Patent Description:
Patients with heart disease may be monitored to detect cardiac events with various means such as a worn pendant. If such events are detected, verbal verification is obtained to a question produced by speech synthesis. The verbal verification can be analyzed by speech recognition and used to prevent false alarms. In some cases, the medical condition of a patient is monitored with implantable medical devices to detect deviation from desired characteristics. If the monitoring indicates a severe condition, an alert may be generated, but in minor deviations, the patient may be queried about her symptoms for holistic diagnostic procedures. <CIT> discloses a method and system to create a personal priority graph. <CIT> discloses, according to its abstract, an alarm system technology, in which an alarm event is detected at a property monitored by an alarm system when the alarm system was set in an armed state. <CIT> discloses an automatic etiology sequencing system. <CIT> discloses a wireless monitor for a personal medical device system. <CIT> discloses an interactive patient monitoring system using speech recognition.

According to a first example aspect of the present invention, there is provided a method as defined by claim <NUM>.

The annotation may be received by monitoring output of the person and identifying the annotation in the output of the person. The output of the person comprise any of speech; utterance; gesture; textual output; use of a key; and any combination thereof.

The customized event detection data may comprise an anomaly limit for a physiological parameter. The anomaly limit may be a maximum or a minimum. The physiological parameter may concern any of heart rate; blood pressure; blood sugar; respiration rate; respiration flow rate; skin color; shivering; blood oxygen; electrocardiography; body temperature; and facial movement. The customized event detection data for a person comprises any of age; weight; height; normal blood pressure; indication of one or more illnesses of the person; and maximum pulse of the person. The customized event detection data may comprise an anomaly pattern for a plurality of physiological parameters. The anomaly pattern may comprise a condition for a combination of thresholds.

The method may comprise responsive to a first condition supplementing the obtained physiological measurement data with one or more given physiological parameters. The first condition may comprise detecting a predetermined event. The customized event detection data may define the first condition.

The prioritizing may comprise using a machine learning process to determine estimated significance of the detected event. The prioritizing may combine the estimated significance of the detected event and the temporally associated annotation. The method may comprise classifying the detected events based on the combination of the estimated significance of the detected event and the temporally associated annotation.

The method may comprise responsive to detecting a predetermined event prompting the person to issue the annotation. The prompting of the person to issue the annotation may depend on the physiological measurement data and on the customized event detection data.

The method may comprise sending the physiological measurement data to a remote data processing system. The method may comprise sending to the remote data processing system an indication of the detected event. The indication of the detected event may comprise the time of the detected event. The indication of the detected event may comprise an indication of a type of the detected event. The method may comprise sending to the remote data processing system the annotation.

The method may comprise storing and batch sending the obtained physiological measurement data and plural received annotations obtained and received over a period of time. The method may comprise batch sending the obtained physiological measurement data and plural received annotations based on a predetermined schedule and / or when a given volume of data has been collected. The method may comprise batch sending the obtained physiological measurement data and plural received annotations on detecting a predetermined event. The method may comprise batch sending the obtained physiological measurement data and plural received annotations on gaining a given network access. The method may comprise batch sending the obtained physiological measurement data and plural received annotations on receiving a delivery request. The delivery request may be received from the person. The delivery request may be received from a source other than the person. The source other than the person may be the remote data processing system or a person thereof.

The method may comprise producing a list of the detected events and associated annotations. The list may be ordered by the prioritizing. The list may comprise hyperlinks to corresponding physiological measurement data sections.

The obtaining of the physiological measurement data may comprise receiving information from a sensor. The sensor may be configured to continually measure at least one physiological property of the person. The sensor may be worn by the person. The sensor may be implanted. The obtaining of the physiological measurement data may comprise receiving information from a plurality of sensors. The sensors may measure same or different physiological properties.

The detecting of the event may be performed by a local processing unit. The local processing unit may be worn by the person. The local processing unit may be implanted. The local processing unit may be a portable device. The local processing unit may be a mobile communication device such as a mobile phone.

The prioritizing of the detected event may be performed by the local processing unit. Alternatively, the prioritizing of the detected event may be performed by a remote data processing system.

The remote data processing system may comprise a data cloud hosted server. The remote data processing system may comprise a supervisor terminal. The supervisor terminal may be configured to indicate the detected event and the annotation to the supervisor.

The method may further comprise receiving the feedback from the supervisor. The feedback may be received from the supervisor terminal. The supervisor may be a medically trained person such as a doctor. Alternatively, the supervisor may be an artificial intelligence circuitry configured to evaluate the physiological measurements using the annotations.

