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 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 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 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 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.

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: Medical lifting devices and methods are disclosed. A lifting support device includes a garment configured to be worn by a user and at least one sensory feedback element. The sensory feedback element is coupled to the garment and is configured to provide sensory feedback to the user. The sensory feedback encourages the user to adopt an appropriate posture during a lifting operation. A lifting vest includes a load transfer element, a posture compliance element, a coupling device, and at least one sensory feedback element. The load transfer element is configured to transfer a weight of a load to a point over shoulders of a user and down to a lower torso of the user. The posture compliance element is configured to passively or actively enforce an appropriate back posture. The coupling device is configured to connect the load-transfer element to the postural compliance element.

Abstract: 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.

Abstract: 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.

Abstract: Medical lifting devices and methods are disclosed. A lifting support device includes a garment configured to be worn by a user and at least one sensory feedback element. The sensory feedback element is coupled to the garment and is configured to provide sensory feedback to the user. The sensory feedback encourages the user to adopt an appropriate posture during a lifting operation. A lifting vest includes a load transfer element, a posture compliance element, a coupling device, and at least one sensory feedback element. The load transfer element is configured to transfer a weight of a load to a point over shoulders of a user and down to a lower torso of the user. The posture compliance element is configured to passively or actively enforce an appropriate back posture. The coupling device is configured to connect the load-transfer element to the postural compliance element.