Patents by Inventor Michael Dohyun Kim
Michael Dohyun Kim has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20240164728Abstract: A system includes a wearable sensor device including an accelerometer configured to be worn by a person and to record sensor data during an activity performed by the person; an analysis element configured to receive the sensor data from the wearable sensor, determine sensor orientation data of the wearable sensor during the activity based on the sensor data, translate the sensor orientation data of the wearable sensor to person orientation data of the person during the activity, determine, for the person during the activity, (a) lift rate, (b) maximum sagittal flexion, (c) average twist velocity, (d) maximum moment, and (e) maximum lateral velocity, and determine a score representative of an injury risk to the person during the activity based on such data; and a tangible feedback element configured to provide at least one tangible feedback based on the score so as to reduce the injury risk.Type: ApplicationFiled: November 15, 2023Publication date: May 23, 2024Applicant: RS1Worklete LLCInventors: Sean M. Petterson, Michael Dohyun Kim, Alan Vito Argondizza, Michael Patrick Spinelli
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Publication number: 20230236675Abstract: Systems and methods of the present disclosure enable movement recognition and tracking by receiving movement measurements associated with movements of a user. The movement measurements are converted into feature values. An action recognition machine learning model having trained action recognition parameters generates, based on the feature values, an action label representing an action performed during an action-related interval. An activity recognition machine learning model having trained activity recognition parameters generates, based on the action label, an activity label representing an activity performed during an activity-related interval, where the activity includes the action. A task recognition machine learning model having trained task recognition parameters generates, based on the action label and the activity label, a task label representing a task performed during a task-related interval, where the task includes the activity and action.Type: ApplicationFiled: April 1, 2023Publication date: July 27, 2023Applicant: RS1Worklete, LLCInventors: Michael Patrick Spinelli, SivaSankara Reddy Bommireddy, Michael Dohyun Kim, Sean Michael Petterson
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Publication number: 20230225680Abstract: A system includes a wearable sensor device including an accelerometer configured to be worn by a person and to record sensor data during an activity performed by the person; an analysis element configured to receive the sensor data from the wearable sensor, determine sensor orientation data of the wearable sensor during the activity based on the sensor data, translate the sensor orientation data of the wearable sensor to person orientation data of the person during the activity, determine, for the person during the activity, (a) a lift rate, (b) a maximum sagittal flexion, (c) an average twist velocity, (d) a maximum moment, and (e) a maximum lateral velocity, and determine a score representative of an injury risk to the person during the activity based on such data; and a tangible feedback element configured to provide at least one tangible feedback based on the score so as to reduce the injury risk.Type: ApplicationFiled: January 17, 2023Publication date: July 20, 2023Inventors: Sean M. Petterson, Michael Dohyun Kim, Alan Vito Argondizza, Michael Patrick Spinelli
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Patent number: 11630506Abstract: Systems and methods of the present disclosure enable movement recognition and tracking by receiving movement measurements associated with movements of a user. The movement measurements are converted into feature values. An action recognition machine learning model having trained action recognition parameters generates, based on the feature values, an action label representing an action performed during an action-related interval. An activity recognition machine learning model having trained activity recognition parameters generates, based on the action label, an activity label representing an activity performed during an activity-related interval, where the activity includes the action. A task recognition machine learning model having trained task recognition parameters generates, based on the action label and the activity label, a task label representing a task performed during a task-related interval, where the task includes the activity and action.Type: GrantFiled: July 14, 2022Date of Patent: April 18, 2023Assignee: RS1Worklete, LLCInventors: Michael Patrick Spinelli, SivaSankara Reddy Bommireddy, Michael Dohyun Kim, Sean Michael Petterson