According to a second example aspect of the present invention, there is provided an apparatus as defined by claim <NUM>.

The apparatus may comprise a user interface configured to receive the annotation from the person. The user interface may comprise a speech recognition circuitry configured to recognize spoken annotations from the person. The user interface may comprise a speech synthesis circuitry configured to output information to the user by speech. The speech recognition circuitry may be at least partly formed using the at least one processor. The speech synthesis circuitry may be at least partly formed using the at least one processor. The user interface may comprise a key configured to receive an annotation. The user interface may be configured to indicate a context for receiving context-sensitively the annotation. The user interface may be configured to prompt the annotation by one or more questions. The annotation may comprise one or more parts provided by the person at one or more times.

According to a third example aspect of the present invention, there is provided a computer program comprising computer executable program code configured to execute any method of the first example aspect.

The computer program may be stored in a computer readable memory medium.

Any foregoing memory medium may comprise a digital data storage such as a data disc or diskette, optical storage, magnetic storage, holographic storage, opto-magnetic storage, phase-change memory, resistive random access memory, magnetic random access memory, solid-electrolyte memory, ferroelectric random access memory, organic memory or polymer memory. The memory medium may be formed into a device without other substantial functions than storing memory or it may be formed as part of a device with other functions, including but not limited to a memory of a computer, a chip set, and a sub assembly of an electronic device.

The apparatus may comprise a memory and a processor that are configured to cause the apparatus to perform the method of the first example aspect.

An example embodiment of the present invention and its potential advantages are understood by referring to <FIG> of the drawings. In this document, like reference signs denote like parts or steps.

<FIG> shows an architectural drawing of a system <NUM> of an example embodiment. The system comprises a local processing unit <NUM>, one or more physiological measurement sensors <NUM> or biosensors in short (here only one is drawn in sake of simplicity), and a remote data processing system <NUM> comprising a plurality of supervisor terminals <NUM> and a database <NUM>.

The local processing unit is in some implementations worn by the person, in some cases it can be implanted or a portable device or a mobile communication device such as a mobile phone. The local processing unit is in some embodiments integrated with at least one of the sensors <NUM>.

In some embodiments, the remote data processing system <NUM> comprises a data cloud hosted server computer, a virtualized server computer, and / or a dedicated server computer.

The supervisor terminal can be used by a supervisor. The supervisor is, for example, a medically trained person such as a doctor or an artificial intelligence circuitry configured to evaluate the physiological measurements using the annotations.

<FIG> is simplified in that the remote data processing system <NUM> can typically operate with a large number of local processing units <NUM> and supervisor terminals <NUM>.

<FIG> and <FIG> show a flow chart of a various process steps that are implemented in some example embodiments. Notice that other than steps <NUM>, <NUM>, <NUM>, <NUM>, <NUM> and <NUM> that are a part of the claimed invention, not all the steps are necessarily taken, and some steps may be taken twice and also it is possible to further perform other steps in addition or instead of any of these steps. These steps include: <NUM> maintaining customized event detection data for a person; and automatically:.

The customized event detection data comprise in an example embodiment an anomaly limit for a physiological parameter such as a maximum or a minimum. The physiological parameter concerns any of heart rate; blood pressure; blood sugar; respiration rate; respiration flow rate; skin color; shivering; blood oxygen; electrocardiography; body temperature; and facial movement, for example. The customized event detection data for a person comprise, for example, any of age; weight; height; normal blood pressure; indication of one or more illnesses of the person; and maximum pulse of the person. In some embodiments, the customized event detection data comprises an anomaly pattern for a plurality of physiological parameters. In an example embodiment, the anomaly pattern comprises a condition for a combination of thresholds.

<FIG> illustrates the detection of event data by showing a graph and how events are detected and annotations given by the person. First, during a period when a person is likely feeling bad <NUM>, a likely anomaly is detected, <NUM>. In the absence of an unsolicited annotation, the person is prompted <NUM> to tell how she feels, but no response is received. Hence, an alert is raised <NUM>. Then, during another period, the person is likely feeling good <NUM>. Likely normal operation is detected <NUM>. In some embodiments, then a supplemental report can be sent to the remote data processing system <NUM> or the earlier sent information may be corrected by clearing the alert, for example.

<FIG> shows some alternative scenarios of event detection taking the annotations into account. First, it is detected that the person is likely to feel bad by monitoring the sensor data, <NUM>. No annotation is received from the person and the event is thus assigned a high priority as apparently suspicious, <NUM>. Next, a similar sensor data is received in <NUM>, but the person annotates that she feels good, <NUM>. Hence, no action appears necessary and the event is classified to some intermediate priority level. Finally, a malfunction situation is presented, <NUM>. Here, the person annotates that there was a cable problem, <NUM>, and the event is classified as a technical problem.

