Personal safety device and method
There is described a method for identifying potential personal safety incidents of an individual ( 5 ), the method comprising the steps of receiving a sample data set comprising motion data and vital sign data from one or more sensors ( 14, 16, 18 ) associated with the individual, determining one or more motion features and one or more vital sign features, and determining whether the sample data set is indicative of a potential personal safety incident of the individual based on a model and at least one of the motion and vital sign features. The sensors can be provided by a fitness tracker and/or an activity tracker, such as a smartphone and/or a smart watch. The method can also include determining one or more responses such as alerting another party of the potential personal safety incident or triggering a deterrent mechanism.
1 . A method for identifying potential personal safety incidents comprising an attack on an individual, the method comprising the steps of:
receiving a sample data set comprising:
motion data from one or more motion sensors associated with the individual; and
vital sign data from one or more vital sign sensors associated with the individual, the vital sign data comprising heart rate data;
determining a plurality of sample features based on the motion data and the vital sign data, the plurality of sample features comprising one or more motion features and one or more vital sign features, wherein determining the one or more vital sign features comprises determining, based on the heart rate data, a measure of heart rate variability;
determining whether the sample data set is indicative of a potential personal safety incident comprising an attack on the individual based on a model and on the one or more motion features and the one or more vital sign features comprising the measure of heart rate variability, the model configured to relate features derivable from motion data and vital sign data to potential personal safety incidents comprising an attack; and
outputting the determination of whether the sample data set is indicative of a potential personal safety incident comprising an attack on the individual.
2 . The method according to claim 1 , wherein the motion data is received from at least a first motion sensor and a second motion sensor associated with the individual;
wherein determining the one or more motion features comprises determining a first motion feature from the motion data from the first motion sensor and determining a second motion feature from the motion data from the second motion sensor; and
wherein determining whether the sample data set is indicative of a potential personal safety incident is based on both the first motion feature and the second motion feature.
3 . The method according to claim 1 , wherein determining the plurality of sample features comprises the step of filtering at least a portion of the sample data set based on frequency.
4 . The method according to claim 3 , wherein determining the plurality of sample features comprises the step of filtering the motion data based on a plurality of frequency bands.
5 . The method according to claim 1 , wherein determining the plurality of sample features comprises the step of transforming the motion data into a frequency domain to produce first transformed motion data.
6 . The method according to claim 1 , wherein determining the plurality of sample features comprises the step of transforming the motion data into a time-frequency domain to produce second transformed motion data.
7 . The method according to claim 4 , wherein determining one or more motion features comprises the step of determining, for each of one or more of the frequency bands, one or more sample motion features based on the filtered motion data within that frequency band.
8 . The method according to claim 1 , wherein the measure of heart rate variability comprises a ratio of a low-frequency heart rate variability to a high-frequency heart rate variability, wherein the low-frequency heart rate variability corresponds to heart rate signal frequencies below a threshold frequency and the high-frequency heart rate variability corresponds to heart rate signal frequencies above the threshold frequency.
9 . The method according to claim 1 , wherein the motion data comprises acceleration data and/or gyroscope data.
10 . The method according to claim 1 , further comprising the step of determining one or more responses based on the determination of whether the sample data set is indicative of a potential personal safety incident.
11 . The method according to claim 1 , further comprising the step of assigning one or more probabilities associated with the determination of whether the sample data set is indicative of a potential personal safety incident based on the model and the plurality of sample features.
12 . The method according to claim 1 , wherein the model is a recurrent neural network configured to receive as input the plurality of sample features and provide as output a determination of whether the sample data set is indicative of a potential personal safety incident.
13 . The method according to claim 1 , wherein the potential personal safety incident comprises at least one of:
the individual experiencing fear;
the individual experiencing surprise; and
the individual struggling; and/or
wherein the model is configured to:
determine a level of stress of the individual indicated by the motion data;
determine a level of stress of the individual indicated by the vital sign data; and
determine whether the sample data set is indicative of a potential personal safety incident of the individual based on a difference between the level of stress indicated by the motion data and by the vital sign data.
14 . The method according to claim 10 , further comprising the steps of:
sending to the individual a personal safety verification request; and
monitoring for a user input via a user interface, the user input being in response to the request;
wherein determining the one or more responses is further based on the outcome of monitoring for the user input.
15 . The method according to claim 10 , wherein the one or more determined responses comprise triggering an alarm, wherein the alarm is triggered on an alarm device configured, in response to the alarm being triggered, to emit a high-volume sound, between around 100 dB and around 300 dB or between around 120 dB and around 200 dB.
16 . The method according to claim 1 , further comprising the step of:
determining a sampling frequency associated with the sample data set;
wherein the plurality of sample features is determined if the sampling frequency is above a sampling frequency threshold.
17 . A method of training a model for relating one or more potential personal safety incidents comprising an attack on an individual to features derivable from a data set comprising motion data and vital sign data, the method comprising:
receiving a plurality of training data sets, each training set comprising motion data from one or more motion sensors associated with an individual over a training time period and vital sign data from one or more vital sign sensors associated with the individual over the training time period, the vital sign data comprising heart rate data;
receiving for each training data set a label indicative of whether conditions corresponding to a personal safety incident comprising an attack on the individual occurred during the training time period;
determining for each training data set a plurality of training features based on the motion data and the vital sign data, the plurality of training features comprising one or more motion features and one or more vital sign features, wherein determining the one or more vital sign features comprises determining, based on the heart rate data, a measure of heart rate variability;
determining a measure of dependence between the labels and the one or more motion features and the one or more vital sign features comprising the measure of heart rate variability; and
creating a model based on the determined measure of dependence, wherein the model is configured to relate a potential personal safety incident comprising an attack on an individual to features derivable from a data set comprising motion data and vital sign data.
18 . A system for identifying potential personal safety incidents comprising an attack on an individual, the system comprising:
one or more motion sensors and one or more vital sign sensors;
a mobile device comprising a processor, a communications interface configured to receive motion data from one or more motion sensors and vital sign data from one or more vital sign sensors, and a memory, wherein the vital sign data comprises heart rate data; and
a remote server comprising a processor, a communications interface, and a memory;
wherein the mobile device is configured to transmit the motion data and vital sign data to the remote server; and
wherein the memory of the remote server comprises instructions which, when executed by the processor of the remote server, cause the remote server to perform the method of claim 1 .
19 . The method according to claim 1 , wherein the measure of heart rate variability is indicative of whether the individual is experiencing stress.
20 . The method according to claim 1 , wherein determining the one or more motion features comprises determining, based on the motion data, one or more motion features indicative of whether motion of the individual is erratic.