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Frequently Asked Question (FAQ). Click on topics below for more details

1. How the device works?

Basically the device works on the principle of trans reflectance spectroscopy technique where there is a glucose specific led placed inside the device which will emit light and this light will help in detecting the glucose values during measuring of the finger.

2. What is the technology you are using?

Device will use transreflectance spectroscopy, where visible light will provide transmission data and infrared light will provide reflectance data. Along with this occlusion of the tip will help in increased penetration of NIR, and device design will help in prevention of sray light interaction. Chemometric predictive model will convert the detected transmitted and refected light data into glucose values.

3. How accurate the device is when compared to anaytical readings?

As of now the device is 70% accurate. We are going to collect the data of 20000 patients and making the device + or – 2% compared to analytical readings.

4. Does NIR has any harmful effects on humans? Is there any medical device which works on NIR?

NIR is not at all harmful for humans. Example BSX Athletics and Moxy Monitor wearable monitors using NIR spectroscopy to measure muscle oxygenation.

5. Where the data is stored?

Patient’s data is automatically stored in the application. Around 30 readings can be stored in the device.

6. What are the features does app has?

The app which is interlinked to the device has different parameters to feature which include diabetic diet suggestions for patient, questionnaires to know whether the person is diabetic or not, storage of the data.

7. Are there any other devices which work non-invasively? Did they got any approvals?

Yes, there are other devices which are in the process of developing. CNOGA, Diamontech, Helo Extenze, Glucowise, Orsense. Glucoise and Diamontech are still underdevelopment. Helo Extenze has still not scured any approvals.

8. Whether the device has any approvalss CDSC& FDA?

Yes, we applied for CDSCO it is under process we will get the approval by the end of 2019. After CDSCO we will also apply for FDA.

9. Can I get my patients data in the app?

Yes, doctors can access the data of their patients on the app for better healthcare of their patients.

10. Can I connect with the patients through the app?

Yes, doctor can connect with their patients and vice-versa during any emergencies or fr any information.

11. Will the app suggest any lifestyle changes?

Yes, the app contains diabetic diet suggestions and etc.

12. Wether the information provided by the app is authenticated?

Yes, the data provided in the app authenticated by best diabetologists.

13. How will you ensure patients data privacy?

All the patient data will be securely uploaded to an encrypted cloud service for further imprvment of the algorithm for achieving better results.

14. Will the device detect parameters other than blood glucose levels?

Along with glucose value device also detects, Diabetic neuropathy, Insulin site, Diabetic foot ulcer, Diabetic Ketoacidosis.

15. Does the device contain any features for blind people?

Yes, the device has a voice readout facility for the blind people, so that they can know their blood glucose values and other parameters6

1. Does the device has any limitations using it on Type I or Type II Patients?

The device has no limitations for type 1 and type 2 diabetic patients, it works similarly for both the diseased conditions giving the accurate results.

2. How many times a Type I patient has to measure his glucose values in a day?

All the type 1 diabetic patients need to measure their glucose readings 5 times per day.

3. How many times a Type II patient has to measure his glucose values in a day?

All the type 2 diabetic patients need to measure their glucose readings 5 times per day.

4. How will the device help in prevention of diabetic neuropathy?

The device consists of monofilament which helps in detecting the diabetic neuropathy condition in diabetic patients earlier to its onset.

5. Can the device be used to prevent diabetic ketoacidosis?

The device consists of a breath sensor which helps in detecting the acetone levels from the breathe this will help in analysing the diabetic ketoacidosis condition in the particular patient.

6. Can the device be used to prevent diabetic foot ulcer?

The device consist of a temperature sensor which helps in detecting the foot temperature in particular patients. In every diabetic foot ulcer patient there is an increased dorsal foot temperature upto 4 to 5 degrees than normal patient foot temperature, this situation can help in measuring the foot temperature with temperature sensor which helps in detecting the foot ulcer condition.

