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 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: Embodiments of the present disclosure relate to an exoskeleton support system for posture feedback and support during body trunk motions. The system comprises a biomimetic semi-rigid support system connected to body attachment straps for coupling the system onto a user. A feedback pad is positioned on the user's back at a location corresponding to a middle thoracic region of the user. This arrangement causes the user to experience pressure feedback on the back when torso flexion and twisting occur. In addition, progressive resistance on the shoulders is provided via shoulder straps containing stitching in a curved manner to allow for longitudinal flexion and unrestricted arm motion during system use. Further, the system provides indications of safe movements by way of little to no feedback during neutral torso positions. Accordingly, the system only delivers feedback pressure in response to an amount of the user's own body motion.

Abstract: Medical lifting devices and methods are disclosed. A medical lifting device includes a brace and at least one strap. The brace is adapted to be positioned against the back of a patient. The brace has a pair of opposed lateral edges. The at least one strap extends from each lateral edge of the brace. The at least one strap has a length sufficient to enable a user of the device to grasp each strap while the user is standing with the user's torso in an upright position and the patient is in a seated position. A method of lifting a patient with the brace includes positioning the brace against the back of a patient, grasping each strap while standing in an upright position and while the patient is in a seated position, and pulling on each strap to lift the patient to a standing position.

Abstract: Medical lifting devices and methods are disclosed. A medical lifting device includes a brace and at least one strap. The brace is adapted to be positioned against the back of a patient. The brace has a pair of opposed lateral edges. The at least one strap extends from each lateral edge of the brace. The at least one strap has a length sufficient to enable a user of the device to grasp each strap while the user is standing with the user's torso in an upright position and the patient is in a seated position. A method of lifting a patient with the brace includes positioning the brace against the back of a patient, grasping each strap while standing in an upright position and while the patient is in a seated position, and pulling on each strap to lift the patient to a standing position.

Abstract: The present invention preferably relates to a self-activated postural compliance lift-assistance device that puts the wearer in an increasingly supported lifting posture, thereby providing a lift-assistance device that conforms with best ergonomic practices for lifting.