CROSS REFERENCE TO PRIOR APPLICATIONS

This application claims the benefit of European Patent Application No. 19182377.2, filed Jun. 25, 2019 which is hereby incorporated by reference herein.

FIELD OF THE INVENTION

The invention relates to evaluating movement of a subject, and in particular to a computer-implemented method, apparatus and computer program product for evaluating movement of a subject.

BACKGROUND OF THE INVENTION

Falls are common among older adults. A fall is an event which typically results in a person impacting with and coming to rest on the ground or floor, or other lower level. In an estimated 20-30% of cases, falls lead to mild or severe injuries and may result in visits to an emergency department. A person lying on the ground or floor for a long time after a fall may be detrimental to the person, and therefore it is of importance to get help as fast as possible after a fall. Personal Emergency Response Systems (PERS) can enable people to signal for help by pressing a help button that is worn as a pendant around the neck or as a watch on the wrist. Other wearing locations include waist (belt), pockets and at the ears. When the person presses their help button, they can be connected to a help provider (e.g. in a call centre) via an in-home communicator.

In addition to a help button, certain PERS devices can contain one or more sensors, such as an accelerometer and air pressure sensor, for enabling falls to be automatically detected. Algorithms that use the sensor signal(s) as input may be used to automatically trigger an alarm in case a fall is detected. This may result in help being obtained for the person more quickly, particularly if the person is not able to press the help button (e.g. the person is unconscious) or forgets to press the button. These algorithms typically determine values for one or more features from the sensor signals (e.g. an impact magnitude, a change in altitude, etc.) and determine whether a fall has occurred on the basis of those feature values.

In some cases the PERS functionality is provided by a dedicated device (e.g. that comprises the sensor(s) and capability to process the sensor signals), but in other cases the PERS functionality can be provided by a more general purpose electronic device that includes suitable sensors, such as a smartphone or smartwatch, and that runs a PERS software application.

In either case it is important for the reliable operation of the fall detection for the signals and measurements provided by the sensor(s) to be accurate and free (or relatively free) from artefacts. The accuracy of a sensor can depend on the quality of the sensor, and the quality of a sensor can vary between different types of electronic device (including dedicated PERS devices) that can provide PERS and/or fall detection capability. In addition, some sensors may require calibration before use, and/or require calibration from time to time, and a poor calibration or a calibration that drifts over time can affect the accuracy of the measurements from the sensor or features derived therefrom. In the case of a more general electronic device that includes suitable sensors, the provider of the PERS software application is not able to control the quality of the sensors in the device, and may not be able to adequately calibrate the sensors.

Similar considerations apply to other types of devices (or software applications) that generally evaluate the movement of a person, for example that detect footsteps of the person in order to detect when the person is walking, or that detect when a person has stood up from sifting on a chair (known as a sit-to-stand (STS) movement or STS transfer). Various measured characteristics of a person's movement (e.g. walking) can be used by clinicians to assess the mobility of the person, including assessing a person's risk of falling.

Therefore there is a need for techniques that provide for reliable evaluation of the movement of a person irrespective of artefacts or quality issues present in a measurement signal from a sensor that measures the movements of the person.

SUMMARY OF THE INVENTION

One example of sensor artefacts affecting the reliable evaluation of movement of a person is an acceleration signal from an accelerometer that can suffer from drift, i.e. the measurements can include an offset (also referred to as a DC offset), and so any features derived as part of a fall detection algorithm that are sensitive to drift or offset may be less reliable (or even unreliable), thereby affecting the reliability of the fall detection outcome (i.e. fall or no fall). For example, a positive offset in the acceleration measurements could lead to an acceleration threshold for detecting an impact being exceeded (suggesting a fall) even though no impact has occurred. Other features that can be sensitive to drift or offset include peak power and peak acceleration during a sit-to-stand movement.

Another example is an air pressure sensor used to measure changes in altitude where the environmental air pressure changes or fluctuates due to weather conditions, such as storms or wind, or where an air vent in a housing that leads to the air pressure sensor can be obstructed from time to time. In such situations the environmental air pressure can exhibit large fluctuations, which can be identified as changes in altitude of the person, which in turn may produce an unreliable result from the movement evaluation algorithm.

In a general case, the measurements from any type of sensor may have an intrinsic amount of noise in the measurements due to the age/quality of the sensor, or the way in which the sensor is installed in the device. In this case the noise present in the measurements may affect the reliability of any features derived from the measurements.

