Periodic Reporting for period 1 - ViSionRF (ViSionRF: Vital Signal Monitoring using Radio-Frequency Technologies – Standard IF-RI)
Período documentado: 2019-09-01 hasta 2021-08-31
The global population is ageing and the incidence of dementia or other serious conditions requiring constant care is increasing.
In-home health monitoring 24/7 could have significant impact on their well-being and quality of life.
Imagine having a nurse unobtrusively monitoring your vital signs and your body position round the clock to detect potential health issues.
ViSionRF is developing inexpensive and effective technology to solve this problem.
Relying on standard wireless technologies and advanced signal processing for rapid and accurate alerts, the system promises to enhance patient wellness and reduce the burden on healthcare systems and loved ones.
Objective
Dementia is a syndrome in which there is cognitive function deterioration and memory loss. Alzheimer's disease consists or contributes to 60–70% of dementia cases. At present, worldwide around 50 million people have dementia, but nearly 10 million new cases are added every year. The total number of people suffering from dementia is projected to reach 82 million in 2030 and 152 million by 2050 – a 3-fold increase. This puts tremendous pressure on the healthcare system and society as a whole. Equally important, dementia is overwhelming for patients' families and their carers, as they require full-time care and watch. For all these reasons it timely and imperative to develop a low-cost and efficient full-time health monitoring solution.
The goal of this research is to develop an unobtrusive system Suite (ViSionRF) that will be able to capture the vital physiological signals of users (breathing, heart rate, heart beat shape, body position), remotely by using low-power radar, Wi-Fi and RFID signal technologies. Envision a home with a single remote and unobtrusive device that acts as a stethoscope, heart monitor, irregular breath detector, and posture sensor. Such a home would have the ability to monitor your breathing, your heart (rate and pulse shape), and your position and alert your doctor when an emergency occurs. Such a home would help tremendously impaired citizens (e.g. dementia patients) and their carers.
Unlike traditional patient monitoring systems that require users to ‘wear’ devices and sensors, the proposed system does not require wearing any wearable electronic or on-body sensor. This maximizes mobility and makes the system completely transparent to the user. This is important as dementia sufferers repeatedly forget or decline to ‘wear’ their sensors. The goal will be achieved by developing a hybrid technology that merges Wi-Fi, radar and RFID responses with advanced signal processing algorithms that are further trained using powerful machine learning.
In-home health monitoring 24/7 could have significant impact on their well-being and quality of life.
Imagine having a nurse unobtrusively monitoring your vital signs and your body position round the clock to detect potential health issues.
ViSionRF is developing inexpensive and effective technology to solve this problem.
Relying on standard wireless technologies and advanced signal processing for rapid and accurate alerts, the system promises to enhance patient wellness and reduce the burden on healthcare systems and loved ones.
Objective
Dementia is a syndrome in which there is cognitive function deterioration and memory loss. Alzheimer's disease consists or contributes to 60–70% of dementia cases. At present, worldwide around 50 million people have dementia, but nearly 10 million new cases are added every year. The total number of people suffering from dementia is projected to reach 82 million in 2030 and 152 million by 2050 – a 3-fold increase. This puts tremendous pressure on the healthcare system and society as a whole. Equally important, dementia is overwhelming for patients' families and their carers, as they require full-time care and watch. For all these reasons it timely and imperative to develop a low-cost and efficient full-time health monitoring solution.
The goal of this research is to develop an unobtrusive system Suite (ViSionRF) that will be able to capture the vital physiological signals of users (breathing, heart rate, heart beat shape, body position), remotely by using low-power radar, Wi-Fi and RFID signal technologies. Envision a home with a single remote and unobtrusive device that acts as a stethoscope, heart monitor, irregular breath detector, and posture sensor. Such a home would have the ability to monitor your breathing, your heart (rate and pulse shape), and your position and alert your doctor when an emergency occurs. Such a home would help tremendously impaired citizens (e.g. dementia patients) and their carers.
Unlike traditional patient monitoring systems that require users to ‘wear’ devices and sensors, the proposed system does not require wearing any wearable electronic or on-body sensor. This maximizes mobility and makes the system completely transparent to the user. This is important as dementia sufferers repeatedly forget or decline to ‘wear’ their sensors. The goal will be achieved by developing a hybrid technology that merges Wi-Fi, radar and RFID responses with advanced signal processing algorithms that are further trained using powerful machine learning.