<FIG> shows an example of possible development of detected events, annotations and determined priorities formed by combining the detected events and annotations. In the case of <FIG> all the detected events are measurement-wise equal i.e. the measured signal is the same in each, hence prioritization of events is effectively determined based on annotations.

<FIG> shows a block diagram of the local processing unit <NUM> comprising: a memory <NUM> configured to maintain customized event detection data <NUM> for a person; a local communication circuitry <NUM> configured to obtain physiological measurement data indicative of physiological status of the person; at least one processor <NUM> configure to automatically perform: obtaining physiological measurement data indicative of physiological status of the person; receiving an annotation from the person; detecting an event that is temporally associated with the annotation using the physiological measurement data and the event detection data; and prioritizing the detected event using the temporally associated annotation.

The memory <NUM> can be used to store computer software such as executable program code <NUM> or instructions executing which the at least one processor may control operations of the local processing unit <NUM>.

The local processing unit <NUM> of <FIG> further comprises a user interface <NUM> configured to receive the annotation from the person. The user interface of <FIG> comprises a speech recognition circuitry <NUM> configured to recognize spoken annotations from the person and a speech synthesis circuitry <NUM> configured to output information to the user (i.e. person) by speech. Either or both the speech recognition circuitry <NUM> and the speech synthesis circuitry <NUM> can be at least partly implemented using the at least one processor <NUM> or remote processing equipment. For example, speech of the person is recorded in one example embodiment and sent as such or with some pre-processing to a network-based processing function (e.g. a cloud-based server). Speech synthesis is at least partly distributed a function in one example embodiment so that the speech is at least partly generated in an external processing function and therefrom transferred to the local processing unit <NUM> for output to the person. The user interface of <FIG> further comprises a key <NUM> configured to receive an annotation, such as an emergency button and a display <NUM> for displaying information. The user interface can be configured to indicate a context for receiving context-sensitively the annotation under control of the at least one processor <NUM>, for example. The user interface can configured to prompt the annotation by one or more specifying questions. The annotation may comprise one or more parts provided by the person at one or more times. The local processing unit <NUM> of <FIG> further comprises a communication unit <NUM> for communicating with the remote data processing system <NUM>. The communication unit <NUM> comprises, for example, a local area network (LAN) port; a wireless local area network (WLAN) unit; a cellular data communication unit; or satellite data communication unit. The at least one processor <NUM> comprises, for example, any one or more of: a master control unit (MCU); a microprocessor; a digital signal processor (DSP); an application specific integrated circuit (ASIC); a field programmable gate array; and a microcontroller.

Without in any way limiting the scope, interpretation, or application of the claims appearing below, a technical effect of one or more of the example embodiments disclosed herein is that large amount of sensor data can be processed to identify potentially relevant events taking into account feedback of the person being measured and the measurement data can be appropriately prioritized for subsequent verification by a supervisor. Another technical effect of one or more of the example embodiments disclosed herein is that delivery of irrelevant alerts can be inhibited by receiving and processing annotations of the person.

The software, application logic and/or hardware may reside on the local processing unit <NUM>, the remote data processing system <NUM> or both. If desired, part of the software, application logic and/or hardware may reside on the local processing unit <NUM>, and a part of the software, application logic and/or hardware may reside on the remote data processing system <NUM>. In the context of this document, a "computer-readable medium" may be any non-transitory media or means that can contain, store, communicate, propagate or transport the instructions for use by or in connection with an instruction execution system, apparatus, or device, such as a computer, with one example of a computer described and depicted in <FIG>. A computer-readable medium may comprise a computer-readable storage medium that may be any media or means that can contain or store the instructions for use by or in connection with an instruction execution system, apparatus, or device, such as a computer.

Claim 1:
An apparatus comprising means for performing:
maintaining customized event detection data for a person, comprising any of age; weight; height; normal blood pressure; indication of one or more illnesses of the person; and maximum pulse of the person;
obtaining physiological measurement data indicative of physiological status of the person;
receiving an annotation from the person;
detecting an event that is temporally associated with the annotation using the physiological measurement data and the customized event detection data;
prioritizing the detected event using the temporally associated annotation; and
receiving feedback data concerning the detecting of the event or the prioritizing of the detected events and calibrating the detecting of the event or the prioritizing of the detected events, respectively;
wherein the calibrating comprises adjusting the customized event detection data.