Koninklijke Philips Corporation N.v.

Gracenote, Inc.

Lifeline Systems Company

Roku, Inc.

Koninklljke Philips N.v.

Connect America

Koninklijke Philips Electronics N.v.

Philips Intellectual Property & Standards Gmbh

Philips Deutschland Gmbh

Warner Rudolph Theophile Ten Kate

Job Cornelis Oostveen

Adrianus Johannes Maria Denissen

David Keith Roberts

Patrick Kechichian

Doortje Van De Wouw

Kars-Michiel Hubert Lenssen

Ronaldus Maria Aarts

Murtaza Bulut

Felipe Maia Masculo

Tine Smits

Janneke Annegarn

Salvatore Saporito

Mark Thomas Johnson

Juergen Weese

Floris Maria Hermansz Crompvoets

Yuechen Qian

Jochen Renaat Van Gheluwe

Daan Anton Van Den Ende

Heribert Baldus

Johannes Henricus Maria Korst

Yang Peng

Sander Theodoor Pastoor

Paul Anthony Shrubsole

Helko Lehmann

Sheng Jin

Francesco Sartor

Teun Van Den Heuvel

Haris Duric

Javier Espina Perez

Michael Bellomo

Andrea Ryter

Sietse Menno Rispens

Inge Theodora Margretha Geven

Jozef Hubertus Gelissen

Wilhelmus Andreas Marinus Arnoldus Maria Van Den Dungen

Pavankumar Murli Dadlani Mahtani

Gabriele Papini

Stephen Ledingham

Chaitanya Dongre

Nicolaas Gregorius Petrus Den Teuling

Wei Zhang

Christian Andreas Tiemann

Nathalie Dorothee Pieternel Leurs

Natasha Kravtsova

Herman Ter Horst

Josef Heribert Baldus

Enrique Antonio Gil Ponce

Mariana Nikolova-Simons

Alex Ivanov

Peter George Matthews

Jakobus Middeljans

Stijn Alexander Van Langen

Astrid Dobbelaar

Yanchun Zhang

Wilhelmus Andreas Marinus Van Den Dungen

Andreas Bindszus

Harald Greiner

Arnoldus Fransiscus Schoenmakers

Helen Louis Charman

Jinghan Feng

John Cronin

A method and system for training a fall detection classifier using subject-specific movement data. 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 probability distribution for non-fall events. A fall event probability distribution is obtained using the non-fall event probability distribution. This fall event probability distribution can then be subsequently processed, with reference to a threshold value, to distinguish fall events from non-fall events.

Personalized fall detector

According to an aspect, there is provided a computer-implemented method of determining a fall risk of a subject, the method comprising receiving a first data set indicative of movement of the subject; receiving a second data set indicative of context information of the subject; selecting a part of the first data set based on the second data set; and determining a fall risk based on the selected part of the first data set.

Method and apparatus for determining a fall risk

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.

Fall detection

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.

Method and apparatus for detecting a fall by a user

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.

Monitoring a subject

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.

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.

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 first input; and output an indication that the subject has fallen if the determined status of the subject matches the initial state of any of the respective types of fall associated with any potential fall indicated in the first input.

Fall detection apparatus, a method of detecting a fall by a subject and a computer program product for implementing the method

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.

Fall detector incorporating physiological sensing

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.

Method and device for generating and detecting a fingerprint functioning as a trigger marker in a multimedia signal

In one embodiment, an apparatus () 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.

System and method for detecting wrist-device wearing location

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

System to secure health safety during charging of health wearable

According to an aspect, there is provided a computer-implemented method for evaluating movement of a subject. The method comprises obtaining a first signal representing measurements of the subject from a first sensor; processing the first signal to determine a quality measure for the first signal; determining if the determined quality measure meets a first criterion; if the determined quality measure meets the first criterion, determining values for a plurality of features in a first feature set, the first feature set comprising one or more first features to be determined from the first signal, and evaluating the movement of the subject based on the values for the plurality of features in the first feature set; and, if the determined quality measure does not meet the first criterion, determining values for one or more features in a second feature set, wherein the one or more features in the second feature set are a subset of the plurality of features in the first feature set and the second feature set does not include at least one of the one or more first features in the first feature set, and evaluating the movement of the subject based on the values for the one or more features in the second feature set. A corresponding apparatus and computer program product are also provided.

Evaluating movement of a subject

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Waalre, Netherlands

Warner Rudolph Theophile Ten Kate