Patent Description:
In digital healthcare sensor applications there is a distinct difference between three types of data: i) consumer grade; ii) medical grade; and iii) research grade.

Consumer grade biosensor data, for example from smart phone apps, home-based sensors and wearable devices, can be collected in large volumes from widely adopted technologies. These devices have, however, often undergone very little validation to demonstrate how meaningful the measurements are, and to characterise their accuracy. Furthermore, it is not clear how stable the measurements are over time, as the software environment of the sensors is often continuously evolving. This can be because the software on the sensor itself (eg: on a wearable), or software in the ecosystems (smartphone app, smart phones OS, cloud servers etc.) is upgraded automatically. It is possible that one reason for this change in software environment is that the sensors or surrounding ecosystem may be "self learning" and alter their performance as they gather more data.

Medical grade biosensor data is collected as part of formal healthcare service provision. This data is normally collected by medical devices, with well characterised performance, and formally validated software that must be updated in a very controlled way. But this data is often limited and sparse compared with what can be obtained from consumer devices, and the devices have less functionality, are less user-friendly, and generally have shorter battery life.

Research grade biosensor data is collected in research studies e.g.: evaluating new drugs or devices or studying the natural history of diseases. The biosensors used in these studies may not be medical devices, but need to have well characterized performance, for example in order to power the study. Similarly the use of the device needs to be controlled to ensure comparable data from different subjects and over time. For all three types of biosensor, obtaining actionable information to inform decision making in clinical trials and patient management requires validation of the measurement for the specific intended purpose, and the ability to calibrate between different devices, e.g. following upgrades of software or hardware. Furthermore, it is desirable to replace one sensor with another one during the course of a clinical trial or the management of a patient, and to ensure the data from the original and replacement sensors is comparable.

The present invention seeks to solve the aforementioned problems by providing systems and methods for validating sensor data such as data obtained from devices including wearable devices and smart phones, for example.

<CIT> discloses a synthetic gesture trace generator, wherein a synthetic gesture trace is generated using a gesture synthesizer which may be implemented in software. The synthesizer receives a number of inputs, including parameters associated with a touch sensor to be used in the synthesis and a gesture defined in terms of gesture components. The synthesizer breaks each gesture component into a series of time-stamped contact co-ordinates at the frame rate of the sensor, with each time-stamped contact co-ordinate detailing the position of any touch events at a particular time. Sensor images are then generated from the time-stamped contact co-ordinates using a contact-to-sensor transformation function. Where there are multiple simultaneous contacts, there may be multiple sensor images generated having the same time-stamp and these are combined to form a single sensor image for each time-stamp. This sequence of sensor images is formatted to create the synthetic gesture trace.

There is disclosed a system for validating biosensor data, the system comprising: i) means for collecting biosensor data from a subject; ii) means for reproducing subject behaviour and/or physiological activity of the subject and collecting data corresponding with such reproduced behaviour and/or physiological activity; and iii) means for detecting any deviation over time in biosensor data collected from the reproduced activity in (ii) in order to identify and/or correct sensor or software introduced deviations in biosensor data collected from the subject.

The ability to use data to support research and clinical practice, as this data is much richer and more available than traditionally available medical grade and research grade data, is highly advantageous in improving the quality of research, regulatory submissions and subject care. The system defined by this aspect of the invention enables professionals to use consumer grade data with confidence due to the ability to verify the accuracy of collected data regardless of the type or brand of biosensor used to collect data.

The system may further comprise means to apply a correction factor to any deviation identified between biosensor data collected from the subject and data corresponding to reproduced behaviour.

According to the present invention, there is provided a method of calibrating sensing data between different sensors of the same type, the method comprising: a) obtaining physiological data from a test subject through a first sensor; b) using said physiological data to program a device to replicate physiological attributes exhibited by the test subject; c) applying a second sensor to the device, the second sensor comprising following upgrades of software or hardware from the first sensor; d) using the device to replicate the test subject's physiological attributes; e) obtaining physiological data from the second sensor regularly over time; f) identifying any variation between the physiological data obtained by the first sensor and the physiological data obtained by the multiple measurements made over time with the second sensor; g) applying a correction factor to the physiological data obtained from the second sensor in the event that any variation between physiological data obtained from the first sensor and physiological data obtained from the second sensor exceeds a predetermined threshold; and h) using said correction factor to calibrate the second sensor.

There is also provided a method of simultaneously calibrating sensing data from multiple sensors of the same type, the method comprising: a) obtaining physiological data from a test subject through a first sensor; b) using said physiological data to program a device to replicate physiological attributes exhibited by the test subject; c) applying multiple additional sensors to the device, the multiple additional sensors comprising following upgrades of software or hardware from the first sensor; d) using the device to replicate the test subject's physiological attributes; e) obtaining physiological data from the additional sensors; f) identifying any variation between the physiological data obtained by the first sensor and the physiological data obtained by the additional sensors; g) applying a correction factor to the physiological data obtained from the additional sensors in the event that any variation between physiological data obtained from the first sensor and physiological data obtained from the additional sensors exceeds a predetermined threshold; and h) using said correction factor to calibrate the additional sensors.

