Abstract: A method (100) for analyzing medication dosage for a patient (210), comprising: (i) receiving (120) a signal indicative of an activity behavior of the patient over a first time period, wherein the signal is collected by a sensor of a wearable device (220) worn by the patient; (ii) receiving (130) information about medication intake by the patient during the first time period, wherein the information comprises both medication dosage and medication intake times during the first time period; (iii) analyzing (140) the received signal and received medication intake information to determine an effectiveness of the medication on the patient, wherein the effectiveness can indicate an overdosage or underdosage of the medication during the first time period, and wherein analyzing the received signal and received medication intake information comprises generating a medication status score, the medication status score configured to minimize patient-controlled variability in the activity behavior of the patient.

Abstract: A method for monitoring the loneliness state of a subject includes receiving proximity information for a plurality of user devices and then generating a loneliness decision for subjects who use the devices based on the proximity information. In one case, the proximity information may be projected onto a lower dimensional space, distance values corresponding to the proximity information may be compared, and the user devices may be ranked based on the comparison. A user may then be determined to be lonely based on the ranking of the user devices. In other cases, clustering techniques may be applied relative to one or more centroids. Distances may then be calculated and compared for purposes of generating a loneliness decision. In other cases, resource information may be taken into consideration with distance information for generating a loneliness decision.

Abstract: According to an aspect, there is provided a computer-implemented method for evaluating movement of a subject.

Abstract: A method for classifying movement of a user during a period of low activity, such as sleep, is provided. The method includes providing a wearable device having a motion sensor; obtaining sensor data from the motion sensor in a buffer having raw sensor data points within a time period; determining whether at least part of the raw sensor data points in the first buffer meets a predefined condition based on a stationarity of motion in the raw sensor data; deriving a low-temporal resolution representation of the raw sensor data points in the first buffer when the at least part of the plurality of raw sensor data points in the first buffer meets the predefined condition based on stationarity; adapting the low-temporal resolution representation based on an estimated effect of a phenomenon on the low-temporal resolution representation; and classifying movement of the user based on the adapted low-temporal resolution representation.

Abstract: A risk prediction system for predicting a risk of a negative event occurring for a monitored subject. The system comprises an input interface configured to obtain case data containing a history of events for the monitored subject and a predictive model based on deep learning networks. The predictive model comprises embedding layers for embedding the case data to generate embedded data, one or more sequence learning layers configured to learn patterns from sequences of the embedded data and generate sequenced data for each layer and one or more output layers each configured to map the sequenced data of each layer to a risk prediction for a negative event occurring.

Abstract: According to an aspect, there is provided a computer-implemented method (300, FIG. 3) for determining a level of interaction experienced by a subject (102).

Abstract: A method for monitoring a patient includes receiving sensor signals from a sensor arrangement, extracting movement information from the sensor signals, determining a sensing period between the sensor arrangement and a body part of a patient based on the movement information, and determining a respiratory rate of the patient based on the sensor signals occurring during the period of contact. The sensor signals may be received from a sensor arrangement incorporated on or within a wearable item that moves relative to the body part of the patient. The sensor arrangement is in intermittent patterns of contact and non-contact with patient as a result of movement of the wearable item. The wearable item may be, for example, a pendant on a necklace.

Abstract: A method for monitoring the loneliness state of a subject includes receiving proximity information for a plurality of user devices and then generating a loneliness decision for subjects who use the devices based on the proximity information. In one case, the proximity information may be projected onto a lower dimensional space, distance values corresponding to the proximity information may be compared, and the user devices may be ranked based on the comparison. A user may then be determined to be lonely based on the ranking of the user devices. In other cases, clustering techniques may be applied relative to one or more centroids. Distances may then be calculated and compared for purposes of generating a loneliness decision. In other cases, resource information may be taken into consideration with distance information for generating a loneliness decision.

Abstract: A concept for personalizing a fall detection algorithm to a particular subject. Sensor data, responsive to a fall of a subject, is obtained at the fall detector, along with feedback information responsive to a confirmation of whether the subject has fallen and/or whether the subject had not fallen. Parts of the sensor data, and corresponding portions of the feedback information, are transmitted to an external device, which generate update information for the fall detection algorithm. The update information is then used by the fall detector to update, and thereby personalize, the fall detection algorithm.

Abstract: A concept for personalizing a fall detection algorithm to a particular subject. Sensor data, responsive to a fall of a subject, is obtained at the fall detector, along with feedback information responsive to a confirmation of whether the subject has fallen and/or whether the subject had not fallen. Parts of the sensor data, and corresponding portions of the feedback information, are transmitted to an external device, which generate update information for the fall detection algorithm. The update information is then used by the fall detector to update, and thereby personalize, the fall detection algorithm.

Abstract: According to an aspect, there is provided a computer-implemented method for evaluating movement of a subject.

Abstract: According to an aspect, there is provided an apparatus (52) for determining whether a subject has entered or exited a building, wherein the apparatus (52) is configured to obtain air pressure measurements from an air pressure sensor (58; 60) associated with the subject, wherein the air pressure measurements comprise a first plurality of air pressure measurement samples; determine one or more of: a first autocorrelation signal for the first plurality of air pressure measurement samples at a first time lag; a distribution of air pressures for the first plurality of air pressure measurement samples; a distribution of air pressure differences for the first plurality of air pressure measurement samples, wherein each air pressure difference is the difference between the value of a first air pressure measurement sample in the first plurality of air pressure measurement samples and the value of an air pressure measurement sample in the first plurality of air pressure measurement samples that is a second time lag befo

Abstract: According to an aspect, there is provided a computer-implemented method for analysing movement of a subject.

Abstract: According to an aspect, there is provided an apparatus (2) for assessing the mobility of a subject, the apparatus (2) comprising a processing unit (4) that is configured to obtain measurements of the movements of the subject over time; process the obtained measurements of the movements to determine values for a first plurality of parameters; and combine the determined values for the first plurality of parameters to determine an estimate of the time that the subject would take to complete a timed up-and-go, TUG, test.