Abstract: 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.

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: 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.