Use of the terms patient and subject are used interchangeably throughout and may relate to clinical patients, athletes or placebo subject, for example.

Embodiments of the inventions will now be described by way of reference to the following figures:.

The technical problem is indicated schematically in <FIG>. The biosensor (which might be a wearable, wall mounted or static device, or built into a smart-phone) makes measurements that then pass through a number of stages of processing on the sensor device, on any smart phone involved in the chain, and in any file server the data is transferred to. In <FIG>, those various software processes and data transfers are represented as a "software cloud". The output is uncontrolled because what happens in the cloud can change in an unknown way at unknown times.

Key technical challenges need to be overcome to achieve the goal of using biosensors for regulated applications in clinical research and subject management, for example in clinical trials of new drugs, and as digital healthcare companion products to drugs. This requires addressing the issues of system validation, and quality assurance of data that is obtained out of this software cloud, in particular so any change over time in the outputs that is caused by the hardware software changes, device failure or improper use can be detected and corrected for.

Embodiments of the present invention describe a solution to the aforementioned technical challenges.

Rather than the validation of the biosensor being done before the device is used, with a carefully controlled process for upgrading software if any changes are required, the validation becomes a continuous process while data is being collected. This requires a standardised input of sensor data from a laboratory environment into the same software cloud that is handling the subject data.

This standardised input involves the use of a robot mimic. For the purposes of this application, "Robot Mimic" means a computer controlled system that, when connected to or measured by the sensor, generates sensor data that is highly correlated with that generated by the clinical trial subject/patient when connected to or measured by the same sensor. <FIG> illustrates one example of how the robot mimic may be set up using actual biosensor data. Raw data from the biosensor is used to set up a simulated bio-cycle in the robot mimic.

The simulated bio-cycle determined using the approach illustrated in <FIG> can be run through the robot mimic as often as required and the robot mimic has sufficient reproducibility to ensure that it performs with a reproducibly that is much better than the measurements accuracy required by the application.

The overall validation of the biosensor measurements requires that measurements from the robot mimic are collected periodically or continuously while the clinical trial subject/patient data is being collected. During this validation process, the same type of biosensor, or biosensor(s), are connected to subject and robot mimic, and the same software cloud is used for the analysis of all data collected. Biosensors used in embodiments of the invention can be any type of biosensor, including consumer grade biosensors "out of the box" as illustrated in <FIG>.

Performance of the system can be improved by using multiple robot mimics rather than just one in parallel with the data collection from the subjects.

It is also possible for multiple sensors (eg: different brands of activity watch) to be attached simultaneously to the same robotic mimic to enable the clinical trial subjects / patients to use different sensors in the same study, with standardisation across those sensors provided by the system.

Biosensors that could be validated by embodiments of the invention include, but are not limited to. temperature, blood pressure, speech, activity, and social connectivity (proximity to another sensor.

The invention can also optionally acquire baseline reference data from the subjects or clinical trial subjects under investigation, for example during a set-up phase in a clinical environment while the subject or clinical trial subjects is being observed by a medical professional or a video recording system peforming activities relevant to that sensor.

An embodiment of the invention involves a sensor that measures sleep from patients with a particular pathology (eg: Parkinson's disease), using three axis accelerometers included in a wrist-worn device. This involves the following steps:.

Step <NUM>. Calibration of a different <NUM>-axes accelerometer based biosensor (eg: one that has a longer battery life), sensor B, using the robot mimic programmed in step <NUM>. This calibration would enable sleep measures derived from sensor B to be made comparable to measures of sleep from PSG, just had been done for Sensor A. And this would have been achieved without Sensor B ever having been worn by subjects undergoing PSG.

A second embodiment of the invention involves a biosensor that measures skin temperature and activity and which sends this information via low energy blue-tooth to a smart phone, from where it is sent via either the mobile phone air interface or WiFi to a cloud server, and then via an application programming interface (API) provided by the sensor supplier to a controlled database to be used for research or clinical purposes. During these various data transfers the biosensor data is compressed so that all that is delivered to the controlled database is information on number of steps, amount of deep and light sleep, and skin temperature. It is the data in the controlled database that needs to be validated for the purpose for which it is being used. This involves four stages:.

Claim 1:
A method of calibrating sensing data between different sensors of the same type, the method comprising:
a) obtaining physiological data from a test subject through a first sensor;
b) using said physiological data to program a device to replicate physiological attributes exhibited by the test subject;
c) applying a second sensor to the device, the second sensor comprising following upgrades of software or hardware from the first sensor;
d) using the device to replicate the test subject's physiological attributes;
e) obtaining physiological data from the second sensor regularly over time;
f) identifying any variation between the physiological data obtained by the first sensor and the physiological data obtained by the multiple measurements made over time with the second sensor;
g) applying a correction factor to the physiological data obtained from the second sensor in the event that any variation between physiological data obtained from the first sensor and physiological data obtained from the second sensor exceeds a predetermined threshold; and
h) using said correction factor to calibrate the second sensor.