One approach to addressing this problem is to use a movement evaluation algorithm that does not extract features that are sensitive to the artefacts or quality issues, e.g. an algorithm that only extracts features from acceleration measurements that are not sensitive to offset or drift in the measurements. For example, a mobility or fall risk evaluation algorithm can use offset-robust features such as walking intensity, walking regularity and maximum jerk during a STS transfer. However, this is not ideal, as signal artefacts and quality issues may not be present all of the time (or even a majority of the time), and the features that are not used may have provided useful information for evaluating the movement and improved the reliability of the movement evaluation algorithm.

The techniques proposed herein provide an alternative approach in which the configuration of the movement evaluation algorithm is adjusted or adapted in response to measurement signal quality issues. For example, a decision threshold of the movement evaluation algorithm (for example a threshold beyond which a fall is detected) may be determined based on the measurement signal quality. As another example, a weighting used in the movement evaluation algorithm for a feature derived from the measurement signal (i.e. where the weighting indicates an influence of the feature on the movement evaluation) can be determined based on the measurement signal quality. In this way, the effect of signal quality issues on the reliability of the output of the movement evaluation algorithm can be reduced. These techniques can be used both with dedicated devices for evaluating movement (e.g. a PERS device, a fall detector, a walking detector, etc.), and other devices that can be programmed or configured to evaluate movement (e.g. a smartphone or smartwatch).

According to a first specific aspect, there is provided a computer-implemented method for evaluating movement of a subject. The method comprises obtaining a first signal from a first sensor; processing the first signal to determine a value of a quality measure for the first signal; and determining a configuration for a movement evaluation algorithm. The configuration of the movement evaluation algorithm is dependent on the determined value of the quality measure for the first signal such that either or both of: a decision threshold of the movement evaluation algorithm is determined based on the determined value of the quality measure; and one or more weightings used in the movement evaluation algorithm for one or more first features that are to be derived from a signal from the first sensor are determined based on the determined value of the quality measure. Thus, the first aspect enables reliable evaluation of the movement of a subject irrespective of artefacts or quality issues present in a measurement signal from a sensor that measures the subject.

In some embodiments the method further comprises obtaining a further first signal from the first sensor, the further first signal representing measurements of the subject; and processing the further first signal using the movement evaluation algorithm with the determined configuration. In these embodiments the configuration of the movement evaluation algorithm can be determined prior to the first sensor being carried or worn by the subject, for example including during manufacture or installation of the first sensor, or when the first sensor is paired with an apparatus that performs the method. Alternatively, these embodiments also apply where a configuration of the movement evaluation algorithm is determined using the first signal and then subsequent measurements of the subject are obtained and the movement of the subject evaluated from those measurements.

In alternative embodiments, the first signal is a signal representing measurements of the subject, and the method further comprises processing the first signal using the movement evaluation algorithm with the determined configuration. These embodiments enable the configuration of the movement evaluation algorithm to be determined from the same signal that the movement of the subject is to be evaluated from.

In some embodiments the decision threshold of the movement evaluation algorithm is a function of the value of the quality measure. In these embodiments the movement evaluation algorithm can be configured based on the value of the quality measure such that, at a first quality of the first signal, the decision threshold has a first value, and at a second quality of the first signal that is lower than the first quality, the decision threshold has a second value that is higher than the first value of the decision threshold. In this way, the reliability of the decision to be output by the movement evaluation algorithm is increased if the quality of the first signal is low.

In alternative embodiments, the movement evaluation algorithm is configured based on the value of the quality measure such that, at a first quality of the first signal, the decision threshold is an operating point on a first receiver operating characteristics, ROC, curve that provides a first specificity and first detection probability, and at a second quality of the first signal that is lower than the first quality, the decision threshold is an operating point on a second ROC curve that provides a second specificity that is higher than the first specificity and a second detection probability that is lower than the first detection probability.

In some embodiments the one or more weightings are a function of the value of the quality measure. In these embodiments each weighting has a non-zero value between 0 and 1. Thus, regardless of the value of the quality measure, each first feature is still able to contribute to the decision made by the movement evaluation algorithm. In some embodiments the movement evaluation algorithm is configured based on the value of the quality measure such that, at a first quality of the first signal, a first weighting for a first specific first feature has a first value, and at a second quality of the first signal that is lower than the first quality, the first weighting has a second value that is lower than the first value of the first weighting. Thus, these embodiments provide that the influence of a first feature on the decision of the movement evaluation algorithm is reduced when the quality of the first signal is low, and vice versa. In these embodiments the first specific first feature is a first feature whose accuracy and/or reliability is dependent on the quality of the first signal. In these embodiments a second specific first feature is derived from the signal from the first sensor as part of the movement evaluation algorithm, and the weighting for the second specific first feature is not dependent on the value of the quality measure. The second specific first feature is a first feature whose accuracy and/or reliability is not dependent on the quality of the first signal.