Phase 1. Wi-Fi system development for remote vital sign detection (Fig. 1)
Wireless signals captured by Wi-Fi routers can be used to monitor health information. The logic behind using Channel State Information (CSI) signals to detect breathing rate (BR) is based on processing the received signal.
A transmitter transmits a wireless signal which travels in the air and reaches the receiver, in what is called line-of-sight (LOS) propagation.
A non-line-of-sight (NLOS) propagation is when an obstacle exists anywhere in this straight line, the signal will be reflected, diffracted, and scattered. This is also known as multipath propagation.
The received signal from these scenarios will have different CSI data. The breathing of a person which causes the chest to move, will change the NLOS paths, and a sinusoid-like pattern in the CSI values can be the result of that.
In this work, the Nexmon tool captures the CSI to enable respiration detection. This additional frequency space and subcarriers improve accuracy.
An artificial neural network (ANN) was implemented. The ANN could define the BR of the respiration. The model was rapid and efficient with low error 4.7%. The same network can be generalized, e.g. to determine HR using a 0.75 to 2.5 Hz range. This ANN can identify abnormal characteristics, detect diseases and can be extended to issue health warnings, a step towards assisted living.
Phase 2. Radar System for remote vital sign detection (Fig. 2)
We built FMCW and CW radar prototypes. We observed that the distance between the radar and the person being monitored can affect the quality of the results due to the periodic null points problem.
To avoid this problem, an IQ demodulator was used along with an Arduino setup to collect the data. Signal quality was improved when we built the demodulator circuit using commercially available components such as mixers and couplers.
Furthermore, a non-contact low-cost radar-based system with automated DC components reduction was designed and implemented. That radar was capable of successfully detecting the breathing rate (BR) and heartbeat rate (HR) of a person.
Experimental results indicated very good accuracy. A Five Port Receiver (FPR) (alternative to a six-port receiver) was designed, simulated, fabricated, tested and integrated with the radar to further improve quality together with a novel concept. Results appear in Fig. 2.
This work has led to a patent (pending) and a possible spin-out SME: https://visionrf.com(se abrirá en una nueva ventana)
Results allowed the ER and his team to participate in 3 entrepreneurial programmes: (i) Lean Launch, (ii) Venture Builder Incubator, (iii) Kickstart Converge Challenge, and win awards.
The work attracted News Media attention worldwide:
- https://www.heraldscotland.com/news/national-news/18936625.touch-free-vital-signs-monitoring-technique-revealed-scientists/(se abrirá en una nueva ventana)
- https://www.insider.co.uk/news/scientists-seek-funding-touch-free-23147529(se abrirá en una nueva ventana)
- https://eandt.theiet.org/content/articles/2020/12/touch-free-vital-signs-monitor-enabled-with-radar-system/(se abrirá en una nueva ventana)
- https://uk.news.yahoo.com/touch-free-vital-signs-monitoring-000100531.html(se abrirá en una nueva ventana)
- https://uk.finance.yahoo.com/news/touch-free-vital-signs-monitoring-000100531.html(se abrirá en una nueva ventana)
- https://www.aol.co.uk/2020-12-10-touch-free-vital-signs-monitoring-technique-revealed-by-scientis.html(se abrirá en una nueva ventana)
Outreach activities include: Explorathon, Botanical Garden, as well as 1 book chapter and 3 conference presentations. Extended work lead to 1 book edited and 12 peer-reviewed journal articles.
Wireless signals captured by Wi-Fi routers can be used to monitor health information. The logic behind using Channel State Information (CSI) signals to detect breathing rate (BR) is based on processing the received signal.
A transmitter transmits a wireless signal which travels in the air and reaches the receiver, in what is called line-of-sight (LOS) propagation.
A non-line-of-sight (NLOS) propagation is when an obstacle exists anywhere in this straight line, the signal will be reflected, diffracted, and scattered. This is also known as multipath propagation.
The received signal from these scenarios will have different CSI data. The breathing of a person which causes the chest to move, will change the NLOS paths, and a sinusoid-like pattern in the CSI values can be the result of that.
In this work, the Nexmon tool captures the CSI to enable respiration detection. This additional frequency space and subcarriers improve accuracy.
