Abstract: According to an aspect, there is provided a fall detection apparatus for detecting a fall by a user, the fall detection apparatus comprising a processing unit configured to: receive measurements of movements of the user over time from a first movement sensor that is to be worn or carried by the user, determine if any of one or more objects are being carried or used by the user, and determine whether the user has fallen by processing the received measurements of the movements of the user and measurements of movements of any object that is being carried or used by the user.

Abstract: Presented are concepts for monitoring walker usage by a subject. One such concept obtains movement data comprising values of detected movement of an arm of the subject for a first time period during which the subject is determined to have transferred between first and second locations the first time period. It is determined if the subject used a walker during the first time period based on a measure of variance in the values of detected movement of the subject's arm during the first time period.

Abstract: Presented are concepts for monitoring a health status of a subject. One such concept employs determining a trend in a health parameter of the subject based input data relating to a plurality of event occurrences for the subject. One or more irregularities are detected in the determined trend and health status of the subject is then determined based on the detected irregularities.

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 and system for training a fall detection classifier using subject-specific movement data. A subject sets a preferred non-fall detection rate. Movement data responsive to a subject's movements during everyday activities are obtained over a predetermined data collection period. For each detected event in the movement data, values for one or more parameters that may (together or individually) indicate a fall are obtained. The obtained values are used to generate a subject-specific non-fall detection rate function. This non-fall detection rate function is used to derive a threshold value, in reference to the subject-set preferred non-fall detection rate, to distinguish fall events from non-fall events.

Abstract: According to an aspect, there is provided a fall detection apparatus, the fall detection apparatus comprising one or more processing units configured to obtain a first input indicating which one or ones of a plurality of fall detection algorithms have detected a potential fall by the subject, wherein each fall detection algorithm of the plurality of fall detection algorithms is associated with a respective type of fall and detects a potential fall of the associated type by analysing a set of movement measurements for the subject, wherein each respective type of fall has an associated initial state of the subject; obtain a second input indicating the status of the subject prior to the potential fall, wherein the status of the subject is determined by analysing a set of measurements from one or more sensors in the environment of the subject; compare the determined status of the subject prior to the potential fall to the initial state for each type of fall associated with any potential fall indicated in the firs

Abstract: A method for detecting a fall by a user wearing a fall detector, including: detecting a trigger event identifying the time location of a possible fall event in user data; extracting motion features from motion data and physiological features from physiological data from within a time window around the identified time location; and determining whether the detected trigger event is a fall by the user by inputting the at least one of the motion features and at least one of the physiological features into a classifier.

Abstract: The present invention relates to sleepiness assessment. In order to provide a more accurate measure to predict OSA severity, an apparatus is provided for sleepiness assessment. The apparatus comprises an input unit, a processing unit, and an output unit. The input unit is configured to receive data indicative of an activity currently performed by a user. The processing unit is configured to determine, based on the activity currently performed by the user, a current situation the user is engaging in. The processing unit is configured to determine whether the current situation matches one of a plurality of pre-determined situations used for situational sleepiness assessment. In response to the determination that the current situation matches one of the plurality of predetermined situations, the processing unit is configured to send a notification to the user for a self-report of a user's current subjective sleepiness level and/or obtain a user's current objective sleepiness level from sensor data.

Abstract: A device is configured to perform a method that detects a trigger marker for an action corresponding to a segment of a multimedia signal. A fingerprint is generated based on the segment of the multimedia signal at a trigger time point. The generated fingerprint is stored in a database and communicated to the device. During playback of the multimedia signal, fingerprints of segments of the multimedia signal are generated and matched against fingerprints in the database. When a match is found, one or more associated actions for the segment are retrieved by the device. The trigger time point may be determined as a time point near or at the segment of the multimedia signal with the matched fingerprint. In this way, trigger markers for actions may be enabled without modifying the multimedia signal.

Abstract: In one embodiment, an apparatus (12) that samples a signal obtained or derived from an accelerometer, filters the samples, accumulates the filtered samples, and determines which limb the apparatus is associated with based on a comparison of the filtered accumulated samples and a threshold set.

Abstract: In one embodiment, a method (110) that determines conditions including that a user is located in a monitored area and not wearing a wearable device (112), and provides an alert based on the determinations and an input pattern from one or plural sensors (114).

Abstract: A method and system for training a fall detection classifier using subject-specific movement data. A subject sets a preferred non-fall detection rate. Movement data responsive to a subject's movements during everyday activities are obtained over a predetermined data collection period. For each detected event in the movement data, values for one or more parameters that may (together or individually) indicate a fall are obtained. The obtained values are used to generate a subject-specific non-fall detection rate function. This non-fall detection rate function is used to derive a threshold value, in reference to the subject-set preferred non-fall detection rate, to distinguish fall events from non-fall events.

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

Abstract: A method for detecting a fall by a user wearing a fall detector, including: detecting a trigger event identifying the time location of a possible fall event in user data; extracting motion features from motion data and physiological features from physiological data from within a time window around the identified time location; and determining whether the detected trigger event is a fall by the user by inputting the at least one of the motion features and at least one of the physiological features into a classifier.

Abstract: According to an aspect, there is provided a computer-implemented method for evaluating movement of a subject. The method includes 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 the 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. A corresponding apparatus and computer program product are also provided.

Abstract: According to an aspect, there is provided a fall detection apparatus, the fall detection apparatus comprising one or more processing units configured to obtain a first input indicating which one or ones of a plurality of fall detection algorithms have detected a potential fall by the subject, wherein each fall detection algorithm of the plurality of fall detection algorithms is associated with a respective type of fall and detects a potential fall of the associated type by analysing a set of movement measurements for the subject, wherein each respective type of fall has an associated initial state of the subject; obtain a second input indicating the status of the subject prior to the potential fall, wherein the status of the subject is determined by analysing a set of measurements from one or more sensors in the environment of the subject; compare the determined status of the subject prior to the potential fall to the initial state for each type of fall associated with any potential fall indicated in the firs

Abstract: Proposed are concepts for distinguishing between fall events and non-fall-events for different sub-groups within a monitored group (i.e., monitored population) of subjects. It is proposed that an entire/group population of monitored subjects may be portioned into sub-groups, each sub-group consisting of a plurality of members (i.e., subjects) having a certain property value or characteristic unique to that group. A respective decision value may be determine for each sub-group, wherein the decision value for a sub-group takes account of a previously obtained false fall detection rate for that sub-group.

Abstract: A method for detecting a fall by a user wearing a fall detector, including: detecting a trigger event identifying the time location of a possible fall event in user data; extracting motion features from motion data and physiological features from physiological data from within a time window around the identified time location; and determining whether the detected trigger event is a fall by the user by inputting the at least one of the motion features and at least one of the physiological features into a classifier.

Abstract: According to an 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 the 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. A corresponding apparatus and computer program product are also provided.

Abstract: According to an aspect, there is provided a fall detection apparatus, the fall detection apparatus comprising one or more processing units configured to obtain a first input indicating which one or ones of a plurality of fall detection algorithms have detected a potential fall by the subject, wherein each fall detection algorithm of the plurality of fall detection algorithms is associated with a respective type of fall and detects a potential fall of the associated type by analysing a set of movement measurements for the subject, wherein each respective type of fall has an associated initial state of the subject; obtain a second input indicating the status of the subject prior to the potential fall, wherein the status of the subject is determined by analysing a set of measurements from one or more sensors in the environment of the subject; compare the determined status of the subject prior to the potential fall to the initial state for each type of fall associated with any potential fall indicated in the firs