In some embodiments, in the movement evaluation algorithm: values for the one or more first features are derived from a signal from the first sensor; the values are combined according to the one or more weightings; and the combined values are compared with one or more decision thresholds.

In some embodiments the first sensor is an accelerometer and the signal from the accelerometer is an acceleration signal representing acceleration measurements. In these embodiments the one or more first features can comprise any one or more of: presence of an impact; change in altitude; velocity; vertical velocity; orientation of the subject and/or a part of the body of the subject; change in orientation of the subject and/or a part of the body of the subject; duration of the subject rising from a chair; peak acceleration of the subject rising from a chair; peak power of the subject rising from a chair; maximum jerk of the subject rising from a chair; and variance, period, periodicity, sample entropy and/or cadence of walking by the subject. In these embodiments the quality measure can be a measure of an offset in the acceleration signal. In these embodiments the one or more weightings for any of presence of an impact; change in altitude; velocity; vertical velocity; orientation of the subject and/or a part of the body of the subject; duration of the subject rising from a chair; peak acceleration of the subject rising from a chair; and peak power of the subject rising from a chair, can be determined based on the determined value of the offset. In these embodiments one or more weightings for any of change in orientation of the subject and/or a part of the body of the subject; maximum jerk of the subject rising from a chair; and variance, period, periodicity, sample entropy and/or cadence of walking by the subject may not be determined based on the determined value of the offset.

In some embodiments the first sensor is an air pressure sensor and the signal from the air pressure sensor is an air pressure signal representing air pressure measurements. In these embodiments the one or more first features may comprise any one or more of: altitude, change in altitude of the subject; vertical velocity; and vertical acceleration. In these embodiments the quality measure may be a measure of variance in the air pressure signal or a measure of variance in differences in the air pressure signal. In these embodiments the weighting for altitude may be determined based on the determined measure of variance.

In some embodiments the movement evaluation algorithm evaluates the movement of the subject to determine one or more of: whether the subject has fallen, a fall risk for the subject, a mobility level of the subject and the physical activity or activities performed by the subject.

According to a second aspect, there is provided a computer program product comprising a computer readable medium having computer readable code embodied therein, the computer readable code being configured such that, on execution by a suitable computer or processor, the computer or processor is caused to perform the method according to the first aspect or any embodiment thereof.

According to a third aspect, there is provided an apparatus for evaluating movement of a subject. The apparatus comprises a processing unit that is configured to: obtain a first signal from a first sensor; process the first signal to determine a quality measure for the first signal; and determine a configuration for a movement evaluation algorithm that is for evaluating movement of the subject. The configuration of the movement evaluation algorithm is dependent on the determined value of the quality measure for the first signal such that either or both of: a decision threshold of the movement evaluation algorithm is determined based on the determined value of the quality measure; and one or more weightings used in the movement evaluation algorithm for one or more first features derived from a signal from the first sensor are determined based on the determined value of the quality measure. Thus, the third aspect enables reliable evaluation of the movement of a subject irrespective of artefacts or quality issues present in a measurement signal from a sensor that measures the subject.

In some embodiments the processing unit is further configured to obtain a further first signal from the first sensor, the further first signal representing measurements of the subject; and process the further first signal using the movement evaluation algorithm with the determined configuration. In these embodiments the configuration of the movement evaluation algorithm can be determined prior to the first sensor being carried or worn by the subject, for example including during manufacture or installation of the first sensor, or when the first sensor is paired with the apparatus. Alternatively, these embodiments also apply where a configuration of the movement evaluation algorithm is determined using the first signal and then subsequent measurements of the subject are obtained and the movement of the subject evaluated from those measurements.

In alternative embodiments, the first signal is a signal representing measurements of the subject, and the processing unit is further configured to process the first signal using the movement evaluation algorithm with the determined configuration. These embodiments enable the configuration of the movement evaluation algorithm to be determined from the same signal that the movement of the subject is to be evaluated from.