7. How will the device aid in prevention of lipodystrophy?

The device consists of a vernier callipers attached to it which helps in measuring the skin fold thickness at various insulin sites. In diabetic patients due to regular insulin shots they are more prone to lipodystrophy conditions, to prevent this the skin fold thickness measurement helps in understanding the more fat tissue present areas by detecting this the insulin shot site rotation can be done which will help the patient in reducing the lipodystrophy conditions.

8. Can the device be used in patients who already have diabetic foot ulcer?

The device can be used on diabetic foot ulcer patients also, by measuring the temperature of the foot if it is high or low based  the patient can be advised with wound healing treatment or guidance.

9. Can the device be used in patients who already have diabetic neuropathy?

The device can be used on diabetic neuropathy patients also.

1. What is the working principle of the devie?

Device works on the basis of Trans-refetance spectroscopy.

2. What is transreflectance spectroscopy?

Trans-reflectance spectroscopy is the simultaneous and conitinuous use of visible light for collection of transmissio data and infra red light for reflectance data collection.

3. Which region in Electro Magnetic Radiation are we using for transreflectance spectrospy?

Red light and infra red light are used for glucose detection in the device.

4. what is the Visible wavelength used in the device?

675 nm (red light) is used.

5. What is the Near infra red wavelenghth used in the device?

940nm and 1100nm (infra red light) is used.

6. How did you select specific wavelength?

Few glucose specific wavelengths are selected by performing preliminary analysis on the glucose solutions using ocean optics spectromete

7. What are the experimental procedures followed to select the wavelenghth?

Extensive phantom tissue model study is performed, where multilayered tissue phantoms containing epidermis, dermis, hypodermis, blood vessels, blood and bone like components with similar optical properties as their named tissues. Various levels of glucose concentrations are prepared and injected into the phantoms to accquire in-vitro data for detection of highly glucose specific wavelength.

8. What will be the possible outcomes of the experiments performed?

Possible outcomes include: Highly glucose specific wavelength will be determined, biological interferences which cause errors can be reduced to a minimum level.

9. What is the advantage of multiple wavelengths over single wavelength in glucose detection?


10. Are all the parameters interlinked with each other in the device?

Yes, all the parameters are interlinked with each other in the device.

In-vitro testing related

11. What is the sample size in in-vitro testing?

Sample size is 0-800 mg/dl with an intervals of 1mg/dl. Each sample data is collected as triplicate.

12. How will the data collected be analyzed?

Collected data will be analysed for highly glucose specific wavelengths using chemometric analysis using unscrambler X software (CAMO analytics).

13. Which type of biological interferences errors can be removed using in-vitro testing?

Biological interferences of skin, melanin pigmentation, tissue absorption and scattering, blood moleucle interference, bone, finger nail interferences. Water and fat absorption errors can also be identified and removed using in-vitro testing and chemometric analysis.

Device Design related

14. How will the device design help in addressing pressure problem?

Device design will help in maintaining uniform pressure on any type of finger irrespective of its thickness, negating the errors which can be developed due to variable pressure on fingers.

15. How will the occlusion of fingertip help in glucose detection?

Occlusion of blood at the fingertip will increase the glucose concentration and enhace the finger optics helping in deeper penetration of NIR light.

16. How will skin roughness errros will be reduced?

LED placement will help in reducing the skin roughness errors for obtaining maximum reflectance data.

17. How will the stray light problem be addressed using device design?

Stray light interference can be negated by device structure, as it will prevent the stray light from interacting with the nir light source and detector.

18. What are other device structures which are used for detection of diabetic complications?

Other device structures installed in the device are IR temperature sensors (for foot ulcer detection), acetone sensor (for ketoacidosis detection), retractile monofilament (for neuropathy detection) and skinfold detection device (for insulin shot site detetion).

