Koninklijke Philips Corporation N.v.

Janneke Annegarn

Warner Rudolph Theophile Ten Kate

Heribert Baldus

Wilhelmus Johannes Joseph Stut

Nathalie Magali Danielle Dessaud

Nicolaas Gregorius Petrus Den Teuling

Salvatore Saporito

Sietse Menno Rispens

Warner Rudoph Theophile Ten Kate

Warner Rudolf Theophile Ten Kate

Heriber Baldus

Warner Rudolph Théophile Ten Kate

Techniques are described herein for incorporating fall prevention into a personalized activity plan. In various embodiments, a baseline risk of fall for a user may be estimated () based on baseline sensor signal(s) (). Based on a prediction model () (which may/may not be associated with the user) and the baseline risk of fall, respective risks of falls associated with various candidate activities may be predicted (), and then candidate activities may be determined or selected () based on the predicted respective risks of falls. Output indicative of the determined candidate activities may be provided () to the user, who may provide input () identifying one or more of the determined candidate activities as an exercise conducted or to be conducted by the user. Based on the exercise sensor signal(s), a subsequent risk of fall for the user may be estimated () and used to update () the prediction model.

System and method that optimizes physical activity recommendations based on risks of falls

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

There is provided a method of processing measurements of acceleration to identify steps by a user, the method comprising obtaining measurements of acceleration from a device worn or carried by a user; analysing the measurements of acceleration to determine a value for a threshold that is to be used to identify steps by the user in measurements of acceleration; and using the determined value for the threshold to identify steps by the user in measurements of acceleration.

Detection of walking in measurements of the movement of a user

The invention relates to a method and apparatus for providing feedback to a user, in particular relating to the fall risk of the user and/or relating to advice to reduce the fall risk of the user. According to an embodiment, there is provided an apparatus for providing feedback to a user, the apparatus comprising a user interface for providing feedback to the user; and a processing unit. The processing unit is configured to obtain measurements of the fall risk of the user over a time period, the time period comprising at least one time portion in which the fall risk of the user increases to a first threshold value while moving, the user rests for a resting period when the fall risk reaches the first threshold value, and the user subsequently resumes moving; analyse the measurements of the fall risk of the user to determine the duration of each resting period and the fall risk of the user at the end of each resting period when the user subsequently resumes moving; determine a fall risk recovery profile for the user from the duration of each resting period and the fall risk of the user at the end of each resting period; determine the duration of a resting period required for the fall risk of the user to decrease to a second threshold value using the fall risk recovery profile; and provide feedback to the user on the determined duration via the user interface.

Method and apparatus for providing feedback to a user about a fall risk

There is provided a fall detection system, comprising a user device comprising one or more sensors for measuring the movements of a user; and the system further comprising a processing unit configured to operate in a first mode in which the processing unit processes the measured movements to determine if the user has fallen and a further mode in which the processing unit processes measured movements obtained during a user-specified time period to determine the risk of the user falling.

Fall detection system and method

There is provided a fall detection system for use in detecting falls by a user, the fall detection system comprising a processing unit configured to determine context information about the user and/or the environment in which the user is located, and to increase the sensitivity of a fall detection algorithm used to detect falls by a user in the event that the determined context information indicates that the user is or may be at an increased risk of falling, the increase in sensitivity occurring while the increased risk of falling is indicated by the determined context information.

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Eindhoven, Netherlands

Janneke Annegarn