In some embodiments the decision threshold of the movement evaluation algorithm is a function of the value of the quality measure. In these embodiments the movement evaluation algorithm can be configured based on the value of the quality measure such that, at a first quality of the first signal, the decision threshold has a first value, and at a second quality of the first signal that is lower than the first quality, the decision threshold has a second value that is higher than the first value of the decision threshold. In this way, the reliability of the decision to be output by the movement evaluation algorithm is increased if the quality of the first signal is low.

In alternative embodiments, the movement evaluation algorithm is configured based on the value of the quality measure such that, at a first quality of the first signal, the decision threshold is an operating point on a first receiver operating characteristics, ROC, curve that provides a first specificity and first detection probability, and at a second quality of the first signal that is lower than the first quality, the decision threshold is an operating point on a second ROC curve that provides a second specificity that is higher than the first specificity and a second detection probability that is lower than the first detection probability.

In some embodiments the one or more weightings are a function of the value of the quality measure. In these embodiments each weighting has a non-zero value between 0 and 1. Thus, regardless of the value of the quality measure, each first feature is still able to contribute to the decision made by the movement evaluation algorithm. In some embodiments the movement evaluation algorithm is configured based on the value of the quality measure such that, at a first quality of the first signal, a first weighting for a first specific first feature has a first value, and at a second quality of the first signal that is lower than the first quality, the first weighting has a second value that is lower than the first value of the first weighting. Thus, these embodiments provide that the influence of a first feature on the decision of the movement evaluation algorithm is reduced when the quality of the first signal is low, and vice versa. In these embodiments the first specific first feature is a first feature whose accuracy and/or reliability is dependent on the quality of the first signal. In these embodiments a second specific first feature is derived from the signal from the first sensor as part of the movement evaluation algorithm, and the weighting for the second specific first feature is not dependent on the value of the quality measure. The second specific first feature is a first feature whose accuracy and/or reliability is not dependent on the quality of the first signal.

In some embodiments, in the movement evaluation algorithm: the processing unit is configured to derive values for the one or more first features from a signal from the first sensor; combined the derived values according to the one or more weightings; and compare the combined values with one or more decision thresholds.

In some embodiments the first sensor is an accelerometer and the signal from the accelerometer is an acceleration signal representing acceleration measurements. In these embodiments the one or more first features can comprise any one or more of: presence of an impact; change in altitude; velocity; vertical velocity; orientation of the subject and/or a part of the body of the subject; change in orientation of the subject and/or a part of the body of the subject; duration of the subject rising from a chair; peak acceleration of the subject rising from a chair; peak power of the subject rising from a chair; maximum jerk of the subject rising from a chair; and variance, period, periodicity, sample entropy and/or cadence of walking by the subject. In these embodiments the quality measure can be a measure of an offset in the acceleration signal. In these embodiments the one or more weightings for any of presence of an impact; change in altitude; velocity; vertical velocity; orientation of the subject and/or a part of the body of the subject; duration of the subject rising from a chair; peak acceleration of the subject rising from a chair; and peak power of the subject rising from a chair, can be determined based on the determined value of the offset. In these embodiments one or more weightings for any of change in orientation of the subject and/or a part of the body of the subject; maximum jerk of the subject rising from a chair; and variance, period, periodicity, sample entropy and/or cadence of walking by the subject may not be determined based on the determined value of the offset.

In some embodiments the first sensor is an air pressure sensor and the signal from the air pressure sensor is an air pressure signal representing air pressure measurements. In these embodiments the one or more first features may comprise any one or more of: altitude, change in altitude of the subject; vertical velocity; and vertical acceleration. In these embodiments the quality measure may be a measure of variance in the air pressure signal or a measure of variance in differences in the air pressure signal. In these embodiments the weighting for altitude may be determined based on the determined measure of variance.

In some embodiments the movement evaluation algorithm evaluates the movement of the subject to determine one or more of: whether the subject has fallen, a fall risk for the subject, a mobility level of the subject and the physical activity or activities performed by the subject.

In some embodiments the apparatus further comprises the first sensor. In alternative embodiments, the processing unit is configured to obtain the first signal from a first sensor that is separate from the apparatus.

According to a fourth aspect, there is provided a system for evaluating movement of a subject. The system comprises a device that is to be worn or carried by a subject and that comprises a first sensor for generating the first signal; and an apparatus according to the third aspect or any embodiment thereof.

In some embodiments, the apparatus is part of the device. In alternative embodiments, the apparatus is separate from the device.

These and other aspects will be apparent from and elucidated with reference to the embodiment(s) described hereinafter.