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Publication number: 20220350398Abstract: Systems and methods of the present disclosure enable movement recognition and tracking by receiving movement measurements associated with movements of a user. The movement measurements are converted into feature values. An action recognition machine learning model having trained action recognition parameters generates, based on the feature values, an action label representing an action performed during an action-related interval. An activity recognition machine learning model having trained activity recognition parameters generates, based on the action label, an activity label representing an activity performed during an activity-related interval, where the activity includes the action. A task recognition machine learning model having trained task recognition parameters generates, based on the action label and the activity label, a task label representing a task performed during a task-related interval, where the task includes the activity and action.Type: ApplicationFiled: July 14, 2022Publication date: November 3, 2022Inventors: Michael Patrick Spinelli, SivaSankara Reddy Bommireddy, Michael Dohyun Kim, Sean Michael Petterson
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Patent number: 11392195Abstract: Systems and methods of the present disclosure enable automated recognition of user performed activities and tasks using sensor data by receiving raw sensor data while a user performs a series of activities wearing at least one sensor for a predetermined interval of time. The raw sensor data is converted into a set of feature values. An action recognition machine learning model is used to generate action labels indicative of actions performed by the user during the predetermined interval of time based on trained action model parameters and the set of feature values. A task recognition machine learning model is used to generate task labels indicative of tasks performed by the user during the predetermined interval of time based on trained task model parameters, the set of action labels and the set of feature values, and a message is displayed with an indication of the task labels to a user.Type: GrantFiled: June 15, 2021Date of Patent: July 19, 2022Assignee: StrongArm Technologies, Inc.Inventors: Michael Patrick Spinelli, SivaSankara Reddy Bommireddy, Michael Dohyun Kim, Sean Michael Petterson
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Publication number: 20220122009Abstract: Systems and methods of the present disclosure enable determining and preventing risk of human injury. Raw sensor data is received via sensors including one motion-related measurements of motion of the user. An aggregate motion value associated with a motion is determined based on a subset of the raw sensor data. A risk metric is determined based on the aggregate motion value. A dynamic risk metric is determined based on a risk metric history and the risk metric, where the dynamic risk metric is indicative of an injury risk associated with the user. A dynamic annoyance metric is determined based on a previous alert associated with the user, where the dynamic annoyance metric is indicative of an alert cadence and is customized based on user behavior. An alert is generated upon a determination that the dynamic risk metric has reached a predetermined threshold based on the dynamic annoyance metric.Type: ApplicationFiled: October 19, 2021Publication date: April 21, 2022Inventors: Sean Michael Petterson, Michael Dohyun Kim, Michael Patrick Spinelli
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Publication number: 20210389817Abstract: Systems and methods of the present disclosure enable automated recognition of user performed activities and tasks using sensor data by receiving raw sensor data while a user performs a series of activities wearing at least one sensor for a predetermined interval of time. The raw sensor data is converted into a set of feature values. An action recognition machine learning model is used to generate action labels indicative of actions performed by the user during the predetermined interval of time based on trained action model parameters and the set of feature values. A task recognition machine learning model is used to generate task labels indicative of tasks performed by the user during the predetermined interval of time based on trained task model parameters, the set of action labels and the set of feature values, and a message is displayed with an indication of the task labels to a user.Type: ApplicationFiled: June 15, 2021Publication date: December 16, 2021Inventors: Michael Patrick Spinelli, SivaSankara Reddy Bommireddy, Michael Dohyun Kim, Sean Michael Petterson
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Publication number: 20210290176Abstract: A system includes a wearable sensor device including an accelerometer configured to be worn by a person and to record sensor data during an activity performed by the person; an analysis element configured to receive the sensor data from the wearable sensor, determine sensor orientation data of the wearable sensor during the activity based on the sensor data, translate the sensor orientation data of the wearable sensor to person orientation data of the person during the activity, determine, for the person during the activity, (a) a lift rate, (b) a maximum sagittal flexion, (c) an average twist velocity, (d) a maximum moment, and (e) a maximum lateral velocity, and determine a score representative of an injury risk to the person during the activity based on such data; and a tangible feedback element configured to provide at least one tangible feedback based on the score so as to reduce the injury risk.Type: ApplicationFiled: February 8, 2021Publication date: September 23, 2021Applicant: StrongArm Technologies, Inc.Inventors: Sean M. Petterson, Michael Dohyun Kim, Alan Vito Argondizza, Michael Patrick Spinelli
