HOT FLASH MULTI-SENSOR CIRCUIT SYSTEM
Embodiments in accordance with the present disclosure are directed to systems, devices, and methods involving hot flash (HF) multi-sensor circuits. An example system includes a plurality of sensor circuits and processor circuitry. The sensor circuits obtain a plurality of sensor signals associated with the user. The processor circuitry extracts features from the plurality of sensor signals obtained by the plurality of sensor circuits, aligns the extracted features to a common time point, identifies a HF event for the user using a predictive data model indicative of probability of the HF event occurring for the user at a date and time and based on the aligned extracted features, and communicates a message indicative of the HF event to the user.
1 . A system comprising:
a plurality of sensor circuits configured to obtain a plurality of sensor signals associated with a user; and
processor circuitry in communication with the plurality of sensor circuits and configured to:
extract features from the plurality of sensor signals obtained by the plurality of sensor circuits;
align the extracted features to a common time point;
identify a hot flash (HF) event for the user using a predictive data model indicative of a probability of the HF event occurring for the user at a date and time and based on the aligned extracted features; and
communicate a message indicative of the HF event to the user.
2 . The system of claim 1 , wherein the plurality of sensor circuits include two or more sensor circuits selected from a photoplethysmogram (PPG) sensor, a skin conductance (SC) sensor, a temperature (T) sensor, and a motion (M) sensor.
3 . The system of claim 1 , wherein the processor circuitry is configured to characterize a level or presence of the HF event based on the extracted features.
4 . The system of claim 1 , wherein the message includes at least one of the identification of the HF event and an intervention action for the HF event, and the processor circuitry is configured to identify the HF event in real-time.
5 . The system of claim 1 , wherein the processor circuitry is configured to:
identify a psychophysiological state of the user based on the extracted features; and
identify, using the predictive data model, a pattern of physiological measurements indicative of the probability of the HF event occurring based on the aligned extracted features and the psychophysiological state of the user.
6 . The system of claim 5 , wherein the psychophysiological state includes a sleep state or an awake state, and the processor circuitry is configured to calculate an amount of awake time associated with the HF event.
7 . The system of claim 1 , wherein the processor circuitry is configured to:
align the extracted features to the common time point based on a plurality of different time windows associated with the plurality of sensor circuits; and
weigh each of the extracted features based on an impact of the extracted features on the probability of the HF event occurring.
8 . The system of claim 1 , wherein the predictive data model includes a plurality of sub-models, and each of the plurality of sub-models are associated with a respective sensor circuit of the plurality and provide an output score indicative of the probability of the HF event occurring for the user based on the extracted features from the respective sensor signal obtained by the respective sensor circuit; and
the processor circuitry is configured to combine the output scores from the plurality of sub-models to identify the HF event.
9 . The system of claim 1 , wherein the processor circuitry is configured to combine the extracted features from the plurality of sensor signals into a vector and input the vector to the predictive data model to produce an output score indicative of the probability.
10 . The system of claim 9 , wherein the processor circuitry is configured to generate a decision tree structure to combine the extracted features, to produce the output score based on the combined extracted features, and to:
identify whether the HF event is occurring or not at a plurality of time points;
detect consecutive identified HF events; and
convert the consecutive identified HF events into a HF region.
11 . The system of claim 1 , wherein the processor circuitry is configured to receive feedback data in response to the communicated message, the feedback data being indicative of at least one of a user confirmation of the HF event, a user denial of the HF event, and a severity of the HF event.
12 . A non-transitory computer-readable storage medium comprising instructions that when executed cause processor circuitry to:
extract features from a plurality of sensor signals associated with a user, the plurality of sensor signals being obtained by a plurality of sensor circuits;
identify a HF event for the user using a predictive data model indicative of a probability of the HF event occurring for the user at a date and time based on the extracted features; and
revise the predictive data model based on feedback data indicative of an impact of the HF event on the user.
13 . The non-transitory computer-readable storage medium of claim 12 , further including instructions executable to align the extracted features to a common time point based on a plurality of different time windows of the plurality of sensor circuits used to obtain the plurality of sensor signals.
14 . The non-transitory computer-readable storage medium of claim 12 , further including instructions executable to receive the feedback data, the feedback data including at least one of:
a user confirmation of the HF event, a user denial of the HF event, a severity of the HF event, and an impact of an intervention action on the HF event.
15 . The non-transitory computer-readable storage medium of claim 14 , wherein each feature of the extracted features is associated with a weight indicative of an impact of the respective feature on the probability of the HF event, and wherein the instructions to revise the predictive data model include instructions executable to perform at least one of:
adjust a first weight associated with a first feature of the extracted features;
adjust the first weight and a second weight associated with the first feature for different psychophysiological states of the user; and
adjust an intervention action for additional HF events.
16 . The non-transitory computer-readable storage medium of claim 12 , further including instructions executable to communicate a message indicative of the HF event to the user, wherein the message indicates least one of an occurrence of the HF event, a prediction of the occurrence of the HF event, and an intervention action for the HF event.
17 . A system comprising:
a plurality of sensor circuits configured to obtain a plurality of sensor signals associated with a user over a plurality of different time windows; and
processor circuitry in communication with the plurality of sensor circuits and configured to:
extract features from the plurality of sensor signals;
align the extracted features to a common time point based on the plurality of different time windows associated with the plurality of sensor circuits;
track a psychophysiological state of the user based on the aligned extracted features;
track a plurality of HF events for the user using a predictive data model indicative of probability of a HF occurring for the user at a date and time based on the aligned extracted features and the tracked psychophysiological state of the user; and
communicate a message indicative of the plurality of HF events to the user.
18 . The system of claim 17 , wherein the tracked psychophysiological state is associated with a sleep state or an awake state of the user, and the processor circuitry is configured to calculate an amount of awake time associated with at least one of the plurality of HF events based on the tracked psychophysiological state.
19 . The system of claim 17 , wherein the processor circuitry is configured to revise the predictive data model based on feedback data indicative of an impact of the plurality of HF events on the user, the revision including adjusted weights for the extracted features as associated with the tracked psychophysiological state.
20 . The system of claim 17 , wherein the predictive data model includes weights for each of the extracted features and for different psychophysiological states, each weight being associated with the probability of the HF occurring at the date and time.