An artificial neural network (ANN) was implemented. The ANN could define the BR of the respiration. The model was rapid and efficient with low error 4.7%. The same network can be generalized, e.g. to determine HR using a 0.75 to 2.5 Hz range. This ANN can identify abnormal characteristics, detect diseases and can be extended to issue health warnings, a step towards assisted living.
Phase 2. Radar System for remote vital sign detection (Fig. 2)
We built FMCW and CW radar prototypes. We observed that the distance between the radar and the person being monitored can affect the quality of the results due to the periodic null points problem.
To avoid this problem, an IQ demodulator was used along with an Arduino setup to collect the data. Signal quality was improved when we built the demodulator circuit using commercially available components such as mixers and couplers.
Furthermore, a non-contact low-cost radar-based system with automated DC components reduction was designed and implemented. That radar was capable of successfully detecting the breathing rate (BR) and heartbeat rate (HR) of a person.
Experimental results indicated very good accuracy. A Five Port Receiver (FPR) (alternative to a six-port receiver) was designed, simulated, fabricated, tested and integrated with the radar to further improve quality together with a novel concept. Results appear in Fig. 2.
This work has led to a patent (pending) and a possible spin-out SME: https://visionrf.com(se abrirá en una nueva ventana)
Results allowed the ER and his team to participate in 3 entrepreneurial programmes: (i) Lean Launch, (ii) Venture Builder Incubator, (iii) Kickstart Converge Challenge, and win awards.
The work attracted News Media attention worldwide:
- https://www.heraldscotland.com/news/national-news/18936625.touch-free-vital-signs-monitoring-technique-revealed-scientists/(se abrirá en una nueva ventana)
- https://www.insider.co.uk/news/scientists-seek-funding-touch-free-23147529(se abrirá en una nueva ventana)
- https://eandt.theiet.org/content/articles/2020/12/touch-free-vital-signs-monitor-enabled-with-radar-system/(se abrirá en una nueva ventana)
- https://uk.news.yahoo.com/touch-free-vital-signs-monitoring-000100531.html(se abrirá en una nueva ventana)
- https://uk.finance.yahoo.com/news/touch-free-vital-signs-monitoring-000100531.html(se abrirá en una nueva ventana)
- https://www.aol.co.uk/2020-12-10-touch-free-vital-signs-monitoring-technique-revealed-by-scientis.html(se abrirá en una nueva ventana)
Outreach activities include: Explorathon, Botanical Garden, as well as 1 book chapter and 3 conference presentations. Extended work lead to 1 book edited and 12 peer-reviewed journal articles.
The proposed research objectives were met at the end of the project and the measurement results have exceeded expectations.
The research has produced numerous results extending the state-of-the-art in terms of performance, and that has initiated the interest from the host institution in exploiting the commercial viability of the developed prototypes. This position is strengthened by the novel system architecture that was used to obtain results with unprecedented accuracy.
The ER and his team are currently holding discussions with A&E doctors, cardiologists, care ome managers, and the NHS Lothian (all in agreement with the Heriot Watt University Enterprise) in order to bring the developed technology into people's homes and hospitals.
The next step includes securing funding to continue the research work, identify market opportunities within the biomedical / health / assisted living sectors, and submitting additional proposals for support and collaborations.
Successful continuation will have a tremendous impact not only in the EU/UK economy but most importantly on our society, as it can save people's lives (particularly of people with dementia / arrhythmias / SIDS history) and improve our quality of living.
The research has produced numerous results extending the state-of-the-art in terms of performance, and that has initiated the interest from the host institution in exploiting the commercial viability of the developed prototypes. This position is strengthened by the novel system architecture that was used to obtain results with unprecedented accuracy.
The ER and his team are currently holding discussions with A&E doctors, cardiologists, care ome managers, and the NHS Lothian (all in agreement with the Heriot Watt University Enterprise) in order to bring the developed technology into people's homes and hospitals.
The next step includes securing funding to continue the research work, identify market opportunities within the biomedical / health / assisted living sectors, and submitting additional proposals for support and collaborations.
Successful continuation will have a tremendous impact not only in the EU/UK economy but most importantly on our society, as it can save people's lives (particularly of people with dementia / arrhythmias / SIDS history) and improve our quality of living.