Martin C Moore-Ede

6511424, Jan 28, 2003

Mar 3, 2000

09/519050

Martin C. Moore-Ede - Wellesley MA
Udo E. Trutschel - Brookline MA
Rainer Guttkuhn - Cambridge MA
Anneke M. Heitmann - Arlington MA

Circadian Technologies, Inc. - Lexington MA

A61B 500

600300, 600301, 600544, 340573

A method and apparatus for determining, monitoring and predicting levels of alertness by detecting microsleep episodes includes a plurality of channel processing units and a channel combining unit. Each of the channel processing units receives an information channel which conveys information associated with the mental state of the subject, such as an EEG channel, and classifies the information into a distinct category. Such categories may include microsleep, non-microsleep, one or more of a plurality of stages of sleep, one or more of a plurality of stages of wakefulness, or a transition state characterized by a transition from one of the aforementioned states to another. Each of the channel processing units includes a neural network which has been trained with a set of example input/result vector pairs. The example input/result vector pairs are generated by correlating actual information channel outputs with observed fatigue related events such as nodding off, head snapping, multiple blinks, blank stares, wide eyes, yawning, prolonged eyelid closures, and slow rolling eye movements.

8075484, Dec 13, 2011

Mar 1, 2006

11/365137

Martin Moore-Ede - Wellesley MA, US

A61B 5/16

600300, 128898, 128920

In a system and method for assessing and modifying fatigue, an input device receives current work-rest pattern and/or sleep data from an individual. A data aggregation and processing platform combines the current work-rest pattern and/or sleep data with previous data related to the individual to generate a fatigue assessment result, a diagnostic assessment result, and a corrective intervention result. At least one output display outputs the fatigue assessment result, diagnostic assessment result and corrective intervention result in a user-readable format to a user. The user uses this information to revise the work-rest pattern to reduce or control future fatigue risk.

20120078063, Mar 29, 2012

Nov 4, 2011

13/289046

Martin Moore-Ede - Wellesley MA, US

A61B 5/00

600300

In a system and method for assessing and modifying fatigue, an input device receives current work-rest pattern and/or sleep data from an individual. A data aggregation and processing platform combines the current work-rest pattern and/or sleep data with previous data related to the individual to generate a fatigue assessment result, a diagnostic assessment result, and a corrective intervention result. At least one output display outputs the fatigue assessment result, diagnostic assessment result and corrective intervention result in a user-readable format to a user. The user uses this information to revise the work-rest pattern to reduce or control future fatigue risk.

20140081179, Mar 20, 2014

Sep 19, 2012

13/622720

Martin Christopher Moore-Ede - Wellesley MA, US

A61B 5/11

600595

A system and method for personal fatigue risk management, performed using a smartwatch including a digital processor, associated memory, and an accelerometer for measuring acceleration in X,Y, and Z axes. A signal is received from the accelerometer indicating a movement of the smartwatch, and the value of the signal is stored in association with indicia of the time at which the signal was received in a time-stamped bin. The stored movement values are analyzed using a state algorithm to determine sleep, wake, and off-wrist state values. Fatigue risk values are determined from the sleep and wake state values, using a state algorithm. The probable relative risk of errors and incidents as compared to average risk, for the wearer of the watch, are then calculated. A warning is issued when the fatigue risk data indicates that one of the thresholds has been exceeded.

6070098, May 30, 2000

Apr 10, 1998

9/058649

Martin C. Moore-Ede - Wellesley MA
Udo E. Trutschel - Brookline MA
Rainer Guttkuhn - Cambridge MA
Anneke M. Heitmann - Arlington MA

Circadian Technologies, Inc. - Cambridge MA

A61B 504

600544

A method and apparatus for determining, monitoring and predicting levels of alertness by detecting microsleep episodes includes a plurality of channel processing units and a channel combining unit. Each of the channel processing units receives an information channel which conveys information associated with the mental and behavorial state of the subject, such as for example an EEG channel, and classifies the information into a distinct category. Such categories may include microsleep, non-microsleep, one or more of a plurality of stages of sleep, one or more of a plurality of stages of wakefulness, or a transition state characterized by a transition from one of the aforementioned states to another. Each of the channel processing units includes a neural network which has been trained with a set of example input/result vector pairs. The example input/result vector pairs are generated by correlating actual information channel outputs with observed fatigue related events such as nodding off, head snapping, multiple blinks, blank stares, wide eyes, yawning, partial and complete prolonged eyelid closures, and slow rolling eye movements.