.org.uk/guidance/cg50\](https://www.nice.org.uk/guidance/cg50)](https://www.nice.org.uk/guidance/cg50]\(https://www.nice.org.uk/guidance/cg50\)) and the National Early Warning Score 2 (NEWS2) Future applications include: _Early detection of cardiac disease_\* (especially myocardial infarction risk) _Early detection of vascular dementia risk_\* _Integration into primary care_\*, community clinics, and employer health programmes \* Long‑term population‑level risk stratification and preventive health planning \* CPHAS alert health screening for construction workers \* CPHAS CAB -UWB Radar in the cab for construction workers to prevent critical incidents before they happen Contactless, predictive ultra-wide band (UWB) radar-based patient monitoring and alert system (CPHAS ) established in Hong Kong in 2026 by JintzMed (Hong Kong) and Nine Health (United Kingdom). JintzMed has developed innovative diagnostic devices for cardiovascular risk, autonomous nervous system assessment and mental stress. An NMPA license has been granted for the heart rate variability (HRV) analyzer using a novel algorithmic model to enhance short-range remote monitoring of multiple vital signs. Nine Health was the data lead in a major European project (VPH-share) involved in silico medicine and supercomputational modelling since 2012; in 2015-2017 with Lantone(Hangzhou) an NMPA approved diagnostic AI system was developed. Successfully worked with UK National Health Service (NHS) in 2020/21 to develop a predictive AI wound-healing app. UWB monitors respiration, heart rate, heart-rate variability, body motion and potentially blood pressure and oxygen saturation,) 24/7 without connecting the patient to wires, clips or pads. Using an AI risk prediction score combines any changes in vital signs with other data to predict critical incidents like stroke, sepsis and heart attack. The system accuracy improves via machine learning. The system is primarily designed for use in healthcare. Effective and reliable contactless, predictive, continuous patient monitoring system is described by a leading Hong Kong private hospital as “the holy grail”. There is no such system currently available. CPHAS will alleviate shortages of care staff by enhancing efficiency: one clinician could monitor more patients and care can be prioritized. Predictive AI provides alerts in advance. Evidence shows that human observations are too late and do not achieve good outcomes. It is also applicable to care home and domestic settings and we are exploring this application with Hong Kong University. We have attracted great interest from, automotive, construction and other industries. Funded by the NHS AI Awards competition \[[https://fundingawards.nihr.ac.uk/award/AIAWARD01723\](https://fundingawards.nihr.ac.uk/award/AIAWARD01723)](https://fundingawards.nihr.ac.uk/award/AI\_AWARD01723]\(https://fundingawards.nihr.ac.uk/award/AI_AWARD01723\)) we were able to predict those at risk of developing hard to heal wounds, the disease pathway and other key outcomes enabling prevention. Applying this methodology in Hong Kong and China together with ultra Wide Radar (UWB)  we expect to create a prototype system over the next 9 months