Abstract: 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.

Abstract: According to some embodiments, disclosed are systems and methods for a novel framework of real-time event alert detection and communication. The disclosed framework operates by analyzing live-feeds of captured video at location and determining whether events lend towards a dangerous activity, then automatically alerting the users involved as to potential and/or imminent harm awaiting their actions. Rather than alerting one user, or a manger, as in conventional systems, the disclosed technology may evidence a communication relay among devices at a location, devices of users involved, as well as devices (and devices of users) overseeing operations within which the dangerous activity is anticipated or detected. This may lead to improved safety at and/or around workplace environments, as well as improved operational efficiency, thereby leading to reduced costs, reduced overhead and a reduction in resource expenditure.

Abstract: According to some embodiments, disclosed are systems and methods for a novel framework that performs management of a location and the individuals operating therein based on determined fatigue data of such individuals. The framework may track a person (e.g., a user) at or around a location. Such tracking may be performed based on captured digital imagery of the user via a set of strategically positioned cameras at the location. In some embodiments, as soon as a user begins working, or upon detection by a camera(s), the framework may cause the camera(s) to begin capturing footage of the user, which may be fed, uploaded and/or streamed to a fatigue detection system that determines fatigue data related to the user. Such fatigue data may be leveraged to control which jobs certain users are performing, while reassigning other users based on safety decisions formed from their respective fatigue data.

Abstract: According to some embodiments, disclosed are systems and methods for machine learning-based image detection and the determination of slippery conditions based therefrom. The disclosed systems and method identify a set of images that depict captured imagery in relation to at least one area of a floor at a location. These images are then analyzed via at least one slippery condition detection machine learning algorithm, which results in a determination of a classification of the area of the floor (e.g., does a puddle exist or other type of slippery condition). This information is stored and later used for training of the at least one slippery condition detection machine learning algorithm. Moreover, the information is communicated to beacons in/around the location, to alert users to the condition.

Abstract: 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.

Abstract: According to some embodiments, disclosed are systems and methods for machine learning-based image detection and the determination of slippery conditions based therefrom. The disclosed systems and method identify a set of images that depict captured imagery in relation to at least one area of a floor at a location. These images are then analyzed via at least one slippery condition detection machine learning algorithm, which results in a determination of a classification of the area of the floor (e.g., does a puddle exist or other type of slippery condition). This information is stored and later used for training of the at least one slippery condition detection machine learning algorithm. Moreover, the information is communicated to beacons in/around the location, to alert users to the condition.

Abstract: According to some embodiments, disclosed are systems and methods for dynamic optimized activity-assignment processing based on a periodic assessment of user fatigue risk via wearable devices. The disclosed technology receives tracking data related to movements and activities of a plurality of users working at a location and determines a user-specific injury prone score for each user. Based on this, the technology analyzes a current activity assignment schedule, which is modified to correspond to the current capabilities of each user at the location. As a result, a notification is generated and communicated to each user so as to alert them to their new assignment.

Abstract: A system includes a wearable device including an accelerometer configured to record accelerometer data during an activity; a modeling device programmed to determine device acceleration data of the wearable device during the activity based on the accelerometer data, the device acceleration data including x-axis, y-axis, and z-axis acceleration data of the device, translate the device acceleration data to wearer acceleration data of a wearer during the activity, wherein the wearer acceleration data includes at least x-axis and y-axis wearer acceleration data, wherein the y-axis acceleration data of the wearer indicates acceleration along a sagittal axis of the wearer, identify a lift, and utilize a trained lift classification machine learning model to classify the lift as high-risk or low-risk based on a ratio of the x-axis wearer acceleration data to the wearer y-axis acceleration data; and a feedback element configured to provide tangible feedback based on identification of a high-risk lift.

Abstract: 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.

Abstract: Systems and methods of the present disclosure enable injury prediction using one or more processors for receiving a time-varying signal of sensor measurements from a sensor device associated with a user. The processor(s) generate time windows of the time-varying signal, including a series of the sensor measurements across a predetermined time period, and generate motion features based at least in part on the series of the sensor measurements of the time windows. The processor(s) utilize an injury risk classification machine learning model to predict an injury risk during each time window based at least in part on the motion features. An injury alert message is generated based at least in part on the injury risk being predicted; and transmitting the injury alert message to at least one user computing device.

Abstract: 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.

Abstract: Systems and methods of the present disclosure enable injury prediction using one or more processors for receiving a time-varying signal of sensor measurements from a sensor device associated with a user. The processor(s) generate time windows of the time-varying signal, including a series of the sensor measurements across a predetermined time period, and generate motion features based at least in part on the series of the sensor measurements of the time windows. The processor(s) utilize an injury risk classification machine learning model to predict an injury risk during each time window based at least in part on the motion features. An injury alert message is generated based at least in part on the injury risk being predicted; and transmitting the injury alert message to at least one user computing device.

Abstract: Systems and methods of the present disclosure enable injury prediction using one or more processors for receiving a time-varying signal of sensor measurements from a sensor device associated with a user. The processor(s) generate time windows of the time-varying signal, including a series of the sensor measurements across a predetermined time period, and generate motion features based at least in part on the series of the sensor measurements of the time windows. The processor(s) utilize an injury risk classification machine learning model to predict an injury risk during each time window based at least in part on the motion features. An injury alert message is generated based at least in part on the injury risk being predicted; and transmitting the injury alert message to at least one user computing device.

Abstract: 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.