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Patent number: 10912521Abstract: A system includes a wearable sensor device including an accelerometer configured to be worn by a person and to record sensor data during an activity performed by the person; an analysis element configured to receive the sensor data from the wearable sensor, determine sensor orientation data of the wearable sensor during the activity based on the sensor data, translate the sensor orientation data of the wearable sensor to person orientation data of the person during the activity, determine, for the person during the activity, (a) a lift rate, (b) a maximum sagittal flexion, (c) an average twist velocity, (d) a maximum moment, and (e) a maximum lateral velocity, and determine a score representative of an injury risk to the person during the activity based on such data; and a tangible feedback element configured to provide at least one tangible feedback based on the score so as to reduce the injury risk.Type: GrantFiled: November 13, 2018Date of Patent: February 9, 2021Assignee: StrongArm Technologies, Inc.Inventors: Sean M. Petterson, Michael Dohyun Kim, Alan Vito Argondizza, Michael Patrick Spinelli
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Publication number: 20190200936Abstract: A system includes a wearable sensor device including an accelerometer configured to be worn by a person and to record sensor data during an activity performed by the person; an analysis element configured to receive the sensor data from the wearable sensor, determine sensor orientation data of the wearable sensor during the activity based on the sensor data, translate the sensor orientation data of the wearable sensor to person orientation data of the person during the activity, determine, for the person during the activity, (a) a lift rate, (b) a maximum sagittal flexion, (c) an average twist velocity, (d) a maximum moment, and (e) a maximum lateral velocity, and determine a score representative of an injury risk to the person during the activity based on such data; and a tangible feedback element configured to provide at least one tangible feedback based on the score so as to reduce the injury risk.Type: ApplicationFiled: November 13, 2018Publication date: July 4, 2019Applicant: StrongArm Technologies, Inc.Inventors: Sean M. Petterson, Michael Dohyun Kim, Alan Vito Argondizza, Michael Patrick Spinelli
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Patent number: 10123751Abstract: A system includes a wearable sensor configured to be worn by a person and to record sensor data during an activity performed by the person; an analysis element configured to receive the sensor data from the wearable sensor, determine sensor orientation data of the wearable sensor during the activity based on the sensor data, translate the sensor orientation data of the wearable sensor to person orientation data of the person during the activity, determine, for the person during the activity, (a) a lift rate, (b) a maximum sagittal flexion, (c) an average twist velocity, (d) a maximum moment, and (e) a maximum lateral velocity, and determine a score representative of an injury risk to the person during the activity based on such data; and a tangible feedback element configured to provide at least one tangible feedback based on the score so as to reduce the injury risk.Type: GrantFiled: April 13, 2017Date of Patent: November 13, 2018Assignee: StrongArm Technologies, Inc.Inventors: Sean Michael Petterson, Michael Dohyun Kim, Alan Vito Argondizza, Michael Patrick Spinelli
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Publication number: 20170296129Abstract: A system includes a wearable sensor configured to be worn by a person and to record sensor data during an activity performed by the person; an analysis element configured to receive the sensor data from the wearable sensor, determine sensor orientation data of the wearable sensor during the activity based on the sensor data, translate the sensor orientation data of the wearable sensor to person orientation data of the person during the activity, determine, for the person during the activity, (a) a lift rate, (b) a maximum sagittal flexion, (c) an average twist velocity, (d) a maximum moment, and (e) a maximum lateral velocity, and determine a score representative of an injury risk to the person during the activity based on such data; and a tangible feedback element configured to provide at least one tangible feedback based on the score so as to reduce the injury risk.Type: ApplicationFiled: April 13, 2017Publication date: October 19, 2017Inventors: Sean Michael Petterson, Michael Dohyun Kim, Alan Vito Argondizza, Michael Patrick Spinelli