Alertness prediction system and method

• US 10,588,567 B2
• Filed: 03/25/2019
• Issued: 03/17/2020
• Est. Priority Date: 02/18/2016
• Status: Active Grant

• Current Assignee: Curgroup Incorporated
• Original Assignee: CurAegis Technologies, Inc.
• Inventors: Kenyon, Matt, Payne-Rogers, Colin, Jones, Josh
• Primary Examiner(s): Ghulamali, Qutbuddin
• Application Number:
  US16/364,003
• Publication Number:
  US 20190216391A1
• Time in Patent Office: 358 Days
• CPC Class Codes: A61B 2560/0242 adapted to measure environmental factors, e.g. temperature, pollution
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/01 Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue clinical contact thermometers G01K13/20
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• CPC Class Codes: A61B 5/02438 with portable devices, e.g. worn by the patient
• CPC Class Codes: A61B 5/11 Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb for measuring pulse A61B5/02 ;A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/165 Evaluating the state of mind, e.g. depression, anxiety
• CPC Class Codes: A61B 5/18 for vehicle drivers or machine operators
• CPC Class Codes: A61B 5/4809 Sleep detection, i.e. determining whether a subject is asleep or not
• CPC Class Codes: A61B 5/4857 Indicating the phase of biorhythm clocks or watches with indicators for biological cycles G04B19/264
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61B 5/7278 Artificial waveform generation or derivation, e.g. synthesising signals from measured signals A61B5/327 takes precedence
• CPC Class Codes: A61B 5/7282 Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence
• CPC Class Codes: G16H 20/00 ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
• CPC Class Codes: G16H 40/63 for local operation
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/50 : for simulation or modelling of medical disorders

Abstract

An alertness prediction bio-mathematical model for use in devices such as a wearable device that improves upon previous models of predicting fatigue and alertness by gathering data from the individual being monitored to create a more accurate estimation of alertness levels. The bio-mathematical model may be a two-process algorithm which incorporates a sleep-wake homeostasis aspect and a circadian rhythm aspect. The sleep-wake homeostasis aspect of the model is improved by using actigraphy measures in conjunction with distal skin, ambient light and heart rate measures to improve the accuracy of the sleep and wake estimations. The circadian rhythm model aspect improves fatigue prediction and estimation by using distal skin, heart rate and actigraphy data. The sleep-wake homeostasis and circadian rhythm aspects may also be combined with additional objective and subjective measures as well as information from a user to improve the accuracy of the alertness estimation even further.

Claims

1. A device for monitoring and predicting alertness of an individual, the device comprising:

one or more sensors configured to obtain information signals about the individual, the sensors comprising at least one of;

a motion sensor configured to produce movement data or body position data of the individual,

a temperature sensor configured to produce distal skin temperature data of the individual, and

a heart rate monitor configured to produce heart rate data of the individual;

a memory configured to store;

a default circadian rhythm configured to be refined with data derived from the information signals about the individual to generate an estimated circadian rhythm for the individual, and

a bio-mathematical model configured to generate a fatigue score for the individual;

a processor coupled to the one or more sensors and to the memory, configured to;

receive the information signals about the individual,

estimate a circadian rhythm of the individual by incorporating the information signals about the individual to refine the default circadian rhythm,

extract features from the information signals about the individual and the estimated circadian rhythm,

extract at least one coefficient from the extracted features using at least one pattern recognition algorithm or machine learning algorithm,

apply the bio-mathematical model to the at least one extracted coefficient, and

generate the fatigue score for the individual from the at least one extracted coefficient using the bio-mathematical model.