19. What is the angle of LED placement and detector placement?


20. What should the viewing angle of LED be?


21. How much should be the distance between Light source and glass used in the device to avoid errors in readings?

Algorithm related

22. How will the algorithm be develped?


23. Which type of algorithm will be developed?


24. What type of errors will the alogirthm be able to negate?


25. How can environment conditional errors be prevented using the algorithm?


26. How can physiological errors be prevented using the algrithm?

Physiological errors caused by heart rate, blood pressure, skin temperature etc can be incorporated into the algorithm for prevention of errors.

27. What is the accuracy of algorithm?


28. What is the sample size required for obtaining 95% and above accuracy?


29. How consistent is the algorithm?


30. How will you keep improving the algorithm for better accuracy in the future?


31. Which software will be used to develop the algorrithm?


Diabetic ketoacidosis related

1. How can diabetic ketoacidosis detection be included in the device?

Diabetic ketoacidosis is the most common complication for all diabetic patients. In diabetic ketoacidosis patients or patients who may prone to diabetic ketoacidosis will have fruity smelling breathe which is nothing but the  release of ketone bodies from persons breathe. In the device we will be having an acetone sensor which will detect the acetone levels from breathe and will analyse the condition of the diabetic ketoacidosis in a particular patient.

2. What type of data is collected for detection of diabetic ketoacidosis?

The data required to detect diabetic ketoacidosis is various diabetic people breathe data are collected and the collected data is used to develop a suitable algorithm and integrated in the device which will give the readings of the acetone levels.

3. Which type of sensors are required to collect the data?

There is only one sensor required to collect the data for diabetic ketoacidosis that is acetone sensor.

4. What is the sample size required to develop algorithm for diabetic ketoacidosis?

Sample size required to develop algorithm will be not more than 1000 patient data.

Diabetic Neuropathy related

5. How can diabetic neurpathy detection be included in the device?

The device consists of monofilament which helps in detecting the diabetic neuropathy condition in diabetic patients earlier to its onset. When the person feet is subjected to this monofilament and when the monofilament bends to c shape, if the person experiences no sensation to the feet then the person is suffering with diabetic neuropathy.

6. What type of data is collected for detection of diabetic neuropathy?

The data required to detect diabetic neuropathy is various diabetic people feet data are collected using the monofilament test and these data are integrated in the device.

7. What is the sample size required to develop algorithm for diabetic neuropathy?

Sample size required to develop algorithm will be not more than 1000 patient data.

Diabetic foot ulcers related

8. How can diabetic foot ulcer detection be done using the device?

Diabetic foot ulcer can be detected using dorsal foot temperature fluctuations, with the help of IR temperature sensors installed in the device. The temperature sensor will sense the temperauture fluctuations and record in the device, which will then analyse and provide reports on the presence or absence of diabetic foot ulcer.

9. What type of data is required for detection of diabetic foot ulcers?

Data required to develop foot ulcer detection is various dorsal foot temperature of different diabetic individuals by using this data the algorithm will be built and integrated into the device.

10. What type of sensors are required to collect the data?

IR Temperature sensor is required to collect the data for foot ulcer complication.

11. What is the sample size required to develop the algorithm for diabetic foot ulcers?

The sample size required to develop the algorithm should be of minimum 1000 patients foot temperature data.

Insulin shot site detection related

12. How will insulin shote site be detected using the device?

Inuslin absorption at a particular site is dependent upon the subcunateous tisse thickness layer. This thickness can be identified by relating with skin fold thickness at particular given sites. Device will have a installed skinfold calipers which will help in measuring of skin fold thickness, using such skinfold values device will analyse and inform whether that site is optimum for insulin injection.

13. What type of data is required for detection of insulin shot site?

The data required to detect insulin shot site selection will be of different skin fold thickness measurements of the diabetic patients and based on that data the algorithm is built or it can be directly measured by checking the skin fold thickness of a person who is measuring and where ever the skin fold thickness is less that can be taken as a insulin shot.

14. What is the sample size required to develop the algorithm for insulin shot site detection?

The sample size required to develop insulin shot site selection will be about 500 patient data.